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Points of ¥iew or opinions stated irl this dOClHIHHlt are 

those of tne author!sj and do not represent the official 

position IH IJolicies of th~ U.S. DeplHtment of Justice. 

U.S. DEPARTMENT Of JUSTiCE 

LAW ENfORCEMENT ASS!ST M~CE ADMIHISTRA nON 

HATIONAl CRIMINAL JUSTICE REFERENCE SERVICE 

WASHINGTON, D.C. 20531 

Date filmed 

6/4/76 

QUANTITATIVE TOOLS 

FOR 

CRIMINAL JUSTICE 

PlANN~NG 

LEONARD OBERLANDER, Etlitor 

UNITED STATES DEPARTMENT OF JUSTiCE 

LAVtJ ENFORCEMENT ASSISTANCE ADMINISTR/HION 

Washington, D.C. 

1975 

.Q ........ ____________________ &_. _______ 2_. _______________________________ ~ __ 

If you have issues viewing or accessing this file contact us at NCJRS.gov.
RICHARD W. VELDE, Administrator 

LAW ENFORCEMENT ASSISTANCE ADMINISTRATION 

FOREWORD' 

A basic premise underlying the programs administered by the Law Enforcement Assistance Administration is that good planning is indispensable to the 

development and implementation of effective programs for improving criminal 

justice and reducing crime. The argument for this is that the chances for a 

rattqnal allocation of scarce resources are greatly increased if a system- atic planning process has preceded the allocation decision. 

The premise is easy to state, but good criminal justice planning is extraordinarily difficult to put into effect at the State and local levels. 

The reasons why this is so are well known to those who have been involved in 

criminal justice planning. They involve factors over which not only planners have no control, but over which State and local governments also have little 

control. These include the basic structure of government in America.. The 

Federa1 system$ the separation of powers, and the reserved powers of States 

often appear to planners as barriers to effective planning. While I would 

agree that these kinds of factors pose special challenges to the ingenuity of 

planners, it seems to me that the largec:.+, barrier to effective p1anning is 

the absence of adequate and reliable data and the absence of the capabi1ity 

to analyze those data. 

The most powedul tool at the planner's disposal is the data: he h,as collected, organized, displayed, and analyzed in such a way that the relevance 

of the data to the problem at hand is clearly apparent. It is unfortunately 

the case that many planners are unable to realize ~be full potential of the 

data which exists because they have not developed the skills they need to 

organize and analyze the data and show its relevance to the critical problems 

of the moment. This is not to say that there is a fa"ilure to recognize the 

importance of planning. State Planning Agencies, Regional Planning Units, 

and local planners across the'nation are engaged in that process. They have 

begun to reach some agreement on the elements of that process. They know 

that they must begin with an analysis of the crime and criminal justice prob- lems they face and that they must thereafter make clear: (1) what reSOUi~ces 

and capabilities currently exist to meet those problems, (2) what capabili- ties are currently 1acking, (3) what goals they will attempt to accomplish, 

(4) what standards they will attempt to establish and achieve, (5) what priorities they will set, (6) what long range programs they will pursue, and 

(7) what specHi c programs they expect to pursue in the comi ng year. Pl al"lners have done a steadily better job, with help from LEAA, with the latter 

stages of that process. In some states and in some communities, they have 

had success with the analysis of crime and criminal justice problems. 

It is the case, however, that the earliest steps in the planning process are where the greatest difficulty has been and still is. Data have been 

hard to come by and expensive to obtain. The consequence is that the data 

bases have been f~'agmentary, incomplete, and sornetirnes highly unreliable. 

Planners and others have concentrated on the basic task of finding the data, 

and then of establishing the systems, also with LEAA help, for collecting and organizing those data. Analysis of the data has often had to wait, and 

has often not occurred except in relatively superficial ways. 

iii 
We have now reached a stage in the development of criminal justice 

planning, however, when it is reasonable for us to take stock of where we 

are, and of what we have learned, and to make available to others what we 

have learned about analysis. This book begins that process. It places 

heavy emphasis on ways to organize, utilize, and analyze data and information about crime and criminal justice. It sets forth much of what we know 

now about the techniques of analysis and is careful to specify their 

limitations. . 

Some of the authors in the book draw·:~onclusions about the ut'ility of a 

particular approach or the lack of utility':of another, which all of us do 

not share. The book is not an attempt to s·et forth all the anSW2rs to all 

the questions planners have about the ways'to analyze crime and criminal 

justice data. It is rather an attempt to make available to all who have a 

concern with criminal justice planning what many who are experienced and 

many who are expert and many who are both have learned about the methods 

avanable for analysis. LEAA does not endoy~se every word in this book, but 

does believe that it will be a useful compendium of quantitative tools from 

wh'ich planners and others may select those~hich they find helpful to them. 

We have called on the expertise of researchers, analysts, planners, 

statisticians, and of greatest importance, people who have had direct personal experience with State and local planning processes. They have done an 

outstanding job of telling us in clear and largely non-technical language 

what they knO\v and how to use what they know in other settings. 

This book will not finish the task LEAA has set itself of helping planners to do a better job, LEAA expects to offer training in analysis, in 

planning, and in evaluation. It expects to make its guidelines more supportive of good planning for the future. It expects to offer technical assistance in planning, assistance which will be tailored to meet individual needs 

wherever possible. This book is a beginning, and I commend it to its read- ers~ with that understanding of its purpose. 

~~~~W 

RICHARD W. VELDE, 

ADMINISTRATOR 

<.'t" 

iv 

PREFACE ' 

The literature ,on crime statistics is expanding rapidly, and interest 

in the use of statistics and quantitative models for analysis of crime and 

the criminal justice system is on the rise. A rapid growth in technology is 

occurring in the criminal justice profession, spurred on by the development 

and adoption of quantitative methods for analysis in research, operations, 

planning, and management. OutStanding scientists, law enforcement specialists, practitioners in courts and corrections systems, as well as emerqing 

academicians, are all contributing to it. A major reason for the rapid advances in use of quantitative methods and information systems, along, with 

the expansion of criminal justice statistics, has been the leadership a;)d 

support for these developments by the law Enforcement Assistance Administration with the support and appropriations provided by the Congress of the 

United States. 

We are still a long way from any attempt at a definitive work on quantitative tools for application to criminal justice problems and their solutions. However, it is intended that this book will serve as a step in that 

direction. Since the nineteen-thirties in the United States, an increasing 

number of disciplines have been concerned with the problem of crime in the 

nation. Concepts from such fields as urban plann'ing t organization theory, 

policy sciences, management science, quantitative geography, and public administration have been applied to the emerging criminal justice technology. 

This book is addressed to criminal justice planners, practitioners, and 

students in applied or professional settings with an interest in the use of 

criminal justice data and quantitative methods for describing, analyzing, 

and communicating a broad array of issues concerning crime and the criminal 

justice system. It is intended that it be of particular interest and use 

to statisticians, operations researchers, systems analysts, and those with 

a special role in planning and evaluation at state and local levels of 

government. 

The best information from expert sources has been compiled and assembled in the time available. The book should serve some of the functions for 

which textbooks arE. .intended in more tradit'ional fields. A compendium of 

materials on quantitative tools in the criminal justice field will not be as 

esoteric or advanced as a text in the field of economics. Future editions 

will build upon the first edition by benefiting from readers' comments which 

will identify the errors and omissions allowed in this volume. The value of 

this book derives not so much from what can be communicated from these pages 

to its readers, as from what its readers can contribute to furthering a systematic body of knowledge on practical quantitative tools for the criminal 

justice profession. 

The Law Enforcement Assistance Administration is grateful to the contributors mentioned elsewhere in this book for their generous)efforts and 

cooperation in meeting a demanding publication schedule, as well as to those 

who have given their time to review and comment on the (h-aft manuscr·;pt. 

The book was written at the suggestion of many ct'iminal justice planners and practitioners, arid wa.s accompl ished under the overall direction of 

v 
James M. H. Gregg, Assistant Administrator, Office of Planning and Management, and Blair G. Ewing, Director, Division of Planning and Evaluation 

Standards, Office of Planning and Management. As the editor of the book, I 

maintain responsibility for any faults resulting from my efforts to edit 

the chapters into a unified volume with a common format and transitional 

flow for facilitation of reading, comprehension, and practical useability. 

. Au g us t 1 5) 1975 

Leonard Oberlander 

Office of Planning and Ma'1agement 

Lnw Enforcement Assistan(;e Administration 

Washington, D.C. 

",1.,. 

't' ' 

',' " 

vi 

ALFRED BLUMSTEIN 

SIDNEY BROUNSTEIN 

RONALD E. CRELLIN 

ilOYCE DEROY 

~DAVID L.DOUGHERTY ~t~'j;.::.:' ,::'_'; 

BLAIR G. EWING 

GEORGE L. FARNSWORTH 

FEDERAL BUREAU OF 

INVESTIGATION 

BRIAN FORST 

THOMAS GIACINTI 

CONTRIBUTORS 

Carnegie-Mellon University, School of Urban and Public Affairs, Urban Systems Institute, Pittsburgh, 

Pennsylvania 15213. Chapter 13. A Model in Planning for the Total Criminal Justice System. i ; 

Institute for Law and Social Research, 1125 15th 

Street, N.W., Suite 625, Washington, D.C. 20005. 

Chapter W. Analysis of the Criminal Justice System 

with the Prosecutors Management Information System 

( PROMISt· 

Program for Data Research, Social Science Research 

Institute, University of Southern California, los 

Angeles, California 90007. Chapter 14~ta Use 

Tools and Techniques Available to Criminal Justice 

Professionals. 

Institute for law and Social Research, '1125 15th 

Street~ N.W., Suite 625, Washington, D.C. 20005. 

Chapter 12. Data Reliability .and Data Purif·ication. 

Office of Criminal Justice Plans and Analysis, District of Columbia Government, Washington, D.C. 

20000. Chapter 5~ UCR in Crime Specific Research 

and Planning; footnote to C~apter 13. A Model To 

Aid in Planning for the Total Criminal Justice 

System. 

Law Enforcement Assistance Administration, Washington, D.C. 20531. Chapter 1. Quantat1ve Tools for 

Criminal Justice Planning. 

Program for Data Research, Social Science Research 

Institute, University of Southern California, Los 

Angeles, California 90007. 01'.1.Q!er 14. Data Use 

Tools and Techniques Availabre'cto CrimiJ]al Justice 

Professionals. 

U.S. Department of Justice, Washington, D.C. 20535. 

Chapter 3. Uniform Crime Reporting: Uses for Criminal Justice Planning. 

Institute for Lavi and Social Research, 1125 15th Street~-N.W., Washington, D.C. 20005. Chapter 11. 

Statistical Techniques and their Limitations in the 

Analysis of Criminal Justice Data. 

1 

Denver Anti-Crime Council, 1313 Tremont Place~ 

Suite 5, Denver, Colorado 80204. Chapter 6. Predicting Crime Incidence and Determining Change. 

vii 
WILLIAM HAMILTON 

SUSAN KATZENElSON 

FRANK LOCKFELD 

BURT NANUS 

LEONARD OBERLANDER 

R. ALLEN PAYNE 

LUTHER PERRY 

JOHN R. PFEIFFER 

LEO SCHUER~IjAN 

WESLEY G. SKOGAN 

CHARLES D. WELLER 

Institute for Law and Social Research, 1125 15th 

Street, N.W., Wash1ngton~ D.C. 20005. Chapter 10. 

Anab:..sis of the Criminal Justice System with the 

prosecutors Management Information System (PROMIS). 

Institute for law and Social Research, 1125 15th 

Street, N.W., Washington, D.C. 20005. Chapter 8. 

An2.l.lsis of Crime with Offender Based Transaction 

Statistics (OBTS); Chapte~" 9. Analysis of the Crimina;-Justice System with Offender Based Transaction 

Statistics (OBTS~ 

" 

County of Santa Cl ara) Off'j ce of the County Executive, Center for Urban Analysis, 19 County Administration Building, 70 West Hedding Street, San Jose, 

California 9S'110. C~aQtet 16. Tools for FOl~ecasting" 

Center for Futures Research, Graduate School of 

Business Administration, University of Southern California, Los Angeles, California 90007. Chapter 7. 

A P1anning-OrJ_ented r\1easur~ of Crime and Delingu-enc1.. 

Law Enforcement Assistance Administration, Washington, D.C. 20531. Chapter 1" ~ntative Tool:; for 

Criminal Justice Planninq . ..::..:.-:.:..:.:..:...:...:.:::...'--=-.:.:.::...;::...:..::c-"'--'_-'--'-'-'-'--'-' ..... 

Law Enforcement Assistance Administration, Washington, D.C. 20531. fhapter 18. Crime-Oriented Planning anq Program Developmen~. 

Center for Futures Research, Graduate School of 

Business Il\dministration, University of Southern California, Los Angeles, California 90007. Chapter 7. 

A Planning-Orient~.o M~}lSUre of Crime and Delinquency. 

Director, Statistical Analysis Center, Massachusetts 

Comnittee on Criminal Justice, Boston, Massachusetts 

02100. Chapter 4._ Crime Statistics Analysis in 

Massachusetts. 

Program for Data Research~ Social Science Resp.arch Institute, Univei'sity of Southern California; Los Angeles~ California 90007. Chapter 14. Data Use 

Tools and Technigues Available to Criminal Justice 

Professionals; Chapter 15. Small Area Population 

Estimation. 

Assistant Professor of· Political Science and Urban 

Affai rs, Northwestern University, Evanston t 111 i noi s 

60201. Chapter 2. Th~ Use of Victimization Surv~y~ 

in Criminal Justice Planning. 

Executive Director, Denver Anti-Crime Council ~ 1313 

Tremont Place, Suite 5, Denver, Colorado 80204. 

Chapter 17. Crime Analysis: Organization of a Team 

To Impact the Local Criminai Justice System. 

viii 

..... i1M 

, 

.4 

CONTENTS 

Foreword 

Preface 

ContY"i bu tors 

PART I. INTRODUCTION 

Chapter 1. Quantitative Tools for Criminal Justice Planning Leonat'd Oberlander and B}air G. Ewillil. , ... 

PART II. CRIME DATA AND TECHNIQUES FOR ANALYSIS 

2. 

3. 

The Use of Victimization Surveys in Cr-:;-;,ina,l Justice Planning ~~sley G. Skogan • , • !, • 

Uniform Crime Reporting: Uses for Criminal 

Justice Planning 

federal Bureau of_lnves~igat;o~ ..... 

4. Crime Statistics Analysis in Massachusetts 

John R. Pfeifer .. . .. . . . . . . , . . . ~ . .. 

5. UCR in Crime Specific Research ind Planning 

David L. Dougher.t.Y. .......... . 

6. Predicting Crime Incidence and Determining Change 

Thomas A. Giacinti ........ , .... . 

7. 

8. 

A Planning-Oriented Measure of Crime and 

Delinquency 

Burt Nanus and Luth~r Perry-. . . . . . 

Analysis of Crime with Offender Based Transaction 

Statistics (OBTS) ~usan Katzenelson .. 

PART III. THE CRIMIN,Il.L JUSTICE SYSTEM: DP,TA, MODELS, AND TECHNIQUES FOR ANALYSIS 

9. 

10. 

"11 .. 

Analysis of the Criminal Justice System with 

Offender Based Transaction Statistics (OBTS) ___ Susan Katzenelson ,r_ _ ........... . • 

Analysis of the Criminal Justice System with the 

Prosecutors Management Information System) (PROMIS) 

.?idney Braunstein and William Hamilton 

Statistical Techniques and their Limitations in 

the Analysis of Criminal Justice Data 

Brian Forst. ......... ' ••• " (I ••• 

ix 

Page 

iii 

v 

vii 

13 

25 

39 

47 

49 

65 

71 

83 

91 

113 
12. Data Reliability and Data Purification 

Joyce Der01.. . . . . . . . . . . . . • V 'I .. .. ., .. 

13. A Model To Aid in Planning for the Total 

Criminal Justice System Alfred Blums~ .......... . • II ........ 

PART IV. USE OF POPULATION DATA 

14. Data Use Tools and Techniques Available to 

Criminal Justice Professionals 

Ronald E. Crel1io t Geor9§ L. Farnsworth, and 

Leo Schuernlan . . . . . . . . . . . . . . . . 

15. Sma11 Area Population Estimation 

Leo Schuerman . . . . . . . . . .. .. .. . .. .. .. .. .. .. 

16. Tools for Forecasting Frank lQckfield .. .. .. .. .. .. .. .. .. .. . .. .. ........ 

PART V. DEVELOPING AN ANALYTIC UNIT AT THE LOCAL LEVEL 

17. Crime Analysis: Organization of a Team To 

Inpact the Local Criminal Justice System 

Charles D. Well~· .......... . 

PART VI. CRIME-ORIENTED PLANNING AND PROGRAM DEVELOPMENT 

........... 

page 

123 

129 

165 

197 

213 

225 

18. Crime-Oriented Planning and Program Development 

R. A 11 en Payne . . . . . . . . . . . . 235 

241 

INDEX 

x 

& 

" 

Part I. 

INTRODUCTI'ON 
, 

CHAPTER 1 , 

QUANTITATIVE TOOLS FOR CRIMINAL JUSTICE PLANNING 

Leonard Oberlander and Blair G. Ewing 

INTRODUCTION 

Criminal justice planners need tools that permit decisions to be made 

on a basis which is more sophisticated than those founded solely on experience, judgement, and intuition. Without quantitative informat1on t defining 

the nature and extent of crime and making decisions about the application of resources to the problem must almost entirely be based on intuition. 

There is no underestimating the value of qu~ntitative data to those who opperate agencies and make decisions. 

QUANTITATIVE TOOLS: A DEFINITION 

Quantitative tools are the devices used to construct and to analyze specific groupings of information in terms of quantity or numbers. The 

tools that criminal justice practitioners have at their disposal include 

quantitative data about crime~ the criminal justice system, behavior, and 

the environment, as well as a wide selection of methods for organizing and 

analyzing these da.ta. These tools help provide a quantitative base of knowledge upon which criminal justice practitioners can bring to bear their own 

expertise for planning, -c!valuation, program development, and policy analysis. 

A partial list of the kinds of quantitative tools that might be utilized includes: 

Data on Crime 

1. Victimization survey data 

2. Uni form Crime Reports 

3. Modus Operandi files 

4. Offender-Based Transaction Statistics 

5. Other local crime data 

Data on the Criminal Justice System 

6. Offender-Based Transaction Statistics 

7. Management and administrative data 

Data on Behavior 

8. School truanc.y data 

9. Mental health data 

10. Social services data 

11. Alcoholism and drug abuse data 

12. Economic trends, including unemployment and those 

persons outside the labor force 

i 

I.' 

r: 
Data on Environment 

13. 

14. 

15. 

Census data on population and populat~on charact~rist~cs 

Environmental data concerning commerclal and resldentlal 

areas, types of buildin~ structures. and land topology 

Dual Independent Maps Encoding (DIME) files, and other geographic based files. 

Methodological Tools 

16. Techniques for statistical analysis 

17. Justice System Interactive Model (JUSSIM) ) 

18. Prosecutor's Management Information System (PROMIS 

'19. Geocod 1 ng 

Although there ~re other data and methodologic~l t?ols ava~lable upon which 

the criminal justice practitioner may draw, th1S llSt prov1des a general 

perspective of the wide vari~ty of tools.that do e~ist and are ~ccessable 

to those charged with analyt1c and plannlng tasks 1n state, ~eg~on~l ~ and 

local units of government and other institutions concerned w1th'cr1m1nal 

justice. 

THE DATA BASE FOR CRIMINAL JUSTICE PLANNING 

Rationale for the Data Base 

. Planning for the management of the criminal justice system requi~es ~ 

base of knowledge from which informed decisions can be made. The ava11ab1lity of data on how many reported offenses there are and how many ar~ests 

have been made and on the handling and disposition of arrests perm1ts ana'lysis to be mr;de of the way in which the criminal jus~ice system as.a whole 

performs; how it deals with its workload; where the maJor areas of d1scretion are; where the bottlenecks exist; and which agencies appear to be effective or ineffective in handling cases, persons, and special problems. 

Analysis of data pertaining to the relationship between crim~ and its 

social and environmental context leads to an increased understand1ng of the 

kinds of settings ~n which crime is more or 1ess likely to occur. That u~­

derstanding is a sO\Jr~:e of hypotheses about what kinds of programs or proJects might best be tested under certain conditions and in certain a~eas. 

This kind of analysis is best done by relating specific types of cnme~ to 

demographic and physical environmental variables, and usually emp~oys lnformation such as land-use maps~ master plans, and census data. Furthermore~ 

such analysis can increase understanding of the ecology of crime; the mobllity of offenders; the circumstances cf victimization; and the role.of age, 

race sex and the socioeconomic status of offenders. Although th1s type 

of a~alysis does not lead to definitive statements ab?ut the caus~s of crime, 

it does provide the basis for an objective understand1ng of the C1rcumstances 

and characteristics of criminal events and offenders. 

Operational planning focuses primarily on the allocation of resources 

and manpower to deal with specific workloads, including workload concentrations during peak hours or at high activity locations. This ~ind of plan- ni ng rests on the avail abil ity of data about when and where d, fferent types 

2 

: 

t$ 

of crime occur. Such data is routinely collected by most police departments in conjunction with their regular reporting of criminal events;:':;·,Jhe 

data that is collected and analyzed, in some cases in very sophisticated, 

automated or computerized systems, can also be used to develop justification for increases, decreases, or shifts in manpower resources. Analysis 

of data can also be of considerable value for forecasting, including forecasting for operational purposes and overall forecasting for crime trends, 

population shifts, and other events such as economic trends and unemployment rates that may relate to crime rate forecasts. 

Kinds of Collections in the Data Base 

Much of the data for criminal justice planning is available as a product of ongoing standardized data collections. This data consists of uniformly cq:llected statistics that concern specified populations fbr one 

specific'po;;nt in time or for certain time periods. The nature of standardized data aHows comparisons to be made in a systematic way between and 

within populations, based on comparable data. The ongoing nature of standardized data collections a110ws comparisons and trends to be computed for 

sequential time periods. 

Other data and i.!1formation that may be acquired from numerous sources a~e non~standardized d~tathat have been collected for singular purposes. 

These data co1'1ections usually concern an interest in a specific~'.~'gment of 

a population, a specifically defined subject area of research, and usually 

are unique to one definedtifu$ period. It is difficult to make comparisons 

of statistics in this kind of~~ata collection with statistics for other populations or points in time because of the lack of a s'tandard of comparabil-

'ity. This data is useful, hO\</ever, for planning pynposes because it provides ins'ights into problems of crime and the criminal justice system when 

systematically aggregated with other data to highlight areas of consistancy 

and differences resulting from the examination of statistics for various 

populations and time periods. 

How the Data Can Be Acquired 

Data sources for the criminal justice planners' data base are varied. 

Some data collections are readily available from Federal~ state, and local 

agencies or from private and public organizations such as information 

clearing houses, university data centers, professional association libraries, and research centers. 

The following are sources from which the pla~ner can most efficiently 

acquire some of the data referred to in this book. 

1. Victimization Survey Data: This data,. collected for the Law Enforcement Assistance Administration (lEAA) by the U.S. Bureau of the Census, is available in the form of published tabular data in a series of reports. 

These reports are available from the Superintendent of Documents, U.S. Government Printing Office, Washington, D.C. 20402. Information concerning 

the nature ·and contents of these and related reports can be acquired by communicating with the National Criminal Justice Reference Service, Law Enforcement Assistance Administration, Washington s D.C. 20531. In addition, 

3 
--- ---------------

( i ng vi ctirni zat-! on data) with accompany; ng pUblic-use,computde~ t~pest;~~~~e~~te"-ials ~\lill become available in early documentatlon an lns ruc " + V' ginia 22209 can be 

1976. DUALABS~ 1601 N?rth Kentd~tre~~~ :~q~~~i~~~n ~~d use of-these tapes. contacted for 1 nformatl on regar 1 ng . .L" ' 

See Chapter 2 for a discussion of Vlc~lmlzat,on data. 

2 Uniform Crime Reports: Chapter 3 of this boOk'f"~~!:~~~ ~~~~~ Reporting Uses for Criminal Ju~t~CE:" bydt~~i~~~~~~ld~~~e~~n~rated by ihe Unidiscusses the means for acqulrlng an 

form Crime Reporting Program. 

3 Modus Operandi (MO) Data: (detailed in Chapter 7

l

0f this ~gOk) suggest that MO files can be an important reso~rce ~~d~h~ Pt~~~~~d officials 

files are prepared accor~~ng to afs~~~~~~dMhO~~~~sg WhichYcontain no names, 

of police departments,"d ent-~Sf~e~s of any kind ar~ in corrrnon use in many addresses, or persona 1 e 1 - , 

police agencies. 

4. Offender-Based Tra~sactio~ Statistics 

it is available is usually located 1n the state 

(OBTS): This data! where 

Statistical Ana1ysls Center, 

See Chapters 8 and 

a module of the LE,1\A Comprehensive Data Systems program. 

9 for discussions about OBTS, 

I Ad . , trative Data' ~uch data includes rnanpo\'ver, 5, r~anagem~nt anc ml n1 s --' ~, f . f t' n The 

cost,.and program or activity'.data amongd~~~e~r~1~~~s~w~~c~r~:r~~t·analy­ most lmpottant e~en:e~ts o~ thIS ~ype t~f meet needs and achieve objectives and 

sis of the capabllltles °t a~~~C~~~gram description data, with manpower and 

goals. The manpower, cos ',e 'bl are most useful in this regard. 

costs allocated by pro~ramd fl~ POSS't e~ the kinds of local agencie~ or de- These data can be obtalne ~om mos 0 

partments 1 istr:d below. 

Departments of finance 

Youth authorities 

Departments of corrections 

Personnel agencies 

Judicial agencies " ' 

Police officers' tralnlng unlts 

Public employers I retirement systems 

Emergency services departments 

Comptrollers of budg~t.off~ces 

Departments of rehabl11tatlon 

" t 1 1 1 eve 15 of government from whi ch 

There ~re many agenclesfldl~e thetsed: ta ~~~ be used. The data may already 

a conslcierable amount 0 lscre e u , d 

be published, or available in easily accesslble recor s. 

, d't e and personnel data directly It may be necessary to obtaIn expen 1 ur . 11 com iled roufrom loca.l units by questionnaires; but such data 1!n~S~~nt~cts ~ith local 

tin~lY,in.the course of ~hed'nortma~hb~d~~tc~~o~:S~~POlied without difficulty. jurlsdlctlons can often ln 1ca e a , 

4 

------------------------,-------

THE UTILITY OF THE DATA ON CRIME 

When the data base has been accumulated, the planner can then execute 

his plan for analysis and utilization of the results to identify the nature 

and extent of the crime problem, and to examine and compare such components 

of the criminal justice system as workload productivity and workload objectives. The following discussion summarizes some of the uses of the data 

base for the criminal justice planner. 

Victimization Data 

Some of the uses of victimization survey data are discussed by Skogan in liThe Use of Victimization Surveys in Crimina') Justice Planning. II Briefly, 

victimization surveys can be used to estimate crime rates for a jurisdiction. They can be util'ized to examine local patterns of reporting and 'non-reporting, 

which can help planners to understand differences between increases in the 

crime rate and increases in the reporting rate. Victim survey data can also 

contribute to the study of the details of events and to isolate deterrable 

crimes. This data can also be analyzed to identify high-risk subgroups in 

the population. By obtaining IIfeedback ll about citizens' views of the opet--

ation of the criminal justice system, the data can be used to measure citizen 

satisfaction with the performance of the criminal justice system. Victimization surveys can also be used to establish benchmarks for evaluating the 

effectiveness of criminal justice programs. The data can also be used as an 

aid to establ ish a pool of crime victims who can be I'tracked ll through the 

criminal justice system. 

Uniform Crime Reporting (UCR) Data 

Uniform Crime Reporting data are a valuable tool to the planner'. The 

Federal Bureau of Investigation, in IIUniform Crime I~eporting Uses for Criminal Justice Planning " (Chapter 3), characterizes the UCR Program, the sets 

of UCR data that can be utilized by criminal just-ice practit'joners, and the 

uses of UCR data. UCR data can be used to obtain crimer'ates and trends 

for the many sets of UCR data, UCR data can also be u~ed in conjunction with 

other sets of data, such as school dropout rates; unemployment rates; economic 

levels; and gross population characteristics relative to age, sex, and race to 

examinate rates and trends that correlate arithmetically with the crime data. 

Some of the uses of UCR data in conjunction with other data relating to 

sociological phenomena include analysis of offenses known. By concurrently 

viewing similarly constructed data from other data sources with this data 

set, significant patterns may be discerned. Clearance information is to 

some degree a measure of law enforcement efficiency. Careful analysis of 

prosecution and court information, overlayed with clearance data, can provide useful information about workload for future planning. Data 011 'youthful offenders can be utilized by correlating it with population composition, 

detention facilities, population configuration, parole, probat~on, and welfare caseload composition, and other data sets to which youth or juveni1ity 

are intrinsic. Moreover, by utilizing the youthful offender data in timeseries, certain observations can be Inade concerning the time lapse between 

the encounter of the youthful offenr··er by 1 aw enforcement and the processing of the individual at later stages of the criminal justice system. 

5 

i 

" 

,., 

~ ~ 
Additional observations or projections may be made concerning workloads, de.-

tention facilities, and alternative programs. 

By utilizing arrest information, the planner can analyze fluctuations 

of law enforcement activity. Such data c~n be of aid to assessing the effectiveness of programs initiated not only within the law enforcement module but 

also within the other modules of the system. Disposition information can be 

helpful to the planner who desires to test offense information, aligning it 

with clearance data and, in th~ case of Crime Index offenses, to observe in 

summary number form the actions of the joint effort of courts and prosecution. In addition,. law enforcement agency profiles can be constructed by 

utilizing UCR data and law enforcement employee data. These profiles can 

serve admi ni strat"ive needs. 

Pfeiffer, in "Crime Statistics Analysis in Massachusetts " (Chapter 4), 

discusses how the criminal justice planning a,gency in ~'assachusetts has employed UCR data to analyze crime patterns and trends for use in planning and 

program development. He describes an approach used by the Massachusetts 

State Planning Agency to deveiop the structure and content of a crime report. 

Doughtery} in IIUCR in Cr-ime Specific Research and Planning" (Chapter 5), 

discusses how UCR has been helpful in examining homicide, an area in which 

he has relied heavily on this data for analysis. 

Modus Operandi (MOl Files 

Modus Operandi data may be helpful as a starting point for generating a 

seriousness index of crime that can be used to evaluate existing policy, monitor activities, and help guide planning decisions in such areas as manpower authorizations and budget allocations. Nanus and Perry, in itA Planning- Oriented Measure of Crime and Delinquency'l (Chapter 7), discus~ the uses of i~O ffles, 

Offender-Based Transaction Statistics 

The gathering of the required data for measure~ent essentia11y involves 

identifying and collecting data elements as set forth in the Offender-Based 

Transaction Statistics System (OBTS) module of the lEAA Comprehensive Data Systems Program (Chapter 8 by Kat::enelson). By using the concept of the Comprehensive Data System, one could examine detailed recidivism indicators separately by crime category, by demographic characteristics, and by type of institution. 

There are many alternative sources of data for criminal justice planning and crime analysis. Each is best suited to given types of applications; each 

has advantages. All provide the quantitative data that is necessary to supplement subjective experience and individual judgement. Which type of data 

to use, and when to use combinations of data sets supplemented by additional 

information as well, depend on the purpose of the analysis to be accomplished. 

THE UTILITY OF DATA ON THE CRI~JNAL JUSTICE SYSTEM 

There are analytic tools that can be used in a variety of ways to develop 

a better understanding of justice system operations and to improve its 

6 

management. Some of these dat~ tool d ' 

in Part III of this booK. In ~ddit,sa~ t-echnlqueS are discussed ']i') detail s~ch as 08TS, PROMIS, and JUSSIM. a'3"t °bapproaches that are discussed, 

tlVe statistics (including personnel :n~ . ase of mal'1a~emel1t a~d adr~rfnistra­ sary for an adequate analysis of cr"I' ,e~pen~llture lnfonnatlOn) 15 necesInlna! JUstlce system activities. 

08TS 

/:.;' , OBTS can be appl i ed to assess th k . . :.'tlVes of the criminal justice system e 1~r load, productlvlty, and objec .. system 01' to compare systems Quest: can be used to examine a justice Wh~t i;; the relative frequen~y of 9U~~~~ ~~a\OB~S can ~elp <ilnswer include: 

trlalsi What is the direction of h . ea~, Jury.trlals~ and bench 

to disposition? What is the quant~t:~~~eln ~ost serlOUS charges from arrest 

through each component of the syst? na ure of the flow of offenders em, \ 

The Prosecutors Management Intonnation System 

. The Prosecutors Management Informati S ( cutor's office to accumul ate information ~~ ,ystem PROmS) permits a prose- and analyses based on'such data so th t each case and to receive reports ority areas and control their own w ~l p~osecutors can concentrate 01'1 pri- provlde,data for planning and resea~~h oa s. PROMIS can also be used to 

and Hamllton, IIAnalysis of the criminalP~rpo~es. Cha~te~ 10~ by Braunstein 

agement Information System, II desci'ibes h us~~~~ System wlth Prosecutors Manods to be employed, and the reports than o~a b IdS ca~ be us~d, the data meth,-

. n e eve!oped wlth PROMIS. 

The Justice System Interactive Mode'l 

. The Justice System Interactive Mod 1 (J )", . nln~ for the total criminal just'ice s'ys~em U~~IMt 1S a model to aid in plan- scnbes the planning pr'ocess in each of 't' ap er ~3, by Blumstein, dethe JUSSIM model as a tool for anal ' 1 s step~ as l~ re~ates to uses of 

the many uses of JUSSIM are detaile~~lS and future proJectlons. In addition, 

Management and Administrative Data 

Th~ data base for the criminal Oust' 1 .. and admlnistrative data.*S h d t J lce p anner.sh?Uld lnclude management 

expen?itures, workload, pefs~~nelaa ~et~hmay contaln '~formation on agency 

expedlte analysis relating these ;a~~oro ter~~e~~u~ces In a format that will 

and other criminal justice system data 5 a Qval ale demographic, fiscal, 

data base to include administrative dat ~netOf the purposes for expanding a 

type of user than the enforcement a en ~ lS 0 meet the needs of a different 

users of the majority of criminal J'ugst~lesdwtho are thought to be the primary lce a a present1y. 

*We are grateful to the California B 

reports Whl'ch 'd d .. ur,eau. of.Crimin.al Statistl'cS for l'tS . , prOVl e a valuable input t t~ admlmstrative data. 0 rrlS dlScusslon of management and 

7 

ME - a= 
One group of users who seem particularly im~ortant ar~ the local ad~'in"· istrative officers, department heads, and superv1sors who prepare.t~e crlminal justice agency budgets. The i~sight t~is small group of offlC1~ls may 

secure from having certain data ava1lable tnat helps relate.changes ln cost, 

workload, and personnel in a meaningful way could have cons1derable effect 

on the local criminal justice system. 

There is more involved in making criminal justice.policy ~han jus~ budgeting expenditures. Yet, the decisions made and the lnformat1~n cons1dered 

in the course of developing a department budget may do more to lnfluence 

local action on criminal justice agencies than any other local governmental 

proc2ss. 

Small groups of officials who are maki~g budget dec~sion~ ~re o~ten. \'/orking with very little organized informat1on about their cr1m:na~ Justlce 

agencies of the type discussed in this chapt~t. The cos~ of bu:ldlng and 

maintaining large, centralized data inform~tl0n.systems 1S conslderable. 

Therefore, statistical output of an analyt1~ un~t should,be planne~ to serve 

not only the traditional needs of the contnbutlng agenc1es, but f1scal, administrative. and elective officials as well. 

The uti1ity of management and administrative data may be thought of in 

the context of a number of considerations. These include~ata ~h~t w~uld be 

useful within the context of the local budgeting process; ldent1~lcat1~n o~ the sources of data needed outside of the analytic unit, and the1r ava1labllity; an assessment of the kind of assistance that could be needed and received from local units in preparing the teports; sugg~s~e~ arrang~ment of 

report formats; suggested organ;z.ation of research actlv1tles of theanalytic unit staff; and schedulinp of the reports. 

As important as comparative workload and staf~i~g information .. ar~ to the 

budget process, it ;s very difficult for local admln1strators and their. 

staffs to catry out the amount of research necessary to condu~t compa~atlVe studies for a significant number of departments and keep the 'nf~rm~t1~n ~ur­ rent. In approaching specific problems wit~in their indi~idual JUf1Sdlctlons, 

local aciministt'ators have developed many uniform and fConslstent standards for 

measuring change and rate~ of growth in.a ~ep~rt~entls basic functions. M:ny 

of the standards are appllcable to all Junsd1ctlons or can be ex~reme~y u..,efu1 in designing uniform standards. The ~'/ork ~ha~ ~as been ~one In thlS area 

for criminal ~ustice agencies on a scattered, lnd1vldual bas1s can greatly 

simplify the task involved in an analytic unit developing a data base of administrative and management data. 

One approach is to organize selected data elemen~s ~nt~ a.number of 

tables. The data elements will differ for different Jurlsdlctl0ns and agencies, and the sample procedure described below is simply intended for a general overview. 

The data elements selected may be organized into four tables: 

• Selected Demographic, Economic, and Criminal Justice 

Characteristics of Geographic.Areas 

• Annual Expenditure and Workload Data 

8 

, 

/1 

I 

Ii 

• Aut.horized Personnel by Functional ,C'lassification Ii 

• Expenditure, Personnel and Workload Ratios I 

A brief discu~sion for each data element should explain the purpose of 

the ~ata element, lts source, and some of the problems encountered in collectlng the data. 

The main purpose for the data in each table is that it can help users 

qualify the data in each of the other three tables. Some of the differences that emerge from that comparison with the other tables can be better 

understood, when some~hing is known about the geography, population, and 

wealth of an area. Dlfferences in the amount of taxable wealth for example a~e ofte~ the ct'itical factor in explaining why one area may appear to pro- ' 

vlde a hlghe~ level of law enforcement than another; when by geography or 

amount of cnme, they look similar. ' 

For every data element used in the tables, time series information can 

be shown, along with the ahsolute change and percent change. In time, and 

as the data base builds, the report formats could be easily expanded to show 

changes over longer time periods. 

. A ltho~gh many o'f '~he ~e~vi ce~ a~d func~ion~"performed by crimi na 1 jus- tlC~ agencles hav~ slmlla~ltles, 1t lS posslbl~ to develop and maintain a rellab~e ba~e of lnformat1on about these agenc'ies that could add an important dlmenSlO~ of reporting and analytical capability to a planner's data 

base. Some.h~nts t~at might be beneficial in ~eveloping and using a data 

base of admlnlstratlVe and management-or'iented'data fo11ow. 

" . • It.may be us~ful to put primary emphasis on developing analytical 1nter~retatl~ns of the data pre~ente~ rather .than expending effort to present :nc~ea~ln~ amounts of deta1led lnformatlon .... ,:Budgeting '!measures'! in 

many JUrl Sdl ctl ons usually tend, to be very genera'l'.rather than specifi c. 

. • Data is usually most effectively presented for i0dividual jurisdic- tlOns on a department-by-department or agency-by-agency basis, although if 

the da~a can be presented also by program category, it would be even more 

effectlVe. 

• When reports ar~ updated, it is usually best to Jvoid changes in 

the ~eport f~rmats, keeplng the data elements constant, and maintaining conslstency 1n the analysis. 

• Reports should be timed to the starting date of the jurisdictional budget cycle. 

• In publications about crime and criminal justice, data should be 

treated without implication that workloads or personnel ratios are being suggested as recommended standards. Standards are a matter for policymakers to determine, perhaps based in part on local data. 

9 
THE UTILITY OF DATA ON THE POPULATION, 

GEOGRAPHY, AND BEHAVIOR 

The linking of crime data to data routinely gathered by many other pub- lic agencies can enable an analysis of crime statistics with socioeconomic, 

demographic, behavioral, and land-use information. Analysis of this kind 

can help decision makers assign weights of importance to different factors. 

An analysis of crime might indicate several key factors present, such as 

age, sex, and race of victims and oHenders; population density; unemployment; 

locations and types of premises; school truancy; and drug abuse. The diagno- sis of crime in one area will pOint to some combination of these and other 

factors. Analyzing crime data with other relevant data sets can provide information on which factors are important to a particular area. This information is important for the formulation of objectives and goals, and to program development. The planner then can utilize manpower, cost~ and program 

or activity data to assess the capabilities of agencies to deal with defined problems, and to make recommendations for allocations of resources to 

address specific needs. 

Part IV of this book discusses data on population and tools for analysis. 

Population data provides an invaluable frame of reference for examining crime 

data and crime patterns, and for 'j,dentifying probable causes and the factors 

linked with these causes. 

InChaptsr 14, Crellin, Farnsworth, and Schuerman emphasize the importance of geographic data in the understanding and analysis of crime. They 

point out the valu~ of census data for crime analysis in geographic and geo- ~olitical areas of varying size. The Dual Independent Maps Encoding File 

(DIME) represents a technology that uses computers to create coded geo- graphic maps. Computer programs that sort, structure, link, and display map 

feature data from a variety of sources are described. A list of these computer programs and their sources is provided, along with a list of reports 

on the uses of small area geographic files. The authors include a helpful 

glossary of geographic terms and a list of Standard Metropolitan Statistical 

Areas (SMSA's) with ~eographic Based DIME Files . . ' . " 

1. ': ~ '," j , 

In Chapter 15, Schuerman discusses'small,.ar~a,,~Opl!19ti:i9n ,esti,mation. 

The chapt~r was written for cl p:rson w~ th a backgr.Ol.i.Il~ .. 1n:e,~ay'ah<;:e~ .. s!at ~ stica 1 techm ques, but for thos'E~ Wl thout 1 t, the chapter rerna 1 TlS a'~g~od on entation to tools and techniques. The Bouge-Duncan CompositilMethoau~~Yu~ed 

for creating separate estimates for subgroups and for summing these to arrive at totals. The Ratio Currelation Method uses multiple regression analysis for estimating purposes. A variety of other techniques is included, 

with cautions about their use. 

Computer mapping, geccoding, and forecasting with geocoded data are described in Chapter 16 by Lockfeld. The uses of time series analysis and 

spectral analysis are described for looking at recurring events with characteristics that vary in a pattern, e.g., weekly and seasonal variations in 

crime frequencies. 

10 

-

Part u. 

CRIME: DATA AND 

TECHNIQUES FOR ANALYSIS 
CHAPTER 2 , 

THE USE OF VICTIMIZATION SURVEYS IN CRIMINAL JUSTICE PLANNING 

Wesl ey G. Skogan 

INTRODUCTION 

Victimization surveys ate it new research tool for criminal justice 

planners. In a victim survey, interviews are conducted with samples of . the population of a jurisdiction. These interviews probe the respondents' experiences with crime, their reaction to these incidents, and their 

evaluations of the performance of criminal justice agencies. If the samples 

are carefully drawn, these reports can be extrapolated into population 

estimates of the frequency of crime and the perceived responses of community 

institutions to the needs of the populace. These estimates may then be 

used to assist planners in allocating resources to various criminal justice 

acti viti es. 

TIle first large-scale surveys of the population designed to measure the 

incidence of crime were conducted by the Crime Commission in the mid··1960 's. 

A national slwvey conducted by Phil ip Ennis and a study of high-ct'ime areas 

by Albert Reiss estab'!ished that victimizations were much more common than 

police statistics indicated and that the burden of crime was unevenly distributed in the population. Since the Corrmissiol1 s;it,H'veys~ a number of 

studies of victimization have been conducted (some of which are listed in 

the Bibliography following this chapte~·). In 1972, the Bureau of tht~ 

Census began a national crime investigation for the Law Enforcement 

Assistance Administration. This continuing survey wi11 produce a new set 

of social indicators monitoring quarterly fluctuations in the volume and 

nature of crime in America. 

LOCAL USES OF VICTIMIZATION SURVEY DATA 

At the local level, there are at least seven ways in which population 

sut'veys can be employed to assist criminal justice planners: 

1. Victim surveys can be used to estimate crime rate;:; for a 

jurisdiction. This is extremely important, for police statistics usually underrepresent the total volume of crime in an area. Citizens do not 

report many crimes to the pol ice, and not all reporteoi crimes are accurate1y reflected in official reports. Victimization surveys can be used to 

generate crime data which are independent of criminal justice agencies and 

which circumvent many of the non-reporting pr'oblems which plague official 

totals. These rates may be calculated in several ways. In addition to 

the usual A'crimes per 100,000," surveys enable us to examine household 

burglaries per 100,000 households, bicycle thefts per 100,000 bicycles~ or 

payroll thefts per 100,000 payrolls. Survey data thus can be used to g~nerate more useful estimates of the "risk" of various kinds qf crimes. 

Slnce these data can be used to estimate the number of targets for 

specific crimes as well as the number of victims. 

13 

N 
, 1 cal patterns of non-reporting. 2. Surveys can be used to examl~~iC~' nationally, about 40 percent Manv crimes are not reported to the Pail )"0 erty offenses are not 

of all personal crime~ andf6~hPerc~~~r~iies. Pon~ role of victim surveys brought to the attentl0n 0 e au s so that the resources of 

is to provide inf~rmation about thes~ ~~~~~~:d in response to the "true" 

the criminal justl~e system can ~e dl\rscrease our understanding of V!h.¥ 

distribution of crlme. Another 1sdt~o devise programs to increase cltlzen 

so many ~ffen~es go unre~ortedon~n crimes which are reported can be deterred 

cooperatlon wlth the pollce. Y. itizen reporting plays an 

by official act~on, ~o effortslto ~~cr~~~e ~urveys can be used to estimate 

important role 1n crlm~ contro: lnaes ~ on official statistics. The 

the impact of changes l~ re~ortlng rat maP be on reporting habits, and 

first impact of new ant1-crlme prog~ams 'tYwill paradoxically appear that 

if citizens begin to re~ort more cr~mes lA careful study of patterns of 

the victimization rate !s sk~rocketlng. lanners"to tell the difference 

reporting and nonr~portlng ~11l entabledPincreases in the reporting rate. between increases 1n the crlme ra e an ,-

t d th details of events and isolate 

3. Surveys may ~e ~sed to S u Yn b~ used to elicit many details 

deterr'ab1e crimes. VlI.:tlm sur~eys .c~. These details can be significant 

about criminal even~s from the1f v~~el::i1mates of the impact of crime 

in severa: wa~s .. Flr~t, thethpr?~ltims of 'crime are heavy consumers of 

on commum ty 1 nstl tU~lOns. e I1C Moreover the poor are often 

the services of hOSP'it~l e~~rgen~~e~~~:~'for assist~nce in overcoming forced to turn to .loca we are. theft or robbery. Information 

the burdens of cr'me~ such a\~l~P~~~~el1 but surveys enable planners to 

on these costs of cr'tmel arteh °tr~e cost of crime to the community. estimate more accura eye 

. . . t' s may reveal information 

In addition, intervi~w~ w~th cr~me ~~~hl:nl assist the police in 

about the structure of cr1mlna even s we most interpersonal violence 

allocating scarce t·~sources. For exa~~~S'iS robably undeterrable by 

between friends, nelghbors~ andlrela~ndoors, ~n private space protected the pol i ce. It tJs~a lly ~a es pace nt and it is spawned by from the unwanted. m~rusllon,of the governme t ~ub'ect to rational , 

bitterne~s and conflH~ts WhlCh o~~~ ~~~u~~le crisis-intervention functlons, 

calculatlon. The poll~e may p~r d ne otiating a peace, but they 

separating the cont~nd~~g ~a~~l~~t:~SivegneighbOrhoOd patrolling. The 

\'dll not deter the ~~cle~ t street crime (robbery, purse-snatching, 

po 1 i ce can do somet!lHlg a ou rna deter many auto thefts, and most assaults by strangers), and they ~ b intensive random 

corrunercial rob~et'~es, and househ~~d u~~~g~~r~:!su~e the "deterrabi 1 ity" patro~li,ng, V"lct1m su:vey~ can"'eristics and resources may then be 

of cnmes ~ased on the~t,e ctharap~~babi1 it/ of their effecti vensss. allocated 1n response 0 e ' 

~4 

.M 

4. Victim studies can be used to identify high-risk subgroups in the 

population. Crime is not randomly distributed in the population; some 

groups of persons or commercial establishments are much more likely than 

others to be victimized and to be victimized o~ repeated occasions. Other 

characteristics which affect the relationship between various groups and 

the criminal justice system are: trust in the police, confidence that their 

complaints will be taken seriously, and insurance claims which need to be 

documented. Surveys can be employed to isolate the attributes of high-risk subgroups, investigate the nature of their victimization experiences, and 

identify characteristic difficulties in their relationship with criminal 

justice agencies. the allocation of resources in the direction of highrisk groups may be highly cost effective. A very large number of potential offenders may be severely inconvenienced by programs aimed at hardening 

c( few such high-risk targets as convenience grocery stores. Survey data 

can reveal the hours of operation of such establishments, counter-measures 

which they have taken against victimization, and the characteristics of 

high-risk operations that lead to their apparent vulnerability. While most 

businessmen can be counted on to cooperate with the police,the same cannot 

be said for many high-risk individuals. For example, young males under 

25 years of age suffer over one-half of all assaults 1 but they report only about one-third of them to the police, and attitude studies conducted by 

the Bureau of the Census reveal that they are usually dissatisfied with 

police performance. Knowing who the targets of crime are is only part of 

the story; victim surveys would enable planners to estimate the problems involved in supplying them with various kinds of services as well. 

5. Surveys may be used to ~easure citizen satisfaction with the 

performance of the criminal justice system, and elicit suggestions of 

needed reforms. Victim surveys not only count criminal incidents; they are 

also a vehicle for contacting large numbers of citizens in a jurisdiction and obtaining "feedback" about the operation of the criminal justice 

system. This feedback may come in several forms. Citizens may be asked 

to report their experiences with criminal justice agencies: How fast 

did the police come when called? How long did they wait to have their 

traffic case heard in court? Were they notified of the outcome of a 

criminal case in which they were involved as a witness or a victim? 

This kind of information can be valuable in evaluating the effectiv~~ess 

of criminal justice service-delivery systems. Several communities, in 

fact, have inaugurated regular programs for interviewing crime vic~ims 

in order to monitor system effectiveness. Each month, samples of those 

who call for police service are questioned ,about the quality of their 

experiences (see the articles in the Bibliography by Furstenberg and Bor~ua). More general evaluations of services can also be obtained through interviews. Survey respondents can be asked to rate the courteousness 

of the police, the orderliness of courtrooms, the flow of traffic, and 

their safety on the street and in thei!' homes (see the article by Skogan in 

Crime and Criminal Justice Policy). People can also be asked to &"ank order 

the priority which local government should give to various criminal jus.tice 

problems, or to volunteer specific suggestions about what is wrong (and 

15 
r'ight) about the community and what could be done about it. Unl i ke 

many policy-making areas, most criminal justice decision-making is 

relatively removed from day-to-day politics and most decisions have very 

low visibility. Surveys would enable ordinary citizens to make inputs into the planning process, and might engender considerable symbolic satisfaction among respondents as well. 

6. Victimization surveys will establish benchmarks for evaluating 

the effectiveness of criminal justice pr.ograms. One of the major uses 

of victimization surveys is program evaluation. First, they may be conduct~d independently of the implementation of a program. The measurement process need not be tied to the success or failure of a project or 

an organization, and the findings may be more trustworthy as a result. 

Also, many reforms are designed to affect what is being measured--crime--

and our ability to measure it--crime reporting and recording. Victim 

surveys are more useful than official statistics in evaluating the 

effectiveness of those programs expected to increase the rate at which 

citizens report crimes or to increase the effectiveness of the police 

in recording and analyzing the data. Surveys also enable us to measure the 

non-crime-related consequences of new programs. Many of the benefits of 

effective crime control are attitudinal and perceptual: the presence of good police on the street in large numbers.may serve to enhance people's 

feeling of security, increase their use of community facilities, and . decrease their desire to move out of town. The symbolic consequences of 

official activity are an important component of governance, and the 

effects of criminal justice programs on people's use of the streets and 

public spaces in the community, their perceptions of danger, and their confid~nce in government~ are crucial factors in the evaluation of those 

programs. The research designs which are devised to evaluate policies 

must sati sfy several techni ca 1 and scientific standa,rds, and thi s component 

of an evaluation program should be considered by experts (see, for 

example, the discussion by Campbell and Stanley). The willingness to do 

evaluations and to structure citizen input into the evaluation design is a more fundamental planning issue. 

7. Victim surveys will isolate a pool of crime victims who can be ~'tracked" through the criminal justice system. Data of this sort would 

provide the base for an extremely sophisticated analysis of the operation 

of the criminal justice system and an evaluation of the effectiveness of 

its components. The key point is that a criminal justice system is a 

system--it is a series of agencies and actors who must work in coordinated 

fashion 'if they are to be effective. Perhaps the primary failing of the 

.criminal justice system in America is that it is not such a system. 

Interviews with victims (and witnesses) provide one mechanism for 

evaluating the operation of the system as well as its constituent parts. The design would involve the periodic reinterviewing of participants who were uncovered during a community-wide survey. At each step, 

citizens' perceptions of what was happening to them and their evaluations 

of that experience could be recorded. The data would also enable 

planners to independently gauge the rate at which individual problems move 

through the system, to identify where blockages deve'lop, and to describe 

what kinds of cases appear to get "lost. II New data on everything from 

16 

• 

r~sponse t~lile to the number of continuances which forced victims d 

wltnesses lnto court reappearances could be gathered. Because'Ofa~" survey base, ~he ~ata \"o~l~ be based upon the experiences of the lt~ 

~~~~ur~~~d~~t~u~~~~ep~f~~~)~Yin~~~t~~~~Sa(t~~l~~C~~S)~l~~~t~~~a~:sunits (th~ J:~lS)~ V1rtual~y nowhere is officially collected data suit~ble for ~yv:teUma.1tnghev orl

. et~en slmply describing) the criminal justice system nua , C 1m survey makes an "end I! d h ~ , .~ 

con~train~s upon the data at the same ti~~n tlt:~o~~ 1~n~sseth,nst1ttutlOnal sat1sfact1on. ' em 0 consumer 

WHAT TO LOOK FOR: VICTIMS AND THEIR CRIMES 

.. Viftimization surv~ys can.be used to measure the incidence of many crlm~na events. Some lnfract10ns of the criminal law are not suited 

~~o~~~~~~gm~~~~~e~~~t~o~ow~v~rt.and many things about which people want 

crimes,to look forI! ~s al~~or:l~~~~ ~~ ;~~pn~g: T~~m~r~~~~~ ~; ~~~~! are~qulte rare, and lt would be difficult for a survey to uncover enou h 

of ",hem t~ analyze; other kinds of crime ate quite common but the' 9 ~argets ate not easy to define for samplin. I ,.'., 1r . lnv~lves manY,compromises between our desi~e tone~~~~;~~ethV1~t1~/urVeYHlg of everyiO cnme and our ability to do so. e 1nC1 el1ce 

behav~j~~t Vi~~i~1 su~v.eys focus upon a relatively small subset of crim'inal , s, t 1e ~op ~f a scale showing s~riousness of crimes homicide ~~ ~su~liY e~clu~ed~ ilI~lde from the obvious fact that it leaves'no victim Off·e.l~ ervl~we., It 1S also quite rare and already well-reflected in 1c~a ,statlst1cS. At the bottom of the scale, surveys usuall exclude 

~~~~t~~~~~~~tshas obscen~ tel~phon~ calls and vandalism; this re!soning is 

th " owever, Slnce certa1n subgroups in the population (women ime elderly) 1n fact often feel quite threatened by such incidents. It'is in~o~t~nt that the su~vey focus upon crimes ",{hich have individual victims-- , erVlews are not sUlted to measuring the incidence of drunken drivin " ~l~orderly conduct, vagrancy, or any of the Hcrimes without victims II g, 

i~J~~~d~~f~ asO;~esek~i~timfize ~if anyone) the collectivity, not ;P~Cific . er 1n s 0 cnme, such as carrying stolen property or ~rs~n, a~e usually uncovered only through police investigations and .cr~mes n?vm to the police" define the universe of known event~ Finan ~~ ~s ~~sslble to use perso~al interviews to measure the inciden~e of Vic~' lmlza 10ns only when the vlctims are aware of their status Doubtless ~~~yt ~QhnfsUtmer fraUdh

, pri c~-fixi ng, and many unsuccessful attempts at prope escape t e notlce of their victims. 
Asking individual respondents to define the eroblem.is of~en Of' ll "tt'le he'l· If you ask IIWhat should be stopped? most ~ntervl~ws Wl 

!~iCit a l~~t of social irritants--~nwashed hiPp;eds, r~d~c~1 p~~~~~sihe . . ters demonstrators--whlCh genera 1 ~y ? no a w. . ~~~~~ey~~n~~imin~l law. Methodological investlgatlons of the Vl~t~~- d 

~~~~~~!ewt~:c~~~~~~n:~~os~~~~:s~V~~aih~~~~rt~~~e~~~~e~~e~~~e~~~e ~l!~~~~s t"vity' disputes over the ownership of automoblle~, property confl. d ~~t~een ~x-spouses. and many assaults within the famlly or bet~een rlen s · tives fall into this category. In general, the followlng are (;nr~~aexperience) incidents which are relatively easy to measure through 

a survey: 

, 

• 

• 

• 

• 

• 

• 

• 

any incident involving a gun or knife 

any personal attack resulting in serious injury 

any assault or rape by a stranger 

successful as opposed to attempted crimes 

property thefts involving entry into the home 

property thefts involving large 10ss~s ~over about $50, although 

this varies by the wealth of the vlctlm) 

successful auto theft 

events which are unambiguously "crimes" .(wh~ch clouds.the interpretation of many lost wallets: was lt plck-pocketlng, or 

was it lost?) 

SAMPLING REQUIREMENTS 

n order to uncover the victims of these crimes, it is ne~essary to 

sam l~ the 'otential targets of crime. The unive~se of.po~ent~al targets andPprocedu~es for select~ng samples of them for lnt~rvle~lny lS not 

always clear, however. Households can be ~ampled uSlng Slmp e.~~~a~nits 

~~~~~~~~~i~ ~~~C~~~~~~y ~~~t~~~~da~~r~~~~t~~~~~h~~~m:~~~~t~~~~l In th; s~rveys being conducted for LEAA, interviews are completed wlth each me~e~ 0 

a sampled household, while other stud~es randomly select responoe~essam led 

W!~h~~ ~~~a1~~~P~lt~~~~r~~~~a~~~e;h~~~e~ro~e~~~~es~;i~b~~~h~!~ts (suchPas 

~ai~ d~essers working out of private homes)may be undersamp1ed. Large 

18 

office buildings present tractable sampling problems, since individual 

rooms can be sampled within them. Rare and hard-to-sample commercial 

operators like taxi-cab drivers have serious crime problems, and contribute 

disproportionately to crimes in such serious categories as armed robbery. 

To track them down requires imaginative sampling designs. Organizations 

also present a challenge. The victims of crime may include Boy Scout troops, lodges, and other entities which are social rather than physical units and 

which are tied neither to place nor person. Like taxi drivers, some 

organizations will require sampling designs of some sophistication and 

imagination. 

Sampling to find events can be costly. Many of the incidents in which 

we are most interested (rape, armed robbery) are relatively rare. When 

the Crime Commission first considered funding a victimization sblrvey in 1966, 

this was a major concern. Official statistics suggested that the robbery 

rate, for example, was only about 120 per 100,000 persons in the population. Given this "l ow density" of events, very large samples would be required in 

order to turn up enough incidents to talk about. Fortunately (but 

unfortunately), those encouraging the surveys were correct in their assumption that the "dark figure" of unreported crime was quite large, and that mJre 

workable samples would suffice. The victimization survey conducted;n 

Cincinnati in 1974 indicated victimization rates of the following magnitudes: 

Crime 

household burglary 

household larceny 

personal robbery 

personal assault 

rapes 

Rate 

14 per 100 households 

31 per 100 households 

1.5 per 100 re~idents twelve and older 

5 per 100 residents twelve and over 

0.2 per 100 residents 

By this standard, in order to accumulate a random sample of about 140 

burglaries (still a small number for many analyses), in many communities 

it would be necessary to quiz informants in 1,000 households or more. Simp'le thefts are more common, but the development of a decent sample of assault 

victims would require screening the experiences of several thousand 

individuals. 

Surveys to investigate the attitudes or perceptions of the population 

wou1d require much more modest samples. Unlike the relatively uncommon victimization'~;<periences, opinions are part of the personal baggage we 

carry around with us, and almost everyone has them. A sample of approximately 

1,200 respondents will reasonably represent the attitudes of a large community, and the group will be large enough to support many detailed analyses. It 

may be necessary to oversample certain key subgroups in a population in order 

to examine their responses, however. In many communities, stUdents or blacks 

may not be numerous enough to appear in large numbers in a random sample 

of the jurisdiction. Neighborhoods in which they dwell may be oversampled, 

and their responses can be down-weighted in analyses which ar~ being projected 

to the city as a whole. Sampling strategies are thus related to the substance 

19 

,. 

h 
of the problem at hand (e.g., how frequen~ are the attributes of 

interest) and the analyses in the data w~lch ~rep1a~ned (e.g., what 

subgroups of the population will be examlned l~ de~all). Once 

these judgements have been made, sample selectlon lS a problem best 

left to experts. 

WHAT TO ASK 

The problem of what to ask is tied to the purposes of a particular 

victim survey. In this section I will suggest some examples of the 

I"lnds of variables which should interest most surveyors, a~~l some tO~ ~he usee to which they may be put. In general, planners Wl wan 0 

know (1) what have the respondents experienced, (2) ~hY have th~y had 

those experiences, and (3) what can be done to a11evlate the Cflme 

problems of high-risk populations. 

Commercial Targets 

Comnercial establishments should be a parti~ul~r.foc~s of attention . . surveys T~Oi" I, .... ,,', \-.,,:;,:. il:'\'C'I'ellle'iy high vlctllnlZatlon rates. One 1n crlme ,1.1It::,}' " .... ~ ~ • • 'I 

national sample of businesses indicated that ln 1973, commerCla f b . 

establisrlme~ts were burglarized a

4

ta ra1t

o

e

o of .20 t~~~s l~~/lp:~~~n~l r~~~~~~s. The COl11merCl a 1 robbery rate was per , SlX .. . rate. These victimizations were not randomly distnbuted: some .t{~e~ d 

of stores were hit much more often than others, and a !ew were V1C lmlze 

many times while others were never attacked. The cru~la1 targets ap~~ar to be as stations, convenience groc~ry .s!ore~, and 11guor stores .. _ e 

reasongfor their particularly high vlctlmlzatlon rate 1S prO~le~atl~'h _ b~t victimization appears to be related to being open after. ~r f~nd aVlng few customers or employees about to make trouble f~r potentla o. en ers. 

In general perhaps 5 percent of commercial establ~sh~ents experlence 

about 75 p~rcent of all commercial robbery, and thlS 1n turn makes up 

a subst~ntial proportion (perhaps 25 percent) of all the arm~d rOb~~rYt_ in an average community. This is a IIpressure point" whe~e t e app lea.lon 

of olice resources might pay substantial dividends. It lS also a fertl1e fie~d for investigating the effect of various an~i-theft e!forts UPo~ll victimization rates. Within a sample of co~erclal establlshments WI 

be found all manner of crime-preventinn de~i'1~es: burglar ala~ms, floor s~fes, armed guards, and ~lectronic shopliftlng de~ectors. Wl~h the. _ ro er controls for other factors (hours of operatlon, emp1oye~s, nelgh~or . ~oo~), it may be possible to evaluate the effectiveness of varlOUS comblnatlom 

of self-protective measures. 

Household Targets 

For some kinds of incidents it i~ useful t? think of the household 

rather than an individual as the victlm of a Cflme. Bu~gla\y~ for example, is distributed in response to the vulnerability and.deslrabll1t~ of the 

target household. Dwellings are desirable targets If they promlse to 

20 

, 

contain goods of high value which can be fenced. They also are vulnerable 

if they are protected by weak locks or doors, or if no one is home during 

the day, or 'if they at'e located in large and anonymous buildings without 

adequate security arrangements (see the study of residential crime by 

Reppetto listed in the bibliography). While burglary does not generate 

the .kind of community insecurity which spills over from personal crime, it 

strlkes a large number of people. In 1973, the household burglary rate 

in the United States was over 9 per 100 targets, higher than all the 

robbery, personal theft, and interpersonal violence put together. 

Because of its high incidence, burglary may be a particularly useful 

subject for study. A modest local survey (3,000 households or so) would 

generate a relatively large number of events for analysis. Be~ause it is 

not often solved (the clearance rate for burglary is under 20 percent) 

and not often report.ed to the police (less than half of the household 

burglary in 1973 was: reported), efforts to reduce burglary must proceed 

on the basis of some' new information. Surveys could collect information 

on the timing and style of entry~ which could be used to alert police 

patrols. The impact of such programs as Operat'ion Identification (property marking) could be assessed by comparing burglary rates for matched 

households which displayed and did not display signs signaling their 

participation. One innovative survey in Portland attempted to gather data 

to gauge the impact of street lighting on household crime by having 

interviewers count the number of streetlights visible from the doorstep. 

Individual Targets 

While individuals are frequently the targets of crime, data on their 

experiences is often of little utility for operational planning. Individuals~ 

unlike households or stores, are not pegged to a particular geographical locatio~. While it is informative to know the relationship between such 

factions as personal income and the probability of being victimized, police departments are OI~ganized along patrol car beats. Victim data is more 

useful if information an the location of the offense can be gathered in 

addition to information on the residence of the v'ictim. This wi11 not 

be easy, for individuals vary greatly in their knowledge of a community and how to describe it to an interviewer. Some will vaguely place an 

incident an "the East Side," others in some socially defined neighborhood, 

while some win be able to give an approximate street address. The goal should be to place each incident within some useful administrative unit 

like a police precinct; there it can be related to other area data and used 

for allocation purposes. Individual-level data can also be used to evaluate 

differential respanse time by neighborhood. This is important, for current 

measures reflect only administrative response time--the ]ap between the 

reporting of an incident and the arrival of a patrol car. In deterrence 

studies it is more important to know the length of time which elapses between 

the commission of the crime .and the arrival of an officer on the scene, 

for almost all important arrests are made by beat patrolmen ~ho apprehend offen~ers before they can flee the site. Personal interviews also provide ~ vehl~le for assessing reports of where people will not go at night (suggest- ' 

1ng nelghborhoods that could use some visible patrols), and their fear of 

various kinds of victimization {suggesting an alternative to the use of incident totals for allocating resources]. 

21 
SOME PRACTICAL SUGGESTIONS AND COST ESTIMATES 

The decision to undertake. a community victimization survey is a serious 

one, for they are expensive. The direct cost of personal interviews (in 

1974 prices) is about $15-$18 per completed questionnaire. For a workable 

sample (say, 4,000 interviews) this amounts to about $70,000. The field 

interviewing in a large scale survey should be contracted to a market 

research firm or to a university survey research unit. For an additional fee 

such an organization will also draw a sample. A block-probability household 

sample is expensive to construct, and the fee using such a sample may amount 

to several thousand dollars. Analysis of the data can be contracted to 

a marketing firm or to a un.iversity, and will cost about half as much as 

the interviewing for a quick-pass examination. A serious survey analysis 

should also include an examination of official crime records. These records 

can be compared to survey rates for the same classes of incidents, and can 

be used to supplement s'urvey data on those crimes for which the latter is 

an inappropriate measurement technique. Pulling police ~~cords, geo-coding them to match the survey data, and recording the data contained in the file, 

are clerical tasks of some magnitude. In the end, it may cost as much as 

$180,000 to conduct a commun'ity crime study of this size. 

At present there are efforts underway which should help reduce the cost 

of conducting victimization surveys. Several years of effort have been 

expended by the Bureau of the Census and LEAA in developing a reliable 

survey questionnaire for measuring the incidence of more serious offenses; 

this could be adopted and modified for local use at little cost. The Police 

Foundation in Washington, D.C. is now sponsoring an investigation of the 

reliability of telephone surveys of victimization. Telephone surveys can 

be conducted for about 35 percent of the direct cost of interviewing, and 

'if they prove valid they may greatly facil itate the use of victim surveys at 

the community level. Analysis expenses could be reduced greatly by the 

formation of consortiums of users who desire the same information. Running 

many data sets in the same fashion is not mucfl more expensive than analyzing 

the results of a single survey. This is precisely the kind of coordinated 

activism that could be greatlY facilitated at the state level by actions 

of criminal justice planning agencies. 

BIBL IOGRAPHY 

Bordua~ David J., and Larry l. Tifft. "Citizen Interviews, Organizational Feedback~ and Police-Community Relations Decisions," Law and Society ~eview~ VI (November 1971), pp. 155-1,82. 

Campbell, Donald T., and -Julian C. Stanley~ Experimental and QuasiExperimental Designs for Research. Chicago: Rand McNally, 1963. 

fUrstenberg, Frank F., and Charles F, Wellford. "Calling the Police: The 

Evaluation of Police Service, II Law and Society Review, VII (Spring 

1973), pp. 393-406. 

22 

, 

Law Enforcement Assistance Administration National C . . 1 . 

Information and Statistics Service 'II,' nl:l1n~ JUstlce 

the Dayton-San Jose Pilot Survey of Vi~~~~~~a~~~nV~c~lm~: ~ Report on 

Law Enforcement ASSistance Administ t' ,.' a~ ~ng on, D.C.: 

Information and Statistl~cS S . rJa lon, Natlona1 Crlmlna1 Justice ervlce, une 1974. 

Maltz, Michael. "M~asu~es of Effectiveness for Crime Reduction Pro rams II 

Operations Re$c~rch, XXIII (May-June 1975), pp. 452-474. g , 

Reppetto, Thomas A. Residential Crime. C b Co., 1974. am ridge, Mass.: Ballinger Publishing 

Reynolds, Paul D. and Dale A B1 th ItS . Rela+ionshi 'B :y. ources of Varlation Aff~cting the 

chapter 16 pVi~~~ee~ PO:lce and SurveY-Bas:d Estimates of Crime Rates," 

Israel Drapkin lV~lOgliI A ~ew,Fo~us. Edlted by Emilio Viano and , '" eXlng on, Mass.: Lexington Books, 1975. 

Skogan, We:5ley G. "Measurement Problems in Official ' 

Journal of C[imina1 Justice, III (Spring 1975). and Survey Cnme Rates," 

Skogan, Wesley G' I "public Policy and Public Evaluations of Justice System 

~~r;~~na~ced" chapt~r ~, Crime and Criminal Justice Policy. Edited 

1975. n ar lner an Mlchae1 Mulkey. Lexington, Mass.: D. C. Heath, 

Skogan. ,We,sley G. liThe Victims of Crl'me.· S ' C 1 B h ome Natidhal Survey Findings II 

nmlna ~ avior and Social Systems. Edited by Anthony L. Guenthe:'. 2d ed. Chlcago: Rand McNally, 1975. ' 

Smith, Paul E. and Richard 0 }lawki "V' t" , 

~~~; ~~, c~h~Cf~~ 1 ~n~ 9~jf: t~~~s 1 fb~t ~~e '~~ ~~~; ~~' l~~P:~d o~o~:!~~en-

" ;.\ ' 

23 

) i 
CHAPTER 3 

UNIFORM CRIME REPORTING: 

USES FOR CRIMINAL JUSTICE PLANNING 

Federal Bureau of Investigation 

I NTR ODUC T ION 

The criminal justice planner must maintain a perspective of the entire 

criminal justice system. While the criminal justice system has many facets 

not easily defined t it is generally accepted that the system is ,composed of 

several modules. law enforcement t prosecutions courts, probation and pa- role, and corrections each playa vital role in the criminal justice process. Historically, these modules have acted with a high degree of uniqueness. It has been found that the interrelationship among these various 

modules has been minimal. To properly fulfill his responsibilities, the 

criminal justice planner must look to each entity while still maintaining a 

perspective of the whole system before efficient, effective planning can 

proceed. In nearly every instance, individuals respons'ible for committing 

criminal acts enter the system at the law enforcement level. Because of 

this fact, the planner must be fully knowledgeable of the information available to him from this module inasmuch as it will impact, eventually, all 

other modules of the system. The most readily available and most comprehensive set of data accessible to the planner relating to la'l' enforcement is 

that provided by the Unifm'm Crime Reporti 119 (UCR) Program. For the pl anner to effectively utnize this information, he must be cognizant of its 

scope and limitations. Further, he must be conversant with the terminology 

and procedures of this program if it is to be used in drafting futuristic 

plans for the entire system. 

BACKGROUND 

The UCR Program was conceived, developed and implemented by law enforcement for the express purpose of serving law enforcement as a tool for 

operational and administrative purposes. (Throughout this presentation, 

the term law enforcement is used to refer to the traditional police function of the criminal justice system.) Under the auspices of the Internotional Association of Chiefs of Police (IACP), the UCR Program was developed in 1930. Prior to that date t no comprehensive system of crime information on a national scale existed. This was, primarily, due to the fact 

that the criminal statutes varied so greatly from state to state in their 

use of terminoiogy to define criminal behavior. To overcome this problem~ 

a set of definitions for specific criminal acts was devised. These definitions were developed following an in-depth research effort relative to 

each of the state criminal statutes extant. It was determine~ that law enforcement would tabulate the number of criminal acts as defined by the UCR 

Program as these acts were brought to the attention of law enforcement. 

Recognizing the problem of coping vlith mere volume, it was decided that 

only those criminal acts deemed serious would be counted. It was necessary 

to define "serious.or In this regard, a criminal act is considered serious 

if it meets a set of criteria; namely, that the act would occur regard1ess 

of geographical location; that it would be an offense most likely to be 

25 

.. ------,------------------------
reported to law enforcement; that it would ,affront the mor~l sensi~i~it~es of our society1s rational being; and that lt w~uld occ~r v!lth sufflclent 

frequency so that counting it would be wor~hwhlle sta~1~tlca1~y, ,In a~p~y­ ing the concept of seriousness to the multltude of cflmlnal acts ldentlfled, 

a select group evolved. This group of criminal acts is referred to as the 

"Crime Index." 

The Crime Index offenses and their definitions are s~t forth below: 

1. CRIMINAL HOMICIDE.--(a) Murder and nonneg1igent manslaughter: .All 

willful felonious homicides as distinguished fro~ death~ ~aused by.negllgence. Excludes attempts ~o ,kill, assa~l~s to klll" ,sulcldes, ,a~cldent~l deaths, or justifiable homlcldes. Justlflab1e homlc~des ~re llml~ed to. 

(1) The killing of a person by a law e~forcement offlcer.ln,the ll.ne of 

duty; and (2) The killing of a person 1n the.act of comm1tt1ng a ~elony by 

a private citizen. (b) Manslaughter by.neg~1gence:, Any death Wh1Ch the. 

police investigation established was pr1m~r1~y attrlbutable to gross neg11-

gence of some individual other than the vlctlm. 

2. FORCIBLE RAPE.--The carnal knowledge of a female, forcibly and d 

against her will in the categories of rape by force, assault t~ r~pe, an 

attempted rape. Excludes statutory offenses (no force used--vlct1m under 

age of consent). 

3. ROBBERY.--Stealing or taking anything of value from ,the ,care, custody, or control of a person by force or violence or by puttlng ln fear, 

such as strong-arm robbery, stickups, armed robbery, assaults to rob, and 

attempts to rob. 

4. AGGRAVATED ASSAULT.--Assau1t with intent to kill or for the purpose of inflicting severe bodily injury by shooting, Gutting, stabbing, 

maiming, poisoning, scalding, or by the use of acids, explosives, or other 

means.' Excludes simple assaults. 

5. BURGLARY--BREAKING OR ENTERING.--Burglary, housebre~king, s~fe­ cracking, or any breaking or unlawful entry of a struct~re Wlt~ the lntent 

to conmit a felony or a theft. Includes attempted forclble entry. The UCR 

definition does not include auto burglaries, burglary of moveables, or a 

wide variety of such incidents as included in some state statutes. 

6. LARCENY-THEFT (EXCEPT MOTOR VEHICLE THEFT).--The unla~ful ta~ing, 

carrying, leading, or riding away of property.from the possess10n or constructive possession of another. Thefts of b1c~c1es, automobile accessories, shoplifting, pocket-picking~ or any steal1ng of pl"operty or article 

which is not taken by force and vlo1ence or by fraud. Excludes embezzlement, "con" games, forgery, worthless checks, etc. 

7. MOTOR VEHICLE THEFT.--Unlawfu1 taking or stealing or atte~pted theft of a motor vehicle. A motor vehicle is a self-propelled vehlc1e 

that travels on the surface but not on rails. Specifically excluded from 

this category are motor boats, construction equipment, airplanes, and 

farming equipment. 

Since the inception of the UCR Program the FB~ has acted ~s administrator, by Congressional mandate, of the program. S1nce 1930, tlme1y 

26 

£16? nr ) 

, 

publications have been issued presenting the results of the monthly crime 

data collection effort. 

STATE UCR PROGRAMS 

. Duri~g tha~ p~r~od of time when.UCR was still a concept, it was recog- mzed ~hat the 1nd1V1dual states WOUld also need crime information of parti- cular 1nterest to the state but of no great importance to the national view 

of crime. It was not until the latter part of the 1960 ls that funds became 

available for states to consider the development of their own individual repot'ting systems. 

The p~r~ose ~or state UCR programs is multifaceted. First, with per- s~nne~ a~m~n1ster1ng ~ state ~rogram, more direct and meaningf~l contact 

w1th :nd'vldu~l co~trlbutors 1S realized. Second, the ability to expand con~r1butorsh1p eX1sts due to state personnel readily available to lend 

ass1stance. Nearly every state thus fay' enjoying the services of a state U~R.program h~s enacted mandates requiring law enforcement agencies to par- t~c1pa~e. Thlrd, mandat?ry participation insures a law enforcement agency 

w11l elther enhan~e an already existent records system or will implant one ~apa~le of p~oduc1ng t~e needed data. Fourth, with state personnel review1ng 1nformatlOn emanat1ng from law enforcement contributors and this infor--

mation being checked at the national level, the validity as well as completeness of data is further insured. Fifth, individual state programs can 

address problems that are unique to the state. For example, numerous Northern states are vita 11y concerned over the theft of snowmobil es whil e this 

data is of little or no interest to those states in'~ore Southern climates. 

As of 1975~ 32 states have ~perational U~R programs. It is anticipated that at least nlne more states w1l1 be operat1onal during ca1endar year 

1976. 

Th~ procedures.uti1ized in data handling for state programs and found ~o be hlgh1y effect1ve are for the state program to continue the collection, 

1n ~urmlary form, of basic UCR data. Upon acquiring at least one year of ex- per1enc~ of such collection, a state program is urged to sophisticate its co11ect~on efforts. There are states currently embarked on incident-based c~llectlo~ programs. In these programs, individual law enforcement agen- C1~S p~ov1de the ~tate program with a copy of an actual incident report w~lch 1S sta~~ard1z~d thr~ughout the state. Under this concept, the indi~ 

v~dual contr1Dutor 1S rel1eved of UCR data compilation responsibility. 

W1th the incident reporting form in hand, the statistical effort can address 

such crime-related problems as stranger-to-stranger offenses, etc. 

The state programs are expected to feed back to individual contributors 

the necessary information required by the agencies for administrative and 

operational purposes. State programs are urged to maintain close and direct 

contact with the contri butors to insure the needs of 1 aw enforcement are 

bei ng met. ) 

27 

'

.

f 
TYPES OF INFORMATION COLLECTED 

Offenses 

Law enforcement agencies are requested to provide, on a monthly b~sis, Crime Index offense information with selected breakdowns. The collectlon 

device is structured in order that the agency may tabulate all Crime Index 

off0.nses btought to its attention. It is further designed to permit a tabulation of t~\ose offenses which upon investigat'ion have been found to be 

false or baseless reports. The total volume of all bona fide actual Crime 

Index offenses is then tabll1ated at the national level. 

Clearances 

For UCR purposes, a clearance is defined as the arrest of one or ~Qre 

'individuals responsible for the commission of an Index offense. There are 

exceptions to this rule wherein a clearance is scored by exceptional means. 

This is true in the event that the individual s for some reason beyond law 

enforcement IS contro'l, cannot be arrested or other\'Ji se handl ed. Cl earances 

of reported actual offenses committed by those 'individuals ~nd~r the age of 

18 who have been arrested or otherwise handled for the commlSSlon of an Index offense are also counted. This is done in an effort to identify the 

youthful offender problem. 

Value Loss 

In order to supplement the monthly offense and clearance data, the 

value of property stolen and recovered by specific type of property is collected. Value loss is also collected as it relates with each of the Index 

offenses along with additional detail for each of the offense classifications. 

Arrests 

Arrest information is a valid measure of one aspect of law enforcement 

activity. To fully capture this data set, the UCR Pr?gram reques~s, ?n a 

monthly basis, the number of persons arrested, exc1uslve of trafflc vlo1ations. This arrest information is broken down by age, sex, and race of the 

person arrested. 

Law Enforcement Officers Killed and Assaulted 

To fully explore the perils associated with the law enforcement profession, data is gathered on a monthly basis relative to those situations 

wherein officers are assaulted. This data collection effort attempts to 

identify the type of officer activity, type of weapon used by the offender, 

type of officer assignment, extent of officer injury, and time of day for 

each of the assaults. Law enforcement agencies are readily guided by this 

data set in the development of training programs and acquisi~io~ of equ~p­ ment designed to minimize situations that are dangerous. ThlS lnformatlon 

further acts as a warning device to officers given specific assignments. 

28 

« 

An adjunct to the assault studies is t~e detailed analysis of situations wherein an officer dies as the result of a felonious attack. 

DispOSition Data 

On an annual basis, law enforcement agencies are requested to provide ~ummary totals on the number of persons formally charged by type of crim- ln~l act. The result~ of these charges aY'e requested by guilty verdict 

gUllty of a lesser ~fren~e, acquitted or dismissed, or whether the chargee 

was turned over to Juvenlle court. 

Law Enforcement Employee Data 

On an annual basis, each contributing law enforcement agency is requested to provide the number of fun-time, swot'n and civilian, employees by.sex, on duty as of October 31 of the specific year. Through 1975, certaln data elements of shift assignment have been collected. 

Bomb Information 

.While not a.direct part of the UCR Program, semiannual publications ~re l~sued.relatlVe t~ actual and attempted incendiary and explosive bomblng sltu3tlons occurrlng throughout the United States and Puerto Rico. This 

data presents such oC~Hrrences by geographic region, division. and state, 

as well as by ascertalnable motivating factors. 

TYPES OF INFORMATION PUBLISHED 

Each qu~rter, on ~ calen~~r year basis, the UCR Program issues a re- por~ re~lectlng trend lnformatl0n on the Crime Index offenses. The prese'n- tatl0n ls.by percent change of the current time period with the same period 

of the prlor y~~r. The trends are exhibited on the basis of total volume 

as well as by vlolent and property crimes and also by each of the Index offenses. A breakdown by popUlation grouping, by cities and counties in 

terms of trend, ~s set forth. A five-year, long-term trend table for each 

of.the offen~es lS presented. Also in the quarterly report is the useable Crlm~.:Index lnformation for all cities over 100,000 population. 

On an annual basis, the UCR Program issues a comprehensive annual report entitled IiCrime in the United States. l

! This document sets forth the 

total ~nited.S~a~es' crime picture based upon the UCR Program. Tabulations 

?y ~e~lon, d~v~slon, and state; Standard Metropolitan Statistical Are-c,; ami lndlv~dual cltles having over 10,000 in population are presented in detail r~latlve to the Crime Index. Correlating clearance, arrest, and disposi- tlon ~ata are presented. Tables dealing with short- and long-term trends 

rel atlVe ~o offenses, cl earances, and arrests portray the crime experi enCt:! 

?f th~ Unlted States. While much of the information, other than that deallng wlth o~fenses, is not published agency by agency, this data is presented 

by popu1atlon grouping. 

The presentation of arrest information is not published agency by 

agency as in the case of offenses. The mere volume precludes such 

29 
publication. However, available data that"is useable is provided,u~on request. Arrest information is presented in summary form by geopo11tlcal breakdowns as well as by age, sex, and race. Both current volume and trend 

data by population grouping are tabulated. 

Law enforcement employee information relative to sworn and civilian employees is presented in summary fashion. Tab1es reflecting interquartile ranges~ by population groupings and by officer to population rate, show the 

law enforcement strength. Tables are constructed by agency for those places 

with a population of 25,000 or more inhabitants. 

A special section of the annual publication, "Crime in the Un~ted States," deals with the criminal repeater. In 1963, a program entltled 

"Careers in Crime\1 was initiated. Individuals arrested for the first time 

for a Federal offense were tagged for tracking. The statistical effort did 

not deal with identities but only with numbers of offenders. As time passed and these individuals were rearrested, the statistical data was accumulated 

and certain observations were apparent on the basis of the rearrest experience. The i969 issue of IICrime in the United Statesll renders a comprehensive view of the Careers in Crime effort. 

With the advent of the Computerized Criminal History (CCH) Fi1e, administered by the FBI, an updated data set was established. The 1972 publication of "Crime in the United States" presents more recent tabulations on 

repeaters in terms of rearrest experience. Profiles of offenders rearY'e~ted as well as the mobility factor of rearrested persons are shown for the tlme 

period 1970-1972. Follow-up studies with this data base appear i.n the annual publication for 1974. 

LmITATIONS OF DATA 

The UCR Program is not a perfect program. While it can be of invaluable assistance to the criminal justice planner, there are definite limitations with which the planner must be familiar. 

Not all law enforcement agencies enjoy the services of a records system 

capab1e of producing UCR data as a by-product of the system1s overall design. As a side note, the IACP, in developing the UCR concept, recognized the need 

for a 1 aw enforcement records system with; n each agency. It was felt the 

individual lavi enforcement agency would develop such a records system in order to be an acti~e participant in this program. Their feelings have proven 

correct. In 1930, there were only a lHtle over 300 participants and in 

1975, there are in excess of 12,000 law enforcement agencies providing UCR 

data, at least in part. The fact still remains, however, there are some 

agencies which do not participate and, consequently, there is not 100 percent coverage of the Nation. 

Another limitation deals with the incompleteness of data. Certain 

agencies are able to provide limited segments of the total UCR request but 

lack the facility to respond totally. The inability to provide complete 

data is generally caused by the type of records system utilized by the 

agency • The system may well serve the departments I needs efficiently but 

is not designed to capture total UCR data. 

30 

It must be borne in mind that from the standpoint of criminal incidents, only seven are tabulated o~ frequenoy of occurrence. Therefore, the 

UCR Program addresses only a portlon of the total crime picture reported to 

1 aw enforcement. 

The recommended,procedures for developing UCR data at the contributor 

level are set for~h 1n a handbook,provide~ ?y the FBI. As in any statisti- ca~ pr~gram of ~hlS n~ture, the flSk of m1s1nterpretation is always present, ThlS ~,sk, comblned ~lth a degree of subjectivity on the part of those re- sponslbl~ fo~ captu~lng UCR data, may create a bias in the data. This sh~rtcomlng 1S ,partlally overcome by correspondence between the fBI and contnbutors and 1n numerous training sessions held throughout the country. 

A mo~e ~ubtle,an~ ~ervasive limitation deals with program and policy changes wlt~ln an 1nd1v1dual.department. For example, an agency1s burglary volume may lncrease substantlally, not because of an actual increase in the numb~r of burglaries occurring but because the department has embarked on a 

broad base p~ogram to reduce burglaries. The public, developing an awareness to the ~nterest of ~aw enforcement, proceeds to report burglary offenses that 1n ~he,p~st lt may ~ave ignored. The planner, when studying trend data for !ndlVldual agencles, must be alert to such aspects which 

could affect crlme data. 

, Along similar lines as the aforementioned, influence can be exerted on 

crlme ~a~a by the general attitude of people and ~nique legislation in given ge~p?lltlcal .a~eas. Numerous places are interpreting drunkenness as a non- c~lmlnal act1v1ty. Some areas have decriminalized certain drug abuse violatlons. Consequently, this will have an impact on arrest information. Agaln, the planner must be alert to these situations. " 

T~e criminal justice planner must be cautioned to not utilize UCR data 

as a stand-alone body of knowledge. There are numerous factors which influ- ~nc~ th~ occurre~ce of crim~. As a caveat, each annual publication of 

Cr1me 1n the Un;ted Sta~es p.re~ent~ a p~rtia1 listing of those sociologi- cal phenomena WhlCh are 1nfluent,al 1n cnme causality. 

CALCULATION OF RATES AND TRENDS 

. TO,deve!op meaningful ,p~ans~ the criminal justice planner must, at some p?ln~t ldent,fy areas requlnng lmmediate or long-term intensive attention 

Wl th"1 n the. pi all. . He must,. a t s~me ~oi nt, array numeri co. "I data in such a way 

as ~o prov1de a plcture WhlCh wlll ldentify areas of need. While;s;s rec- ~gn~z~d that examining the vo~ume of ~r;me occurrence eithe~; totally or by lnd1vldual offense over a perlod of t1me for a particular agency is important,.the planner must also view this experience for a selected group of 

agencles. An agencY,equalizing factor must be developed. In short, he 

needs a common,denomlnator. The geographical area he is studying will un- ~oubt~dly consl~t of,law enforcemen~ agencies of various strengths and serv1ng w1dely varYlng Slzes of populatlon. To accomplish a leveling technique 

the concept of rate is suggested. ' 

) 

The crime rate is defined as the number of Index offenses occurring per 

l?O,OO? people over the period of one year. In the way of example, a communlty w1th 100,000 people that experiences a reporting of 2,500 Crime Index 

31 
offenses for a given calendar year is said to have a crime rate of 2,500. A 

second community having a population of 20,000 people that experiences 500 

reported Crime Index offenses in a given calendar year is also said to have 

a crime rate of 2,500. 

A common factor for any community can be acquired by dividing the community's population by 100,000. Once this factor is determined, it becomes 

a relative constant until such time as the population of the community changes appreciably. The crime rate can be ascertained by dividing the total reported Index crime by the factor. If the planner wishes to identHy the crime rate for an individual offense, the total number of that reported 

offen;.e is divided by the factor. 

Frequently, a planner may desire to compute a crime rate on partial 

year data. When this is done, only a fractional portion of the jurisdiction's population should be used, correlated with the fractional part of 

the year to be observed. If a city has 100,000 ~eople and the crime rate 

for the first three months is desired, then only one quarter of the population would be used in computing the crime rate. In this instance, 25,000 

would be divided by 100,000 (one quarter of the city's population) and the 

factor becomes .25. The offense counted is then divided by this factor and 

the result would be the crime rate. 

Crime trend information is a presentation of the fluctuation of data 

from one time period to another. Trends can be computed on the basis of 

volume or rates. In effect, a trend is presented as a percent change. The 

important factor to consider here is that similar data bases and similar 

time periods are used for comparison in determining the trend. To compute 

a trend, always subtract the current period from the prior period. The result is then divided by the prior period. The result of this division represents the percent change or, in other words, the trend. 

Trend compilations can be applied to any of the many sets of UCR data, 

just as long as the aspects of jurisdiction and time-frame are similar. 

The development of trend data requires certain degrees of discipline 

within the collection methodology. The observer must be assured of the constancy of data conversion. There should be no evidence of policy or pro- cedural changes in data capture within a given agency. The observer needs 

two or more set time periods for data observation. If he can rely on these 

two aspects, then a trend observation can be made. Trend tabulations are 

no more than an aligning of what has occurred within a given recent time 

period with what occurred in a prior identical time period, all identifying 

characteristics being equal. 

Assuming that seven law enforcement agencies in a given geopolitical 

area experienced 600 reported Index offenses for a three-month period and 

all seven had population coverage of 80,00Q people, only the following can 

be observed. 

32 

Agencies Population 

7 80,000 

Offenses 

Reported 

600 

Period 

3 months 

~f, in a later period, preferably a year or more, the same set of 

agencles has 750 offenses reported for the same three-month period a more 

meaningful presentation will develop. ' 

Agencies Population 

7 80,000 

Offenses 

Reported 

Period 1(600) 

Period 2(750) 

Period 

--

3 months 

Percent 

Change 

25% 

The r~te change, affected by an adjustment for partial year coverage 

of populat~on,would be a crime rate in the first period of 3,00Q and in the 

second perlod 3,750. The percent difference, or rate trend, becomes 25 

percent. 

Historically, trend data is computed on the basis of volume for Crime 

Index offenses on a quarterly basis. Rate differentials are reserved for 

annual observance. 

,'One of the measures of law enforcement effectiveness is the clearance 

rate. The clearance rate is calculated by dividing the number of offenses 

cleared, as per this program's definition of clearance, by the number of 

offenses kno~n with the result being multiplied by 100. If an agency 

should experlence a total of 72 robberies, 38 of which were cleared, a 

clearance rate of 52.8 percent would exist. / 

While the crime rate is computed on the basis of the number of Index 

off:nses per 100,000 people, the police employee rate is developed on the 

basls of 1,000 people. Both concepts utilize the same arithmetical func- tion~. To compute the police employee rate! divide the jurisdiction's pop- ulatlon by 1,000, then divide the number of police employees by the result. 

When the employee rate is referred to, it is always construed to be the 

number of police employees per 1,000 people. 

.. As mentioned earlier, UCR information represents one data base. The :p~anner must consider other data bases in order to acquire a well-rounded 

plcture of the sociology of the given jurisdiction. He must look to such 

sets of data as school dropout rates; unemployment rates; economic levels· 

gross population characteristics relative to age, sex, and race and from' t~ese data sets, develop rates ahd trends. that correlate arithmetically wlth the crime data manipulations. 

APPLIED USES OF UCR DATA 

The criminal justice planner must respond to the needs of the criminal 

justice c~mmunity in his area. Before he is abl e to respond lto these needs, 

he must flrst identify them, determine priorities, and proceed in such a fash~on ~hat, while meeting the needs of one aspect or module of the criminal Justlce system, he does not unnecessarily intensify existing needs or 

33 
create additional needs in some other modu1e' of the system. In an effort 

to preclude such situations, a starting point for study must be found. It 

would appeat the logical point would be within that modu1e first dealing 

with matters of interest to the entire crimina1 justice system; namely, 

law enforcement. (mother consideration would a'\so be to consi.der that 

module first which is able to provide the most comprehensive picture of 

its activities. In I,nany instances, this would again be law enfo~'ce:ment. 

In each of the following suggested uses of UCH data. it is assumed that 

efforts have been made to acquire other information relating to sociological 

phenomena outside the purview of law enforcement. 

1. Offenses Known--Create a line chart covering a five-, and if availaule, a t'en-year nlusfration of the volume variation of reported offenses. 

Now, do the same for the crime rate. By overlaying similarly constructed 

data from other data sources both from within and without the criminal justice system, significant patterns may evolve. Certain projection characteristics may offer the planner a guide that would have an impact on all 

modules separately and/cr collectively. 

2. Clearance InfQrmatiQn--To some degree, this is a measure of law enforcement efficiency. To use this data, structure graphs showing timeseries experience by individual agencies and groups of agencies. Make correlations with the nationa1 experience and with the appropriate popu1ation 

group experience. The annual publication, "Crime in the United States/ 

gives this information. 

Ptosecution and court infonnation, overlayed with clearance data, will 

give an in-camera view of the workload correlation and provide for future 

planning. 

Care must be taken in time-series analysis to allow for impact of such 

things as precedent setting court decisions which have had a direct effect 

on caseload responsibility. An example of this WOUld be the effect of a 

court decision impacting the length of time to try a case. 

3. Youthful Offenders--By use of additional background, the criminal 

justice plann2r can rook to three areas of UCR data collection for information on youthful offenders. First, one segment of clearance information 

addresses those offenses cleared by persons arrested under the age of 18. 

Second, age, sex, and race characteristics of persons-arrested data present 

arrest information in such a format that specificity of age can be deter'-

mined. Third, in conjunction with the collection of arrest data, individual agencies are asked to provide data on juveniles (as defined by state 

statute) that are (a) Handled within the departnient and released; (b) Referred to juvenile court Dr probation department; (c) Referred to welfare 

agency; (d) Referred to other police agency; and (e) Referred to criminal 

m' adu 1 t court. 

The planner may desire to structure tables and graphs reflecting the 

law enforcement experience with the youthful offender! correlating the aggregate data with population composition; school dropout rates; family size 

and composition; detention facilities; population configuration; parole, 

probation, and welfare caseload composition; and other data sets wherein 

youth or juvenility are intrinsic. 

34 

By utilizing this data, again 'in time-series, certain observations: can 

be made. To some degree, the time lapse can be determined between en- , coun~er~ n~ the ,youthful offender by 1 aw enforcement and the process i ng of 

the lndlvldual at later' stages of the criminal justice system. Workload! 

projections will be inferred by pictorially pf'esenting this informati'on 

with bar graphs and other such analytical devices. As in other compilations) 

care must be taken to make the observations on the basis of volume, rate, and 

trend. 

By wedding the various data bases, the planner may 'identify needs not 

heretofore encountered by anyone Ot' all of the crimiYlial justice modules. 

It is conceivable the planner may decide to suggest a strengthening of a 

law enforcement agency1s manpower commitment to youthful offender crime. He 

may, in turn, envision a need for intensified foster home programs or halfway houses. He may discover that while detention facil'ities currently being 

used are sufficient, in the next five years they will be taxed to the detriment of the overall system. 

4. Arrest Information--As mentioned earlier, this data is partially a 

measure of police activity. By constructing various visual analytical devices, utilizing time-series, the planner can identify the fluctuations of 

1aw enforcement activity. To some degree, such compilations test the effectiveness of programs initiated not only within the law enforcement module but also the other modules of the system. 

By testing law enforcement's arrest experience against the aqe composition of the population and correlating this information with other data 

sets, some of which have been previously mentioned, and again applying timeseries! limited projections will evolve. 

It should be recalled that clearance information does not relate directly to arrest information; a cl earance can be scored o,nly by the arrest 

of one or more persons and conversely, several clearances can result from 

the arrest of only one person, With this thought in mind, associating total arrest numbers for specific offenses with the clearance rate, certain 

observations can be made. Offenses which inordinately cause the arrest of 

numerous people can be isolated and innovative programs designed to address 

this problem. This is one of many instances where the criminal justice 

planner may be in a position to recommend programs to agencies and groups which are outside the purview of the criminal justice system. 

Arrest data on the sex of persons arrested, utilized in ways previously mentioned, may suggest to the planner the need for future detention 

facilities, personnel composition of parole and probation services, and the 

development of innovative programs within law enforcement agencies to handle 

female offenders not heretofore needed. 

Certain implications are involved when analyzing the population composition of persons arrested. By correlating such data \I/ith economic, employment, education levels, housing, etc., data and applying age, sex, and race 

characteristics to these data sets and again using time-series, it may be 

determined that a small, suburban law enforcement agency can)anticipate a 

significant effect on activity at some point in the future. Population 

shifts and mobility must be taken into account. This is only one application dealing wit~ only one module of the criminal justice system. Obviously, all modules will eventually be im~acted. 

35 
5. ~? i tion Infonnad on--Of all the sub-data sets in UCR di scussed, 

disposition lnformation is generally the most dHficu1t for law enforcement 

to acquire. This is primarily because this data set is ordinarily outside the nonna1 channels of information within ill law enforcement agency. 

Once a matter is presented for prosecutive action, the law enforcement agency. 

to a degree, loses control IOf pertinent statistical information relating to 

the disposition of offenders. It is only in those instances where a smooth, 

cooperative information exchange system among the law enforcement, prosecuti on ~ and court modu1 es exi s ts that the 1 aw enforceffi.ent agency is able to report efficiently on the disposition of cases handled. Consequently, the 

criminal justice planner may be unable to rely on law enforcement to provide this data. In those instances where it is available. obvious benefits for 

the planner are forthcoming. The planner will be in a position to test offense information, aligning it with clearance data and, in the case of Crime 

Index offenses, observing in summary number fotm, the actions of the joint 

effort of courts and prosecution. He may be able to identify specific areas 

which have a debilitating effect on the caseload of prosecutors. This may 

suggest to the planner a need for expansion of the prosecutor's office, 

added training for prosecutors; it may, in effE!ct, identify a need at the 

law enforcement level, and it may suggest to the planner certain items of leg- islation to be recommended to the appropriate authority. 

6. Constructing Law Enforcement Agency Profiles--A table can be constructed utilizing three data sets from each individual law enforcement 

agency. By cm~rel ating law enforcement employee data with'the number of offenses reported, the volume being reduced to a rate, a percent change or 

trend can be constructed. The second portion of the table would reflect 

the number of arrests per offense and a ratio tabulated. The number of offense arrests can then be computed on the basis of per police employee. The 

third portion of the table deals with clearance information. The total number of clearances, correlated with the total number of offenses, correlated with the number of arrests, and further correlated with law enforcement employees completes the profile. By constructing such profiles over 

time, administrative observations can be made. (SeeF'i,gure 1.) 

AVAILABILITY OF DATA "(,." 

The planner's attention is drawn to the limitations of this program. Certain data sets are not available from certain,;~j"gencies. However, inquiry 

to the state agency responsible for state data collection or to the FBI will 

resolve any questions of data availability. If the planner is dealing with 

a limited number of agencies in close geographical proximity, direct contact 

with those agencies will, in most instances, provide a quick response to 

data requests. In the event more expansive coverage is required, or if the 

agency refers the planner to the FBI, he should correspond with the FBI's 

UCR Section, Washington, D.C. 20535, to determine what data is available. 

In many instances, the FBI will provide reported information, and for those 

agencies not actively partiCipating, an estimate is available. The FBI will 

withhold information if some question of completeness exists. Completeness and validity can be affected by a change in records systems, annexation or 

deannexation of geography and/or population, or some other phenomena that 

would cause a current crime count to deviate from a prior comparable period. 

Another source of UCR data is from state UCR Programs. Thirty-two 

states have such efforts and are listed in the annual publication, "Crime in 

the United States. 1I 

36 

---------------.~--.-----, 

AGENCY PROFILE 

City of 

--------------------- Sworn Officers ------ 197_ Population ------- Total Employees 

Crime Index 

Total 

Murder 

Rape 

Robbery 

l~ssauli.: 

Burglary 

Larceny 

Hotor VE.hicle 'l'heft 

Crime Index 

Total 

t1urder 

Rape 

R.obbery 

l,ssault 

Burglary 

Larceny 

Motor Vehicle Theft 

l'art II Arrests 

Crime Index Number 

Total 506 

Murder 6 

l~o.pe 14 

Robbery 65 

Assault 85 

Burglary 148 

Larceny 137 

Motor Vehicle"Theft 48 

OFFENSE TAB 

Number 

3,067 

6 

17 

/.23 

97 

1,377 

706 

638 

ARREST 'l'AB ----

Number 

635 

5 

15 

lOG 

84 

170 

195 

57 

5,181 

CLEARANCE TAB 

Per 

Offense 

.1649 

1. 0000 

. 8235 

.2914 

.8762 

.1074 

.1940 

.0752 

Per 

100,000 

2748.2 

5.3 

15.2 

199.8 

86.9 

1233;8 

632.6 

571. 6 

Per 

Off0nse ------ .2070 

.8333 

.8824 

.4753 

.8659 

.1234 

.2762 

.0893 

Per 

Arrest 

.796 

1. 20Cl 

.933 

.613 

1.011 

.870 

.702 

.842 

-----

,% Change 

1971-1972 

24.1-

20.0-

12.1-

30.6-

10.9-

24.8-

Per 

Officer 

1.702 

.013 

.040 

.284 

.225 

.455 

.522 

.152 

13.890 

Per 

Officer 

1. 3565 

.0160 

.0375 

.1742 

.2278 

.3967 

.3672 I 

.1286 

'---------"--__ J 

Figure 1. LAW ENFORCEMENT AGENCY PROFILE 

37 

'

'·'·'·'1··.'·

!~ 

:', ~ .. 

, 

-.\.'"'1 
... 

CHAPTER 4 

CRIME STATISTICS ANALYSIS IN MASSACHUSETTS 

John R. Pfeiffer 

INTRODUCTION 

Analysis of crime patterns a~d trends is gaining more attention as an 

important criminal justice planning tool. It is recognized as a prerequi- site to sound criminal justice planning at state and local levels of 

government. An increasing number of state and local criminal justice planning bodies are intensifying efforts to utilize data about crime more fully in 

their planning. Since analysis of crime statistics has been the'province of 

academicians until recently, guidelines for such analysis are not available 

in laymen's terms. The following discussion explains how criminal justice 

planning agencies can analyze crime patterns and trends in a way that is useful to planners and program developers. A model is offered that is based on 

that used by the Massachusetts State Planning Agency (SPA) but should 

be easily applicable to other states. 

The discussion is divided into three parts. The first part reviews 

major considerations in planning the crime report. The second part discusses the organization and content of the crime report in more detail. The 

final part reviews some mechanics of data analysis and points out some of the 

limitations involved in using Uniform Crime Report (UCR) statistics for policy analysis and planning. ' 

PLANNING THE REPORT 

The first step should be the development of a good plan for preparing the report. Careful planning of data to be presented, methods of data analysis, 

and report format greatly facilitate successful project completion. Extra 

time and effort spent planning can save the time and labor involved in 

redoing the data analysis or revising parts of the report. There are several important questions to be answered at this stage. We shall comment 

on each of them briefly. They are all somewhat interdependent, the anSI'ler 

to one being contingent on the answer to another. Consequently, their 

relative priority is not as important as having answers to all of them. 

What Do We Want To Include in the Report? 

Many organizations, including the FBI, present little more than tables 

of statistics in their reports. This approach is simpler and less time 

consuming than adding analysis and narrative description of the data. But 

even if you only provide statistical tables, you must decide w~at data to 

include and how the tables will be organized. Whether or not the report is to contain much narrative, some organizing scheme must be developed and 

limitations set on the topics covered. More about this later. 

39 

:

,.~:

\ 

',', "" 

, 

, 

:"
".'_~ .. 

 ~~, _~~ ... ~~._ ... __ ====._=_= __ =_::::;:i~:::;;;:'''':;;:'':;:;;:~'''''''':'''''==,"$"::;;;;'':;:;;'tl1C~~ ~=.=£t!JZ!!I(!!!!O!"""'---------.i.,------

How long the report should be is also a significant consideration when 

deciding what to put in it. Even a seemingly narrow topic like the geo- graphic distribution of reported offenses can easily fill 50 to 100 pages of text, charts, and tables. Given the potential readership's interests 

and attention span, shorter, efficiently presented reports might be better 

received and have more impact than a longer, more comprehensive but more 

forbidding volume. 

What Can Be Included in the Report? 

The data available obviously limit the contents of the report. The 

two most widely known data sources are the FBI'slannua1 Uniform Crime Reports 

and the 1970 U. S. Census volume for your state. A good report can be prepared from just these two sources where other data are not ac~essib1e. To 

go. beyond reported crimes and their demographic corre1ates--for example, 

to include data on the organization and activities of diffey'ent parts of 

the state criminal justice system--may be difficult presently in some areas. 

It may require asking other state and local officials fOl" 'Information, 

consulting other agencies' annual reports, or conducting c. formal survey. These efforts add to the time needed to complete the report, but can be 

very worthwhile. If the data are of poor quality or of limited utility to 

readers, it may be desirable to initiate methods for improving the data. 

The Sourcebook of Criminal Justice Statistics: 1973 and Expendityre 

and Employment Data for the Criminal Justice System: 1972-1973 contain data 

on criminal justice operations in all states and larger counties and cities. 2 

These volumes can be helpful in attempting more extensive data collection. 

How Will the Data Be Compiled and Analyzed? 

Two options are offered here. One can use paper and pencil and a hand 

calculator--or punchcards and a computer to do the analysis. The latter 

approach sometimes avoids arithmetic errors and allows for more complex and 

interesting analyses of the data. The number of cases (cities and towns) 

will be so few and the kinds of statistics generated will usually be so 

simple that computer costs would be quite small. 

How Much Staff Time Is Available for the Project? 

A conceptually simple report can take a surprising amount of time to 

complete. Original time estimqtes should probably be doubled to be realistic. For example, one person could reasonably take two to three months 

working full time to prepare a straightforward report on the geo~lr'aphic distribution of reported crime, including planning, preparation of charts 

and tables, writing the narrative, and editing. 

40 

• t>'. 

REPORT ORGANIZATION AND CONTENT 

This part ~eviews.the organization of the Massachusetts crime analysis 

report. E~sentlally, lt provides an annotated table of contents for one ty~e o~ cnme report. The :I~assachusetts crime report focused on the di s- trlbu~lon ~f r~por~ed UCR crime i~ the state and examined the relationship of c~lm~ dlstrlbutlon to some baS1C demographic indicators. Both the UCR stat~stlcs and the demo~~aphic statistics were available in nationally publl shed documents. .Itn s approach dea It only with the crime prob1 em as repor~ed ~y local pollee. It excluded other possibilities. In particu- 1a~, lt dld not cover the criminal justice system's reactions to reported 

cnme .. For example, it did not discuss rates of arrest, conviction or s~nt~nclng. Moreover j the report did not discuss the impact of cri~e on 

vlctlms or on the crimina! jus~ic~ system. For example, no mention was 

made of dollar 10ss.to crlme ~lctlms or ~f the costs of operating, police, ~our~s,.and correctl~ns.agencles. Nor dld the report compare victimization 

_tatlstlcs (a~other lndlcator of the actual incidence of crime) with the 

numbers of crlmes reported to police. 

. There are several reasons that these alternatives were foregone. 

Flrst, the o~her types of ~ata were either unavailable or difficult to obtain. 

Second, a brlefer report wlth one theme was likely to have more impact. 

Later reports could explore the other issues. Third time to prepare the 

report.was limited. Inclusion of only one or two of'the other topics might 

have glve~ undue emphasis to the crime problem as somehow the responsibility of a p~rtlcu1~r part of the criminal justice system. However, careful ~na1ysls.and lnterpretation of additional data will often yield better lnforma~lon for understanding crime and the criminal jllstice system comprehens lVe 1y. ' 

An area-specific rather than a crime-specific approach to organizing 

the repo~t was chose~. Th~t is, each section of the report was written abo~tcrlme trends elther ln the. stat~ as a wh?le or in particular parts ~f ~he state ~a~her than about a partlcu1ar crlme and its distribution. I~e ~rea-s~eclflc approach facilitated analysis of the total crime pr9b18m wlth~n a.91ven area and comp~ris~ns among diff~rent parts of the state, 

but lt dld not preclude examlnatlon of trends ln reporting of specific offenses. 

. Consequently, the report was organized as follows. There were two 

maJor p~rts. The ~irst part contained the analysis of state crime trends 

along wlth su~port~ng ~~b1~s a~d graphs. The second part contained a series 

of map~ of c~lme dlstrlbutlon ln the state and a listing of all communities ~or.whlch crlme data were available, along with their demographic characterlStlCS and crime statistics. 

Part One of the Report 

The introduction to Part One included a brief statement of the purpose ~f t~e report, suggested some of its possible uses, and describkd its organlzatlon. There was also a commentary on the weaknesses of the kinds of ~ata used in the analysis .. It is ex~reme1y i~portant that readers be clearly lnformed about limitations lnherent In the Unlform Crime Reports as measures 

41 

1 

. \ 

".~ .. 

,1 
of crime. Some of the limitations will be described in the final part of 

this paper. 

Next, the report presented an overview of trends in statewide crime 

rates in recent years, using data from several of the latest UCR volumes 

and compared the trends in the state with those in the nation. Comparisons 

with neighboring states or with states having similar socioeconomic characteristics also would have been appropriate here. 

After the revie\,1 of crime trends in the state as a whole, sections 

were presented describing crime patterns in each of the officially designated planning areas and sub-areas of the state. Each section started 

with a description of the demographic characteristics of the area. The 

total volume and rate of crime in specific areas relative to those in other 

areas and in the state as a whole also were discussed here. Then there 

were comparative descriptions of the patterns of each of the violent crimes 

and of each of the property crimes. When there were any large cities in 

the area, the section ended with a discussion of cities ' impact on crime 

statistics for the area. 

A section on correlational analysis of the relationships between crime 

rates and socioeconomic indicators for each town for which crime data were 

available was planned for the report. The section had to be dropped. The 

planned analyses were not undertaken when it became apparent that the statistical form of demographic variables was incompatible with the form in 

which the crime statistics existed. In this case a lesson was learned 

after the fact on how to accommodate data that contain both median statistics and percentages. The situation offered a lesson in the complexities of statistical analysis and interpretation, but the explanation goes beyond 

the intent of this section. The rea~er is referred to Robert Gordon's 

article. 3 

The final section of the first part of the report summarized findings about crime patterns in the state and outlined policy implications of the 

data. We pointed out that UCR statistics can only indicate where crime is 

being reported but not why. The data could indicate where in the state 

particular programming efforts probably should be focused but not what form 

those efforts should take. Consequently, the policy recommendations suggested 

particular parts of the state where programs to fight certain crimes should 

De concentrated. Additionally, specific recommendations were made for 

improvements in collection and analys·is of crime data in the state. 

Part Two of the Report 

The second major part of the report contained a series of maps of the 

state divided according to geographic planning areas. Individual planning are~s were then shaded in to indicate variations in the distribution of 

specific crimes and demographic factors. In addition, an alphabetical li~ting of all cities and towns for which crime data were available was 

included in the second part of the report. Each listing showed in which 

planning area the community was located, the number of crimes rep0rted, the 

crime rate for all offenses combined and for each individual type of offense, 

and the rank order of that community within its planning area and within the 

42 

i 

Ii' 

, 

state on each crime. Ranks were given for both the crime volume and the 

crime rate: For.example,.one.small resort town was ranked first in the 

state for 1ts cr1me rate but 1ts rank for volume of crime was much lower 

Similar statistics and ranks were listed for the demographic variables .. Figure 1 illustrates the listing format. 

CITY CRIMES Offense 

NUMBER Number reported AREA # RANKS (State Area Region)* REGION # 

RATE Offenses 100,000 population RANKS (State Area Region)* 

DEMOGRAPHIC DATA Demographic characteristic 

VALUE City value on that characteristic 

RANKS (State Area Region)* 

Figure 1. FORMAT FOR liSTING EACH COMMUNITY'S CRIME AND 

DEMOGRAPHIC CHARACTERISTICS WITH RANKS 

*City's rank for that offense or demographic characteristic in the state, 

area and region. 

DATA ~REPARATION AND LIMITATIONS 

Coding the Data 

Before computer analysis can begin, the data, for each city and town 

must be abstrated from the UCR listings from the U. S. Census volume and 

other sources, written on coding sheets, and keypunched onto cards. This 

effort requires development of a scheme for coding the data after the 

variables to be analyzed are chosen. Decisions must be made about where 

each variable is to be recorded on a punchcard; how many card columns wnl 

be allowed for each variable; and if more than one card is needed for each 

community, which card each variable is to be punched on. 

To keep track of all this and to make it easier to do the next year's data coding, it is very helpful to prepqre a codebook. A codebook is like ~n index. It tells which variables were coded and where they are located 

1n a set of data cards. Indication of the sources from which particular variables were obtained would also be worth including. A "Codebook for 

State Crime Analysis, 1975" could easily be written using a format 1 ike 

that in Figure 2 for each variable. 

43 

. 

,

"".' 
Card # 

Columns 

Card 1 

32-34 

35-36 

Variable 

Name 

Homicide Volume 

Homicide Rate 

Source 

1973 UCR, Table 75 

1973 UCR,plus 1970 

CensuS:-Table 10 

Figure 2. FORMAT FOR VARIABLE CODEBOOK WITH EXAMPLES 

After the tedious job of coding is completed, the analysis can begin. 

The statistics needed for the report probably will consist of simple crosstabulations or correlation coefficients. Programs for computing these may be available at a state computer center. If not, they are available at 

most colleges. Specialized programming assistance might be needed only if 

special printouts are planned for publication in the report. 

Limitations of UCR Data 

It was mentioned earlier that it is extremaly important that readers 

and users of the report be clearly informed about weaknesses in the data 

and about the kinds of interpretations that may be legitimately derived 

from the data. Some readers may not be experienced in the complex use of 

statistics. They should not be misled into thinking the data explain more 

than they actually do. 

The most serious interpretation proulem likely to arise--especially 

if crime report statistics are correlated with socjoeconomic or other 

indicators--is that readers may think such rl:ta show what or who causes 

crime. Everyone wants to know that, but the UCR data cannot tell us. UCR 

data show only which communities are reporting how much crime. Knowing the 

location of crime is valuable information in its O~'!l right but, by itself, 

it does not provide valid evidence on the causes of such crime. 

To illustrate: A positive correlation of reported crime with cities 

having a higher percentage of population older than 60 years of age simply 

shows that crime is more frequently reported in cities with a large propor- tion of older people. That correlation cannot be u~ed to conclude that old 

persons are more likely to be victims of crime or to commit crime or to . report crime, but it does suggest the usefulness of further data and analysls 

to test the two more reasonable hypotheses--higher victimization and reporting 

rates. There is no way to tell from the statistic itself why such cities j 

report more crime. Hypotheses can be developed about why the correlation ./ 

occurs but other data have to be investigated to find an answer. 

44 , 

f 

Another difficulty is in distinguishing crime rates from crime volume. 

For example, a ~mall town can have a higher rate of crimes reported per 

100,000 populatlon than a larger city, while the absolute volume of crimes r~porte~ can be m~ch greater in the big city. Another example: propor- t~onal lncreas~s ln the vol~me (absolute number) of crimes in a city over ~lme can be q~lte large, whlle proportional increases in the crime rate l~ the same ~lt~ ~re not ~ear~y as dramatic. The discrepancy occurs if a Cl~y grows sl~nlf~cantly l~ Slze during the time period. In such cases, 

crlme volume ls.llkely to lncrease much more than crime rate. Both rate a~d volum: are lmportant and should be considered together in analyzing a CltylS crlme problem. 

. .Even when reade~s.do interpret the findings properly, they should keep ~n mlnd sev~ral qualltles of the UCR data themselves that may aff~ct their lnterpretatlon. 

NOTES 

• The FBI publishes only crime data for individual cities and towns 

with populations of 10,000 or more, and even in these cities, 

data are only presented for the more "serious" (as defined by the 

FBI) crimes of murder, rape, robbery, aggravated assault, larceny, burglary, and auto theft. Consequently, it is difficult to use 

UCR data to analyze crime trends in small towns and to analyze pattern~ of other (non-UCR) offenses that may constitute a large proportlon of the crime reported in the state. The FBI does collect data from smaller towns but does not publish them. The FBI 

does, however, include the small town statistics for serious' offenses in its calculation of the overall stat~ crime rates. 

• Not all communities do presently participate in the UCR program. 

• The ~BI data ~nc~ude only offenses reported to the police. Some 

studles have lndlcated that the actual amount of crime is greater than that reported to the police in any locality. 

lClarence M. Kelley, Director, FBI, UNIFORM CRIME RE~ORTS for the United 

States. (Washington, D. C.: U. S. Government Printing Office, Printed 

annually); U. S. Bureau of the Census, "Characteristics of the Population, 

Part (Number) (State)," Census of Population: 1970, Vol. 1 (Washington, D. C.: U. S. Government Printing Office, 1975). 

2Michael J. Hindelang, et al., Sourcebook of Crjminal Justice Statistics 

J_973. (Washington, D. C.: U. S. Government Printing Office, August 1973); 

U. S. Law Enforcement Assistance Administration and U. S. Bureau of the 

Census, Ex enditure and Em 10 ent Data for the Criminal Just; e S stem: 

J972-73. Washington, D. C.: U. S. Government Printing Office, 1975 . 

3~or~on, ~obert A., "Issues in the Ecological Study of Delinquency," American 

SOclologlCal Review, XXXIJ., (December 1967), pp. 927-944. 

45 
== 

\ 

( 

, 

CHAPTER 5 

UCR IN CRIME SPECIFIC RESEARCH AND PLANNING 

David L. Dougherty 

I NTRODUCTI ON 

The usefulness of Uniform Crime Reports is extensive for law enforcement agenci.es. Information prcv-ided by the Repoit c·an be applied to Q'ri::QS 

of statistics and hard data, as well as to aspects of planning and research. 

This chapter provides examples of using UCR data to help develop crime 

patterns and trends that are both necessary and useful for planning and 

resea rc h needs. 

THE USEFULNESS OF UNIFORM CRIME REPORTS 

The Uniform Crime Reports have utility for crime specific research 

and planning as (1) indicators of specific crime trends calling for further 

investigation, and (2) data to begin more detailed study. 

HOMICIDE AS AN EXAMPLE 

An example of a study which might rely heavily on'~CR data is an 

analysis of trends in homicide. Homicides are considered among the most 

reliably reported crimes in the UCR Program and additional information 

regarding homicides reported is provided in the attachment to the reports prepared by local law enforcemen~ agencies. 

The attachment to a Uniform Crime Report may have less reliability in 

that it'is prepared soon after the crime occurs, usually prior to the 

completion of investigation of the case; and, although in some cases it is 

not completed for all reported homicides, enough information is generally provided to direct the researcher toward trends regarding weapons used 

and other factors. When used in volume, trends both over time and across 

jurisdictions can be determined. 

The UCR reporting form itself is highly reliable with respect to 

homicide reporting. A simple count of increases or decreases over time 

and across jurisdictions can easily provide trend data and pOint to the 

need for programs to respond to changing trends or for further investigation of factors related to those trends. 

The UCR Report and the attachment are especially useful in conjunction with arrest reports or rap sheets to provide an in-depth analysis of trends. 

When these three sources are available, the simple count of increases or 

decreases may indicate a period or (if compared across jurisdiction~) a 

location in which homicide is increasing. This would direct the researcher 

to further analysis of the attachments related to that finding which may 

provide information on such changing factors as the types of weapons used, 

47 
the location 8!.nd time of the crimes, and, in ~ome cas~s, the relationship 

of the victim to the alleged offender. That lnformatlon can be further t' 

ex anded by analysis of arrest reports and rap sheets for at least a ~or 10n ofPthe~homicides of interest. The arrest reports and ra~ sheets provlde 

additional information about the circumstance~ of the,crlmes under st~dy and rna show patterns in the nature of the crlm~s,or ln the o!fenders backgr~Unds amenable to control through new pollcles ~r prabctlcdes of , f the criminal justice system. Recommendatlons ase on a %~~~~~~~ ~tudY of trends, factors, and patter~s discoverer: f~om ~~R reports 

and othe~ sources migh~_~~:l~~e~~~:~:~~:~ P7~!~:~~:~~~1~f'~tr~~~e~~~~- and at tlmes where hOmil-lul: I=> 1I1\.I<;;U;)'''!j' , ... ..,,., ...... :- .. J, ' "'f h "des 

stran er homicides are involved), more intensive 1nv~stlgat1on 0 omlCl 

at th~ scene and by the coroner, alternative sentenc1ng,for offe~d~~s whose crime patterns are similar to those who have comm1tted hom1c1 es, 

or new controls on weapons. 

UCR AND PART II, CRIMES 

A more difficult but still valuable use of the UCR prog~am for crl~e s ecific research is for Part II crimes such as drug use. ~lnce Part O~fense reports are generally based on arrests madeh

, not crl~eStr~~~r!~~~rce_ research with respect to those offenses tends tos ow more ,a ou 

ment activity than actual offense c~nditio~s in th; communlty. Nonetheless, 

trends can be shown in relation to lncreas1ng arre~ts. 

Arrest reports and rap sheets can be especiallY,useful in regar~ to 

Part II offense patterns. Much as Part I reports pOlnt to trend~ an 

patterns call ing for further investigation, Part II re~or~s ,prov1de 1 ' 

similar indicators and when such t~ends appear to be,Slgnlflcant, ~n~ YS1S 

of a rrest reports and rap sheets can show factors Wh1Ch may accoun.; or 

changes and suggest activities to respond to the changes. S~chkth,ng~ as 

circumstances of the crimes (from ar~est r{efPcrts), O~f~~~)rSh~~ ~~~~~r~s and patterns of arrests for other crlmes rom rap ~ e ~ 

requiring new responses from criminal justice agenc1es. 

It should ~e noted that, due to the nature of the re~or~in~, ~~rt II 

data ~s much less reliably compared over time and across JUrlsdlct~?nS' f 

therefore such comparisons should be made only with,full understa~ l~g 0 

po~s;b'le changes in reporting ~nd enforcement practlces over the penod or 

between the jurisdictions studled. 

48 

I ;" 

, 'h 

i 

• 

CHAPTER 6 

PREDICTING CRIME INCIDENCE AND DETERMINING CHANGE 

Thomas A. Giacinti 

INTRODUCTION 

If planned improvements are to be incorporated in the criminal justice 

system, there is a need to estimate the current extent of crime as well as 

to forecast future crime rates and apply these estimations and forecasts to 

a determination of whether or not the actions have had an effect on the 

rate of crime. The utility of quantitative methods for estimating. and forecasting crime incidence and for interpreting observed changes in crime patterns is becoming increaSingly apparent. 

There are both simple and complex statistical methods that can be employed to predict crime incidence on the basis of previous ~nnual crime statistics. These methods are associated with predicting crime as a function 

of a straight line extrapolation. This chapter describes methods for performing both visual preliminary estimations and computations of crime rate 

statistics and forecasts. 

LINEAR PREDICTION METHODS 

A Visual Estimating Method 

The albegraic formula for linear regression (V = a + bX) permits one to 

determine "V" (the incidence of crime) from the relative slope of a line "h" 

or by a technique called the "lea'st squares" method. The simple preliminary 

method of doing this involves prepar1ng a graphic presentation of the annual 

frequencies of crime and a "visual estimation" or projection of future crime 

frequency. This is done by plotting annual frequencies of crime on a r,alibra ted graph, linking the points together to show the historical trend ~f 

crime, and then plotting an estimated average increase (or decrease) trend 

line which extends through the points indicating past annual crime frequen- cies to a future year. . 

Customarily, such a graphic display of data is presented utilii~ing X 

and V coordinate axes, with the V axis being a vertical line on the left, depicting frequency intervals of crime, while the X axis is the graph1s horizontal base (abscissa) depicting elapsed time in days, weeks, months, or 

years. 

In Example A (Figure l), the V axis denotes the frequency of 'rape in in- cr~ments of 15, going up from 0 to 600. The X axis depicts the years for 

WhlCh crime data is available, in this case 1966 through 1974 with the year 

1975 added as the year for which a prediction of crime frequency is desired. 

The steps to construct this graph are few and uncomplicated. 

First, draw the X and V axis for the graph and determine w'nich units of 

measure will be most appropriate for recording the frequency of crime. In 

49 
w 

a.. 

« 

a: 

,u.. 

'0 

G 

z 

w 

:::> 

a 

w 

a: 

u.. 

.....J 

« 

:::> 

z 

z 

« 

525 

(474) 

450 

(563)-IJ 

(500)1~Range I '"1 of the 

,..,,'" '" Estimate 

__ --JL--3'V"""''--- (437)-

300-

225 

150 (163)-- " ' 

Actual Incidence 

......... of Reported Rape 

Predicted Incidence 

--- of Rape Offenses 

L __ .2X~A~X~IS~ __________________ ~~--~~-:~--~:-~ 

o 1972 1973 1974 1975 19661967 1968 1969 1970 1971 

Figure ~".'~~~~;;~~-'~A'" PREDICTED YEARLY INCIDENCE OF RAPE 

AS THE !=UNCTION OF A STRAIGHT LINE 

50 ,', 

• 

Example A, the units of measure were 15 crime~ on the Y axis and 1 year on 

the X axis. Generally speaking, the smaller the units of measure are the 

more accurate the graph will be (in the example the emphasis was on predic- ting the yearly incidence of crime, so there was no need to present time in 

smaller units). The next step is to enter a coordinate point within the 

axes (X and Y coordinate values) which depicts the frequency of crime for 

each year. 

In Example A, the year 1966 resulted in a frequency of 163 reported 

rapes. The use of calibrated graph paper makes this exercise accurate with 

minimal effort. After entering a point on the graph depicting the frequency of crime for each year, then link all the points with a solid line. 

Now the graph portrays a historical trend line of criminal events over time. 

To IIvisually estimate ll the predicted frequency of crime for the next 

year and into the future, another line must be added to the graph. It will 

be called the "regression 1inell (broken line in Example A). To construct 

this regression line place a straight edge or ruler over the trend line 

which has already been drawn so that one half of all the data points are 

above the ruler and one half of the data points are below it. Now, simply draw a straight line through the existing historical trend line and extend 

the line one year into the future so that the line describes what appears to 

the eye to be the angle or slope that the connected data points follow. The 

point at which the regression line crosses the next year's location on the 

graph will be an estimate of the frequency of crime for that year. 

In Example A, a regression line (broken line) has been drawn through 

the historical trend line separating the yearly freque,ncy points so that 

four points are below the line and five are above it. The regression line 

has been extended through 1975. The point at which it crosses 1975 is the 

500 frequency level on the calibrated scale, and this represents the estimated frequency of rape for 1975 using the simplistic model or II visua1 estimate" method. This technique is' meant to provide only a very rough indication of what one might expect to find. Although an efficient procedure, reliance should not be placed on it when estimates are needed for reporting 

purposes. 

One should assume a IIrange of to1erance" around this 1975 predicted 

frequency of 500 approximately equal to the average differences of the annual frequencies for the years plotted, as discussed below. This method is 

also only a rough approximation for preliminary u'se in arriving at a "ballparkll estimate. 

The average difference is calculated by taking each year's frequency 

of crime and subtracting from it the previous year's frequency; summing the 

differences; and dividing by the number of years of data for which the differences were calculated as follows: 

51 

;~,. 
Year Freguency Difference 

no 

(1966 ) 163 previous = not applicable 

data 

1- (1967) 224 163 :::: 61 

2. (1968 ) 304 224 = 80 

3. (1969 ) 330 304 = 26 

4. P970) 474 - 330 :::: 144 

5. 1971 ) 434 - 474* :::: 40 

6. (1972 ) 368 - 434* :::: 66 "7 

I • (1973) 461 368 93 :::: 

8. (1974) 403 - 461* :::: 58 

568 ~ 8 = 71 

In determining the differences* between the frequencies, an absolute difference is calculated regardless of whether it was an increase or a decrease. 

In other words, ignore the plus or minus signs of the difference when calculating the average yearly difference. The exercise above reveals an average change in the frequency of rape from 1966 to 1974 of 71 events, so 

the "range of tolerance" for estimating the frequency in the next year 

(1975) is 500 + 71, or 500 - 71. The range for the visual estimate of rape 

in 1975 as portrayed in Example A would be 429 to 571. An actual frequency of rape which occurred in 1975 and was above 571 or below 429 would more 

than likely be a significant increase or decrease. 

~ebraic Method 

Because its accuracy is dependent upon a fair1y consistent linear 

trend with small fluctuations from year to year, and because this visual 

estimating method is only a preliminary technique, a more accurate estimate 

of the frequency and the range of the predicted frequency should use the 

equation for l'inear regression and a formula for determining !;onfidence 

intervals using the standard error of the estimate. The formula for predicting the freque.ncy of crime· is: 

Y = a + bX 

To actually arrive at tr.e prediction, one must use the computational formula. In the following example we will compute the coefficients "a" and 

Ilbll for the descriptive formula above using the data from Example A. To develop the coefficients, it is easiest to compute lib" first because it can be 

used to compute the coefficient !la ." The computational fonnula for "b" is: 

NEXY - (EX) (EY) 

b = --..,,---,...-

NEX2 _ (L:X)2 

and the computational fonnula for Ita" is: 

a = 

EY - bEX 

N 

52 

-!, 

n 

.The data we have available from Example A is: , 

X {is ordinal value of the year) y (is yearly frequency of rape) 

1966 = 1 163 1967 = 2 224 1968 = 3 304 1969 0: 4 330 1970 = 5 474 1971 = 6 434 1972 = 7 368 1973 = B 461 1974 = 9 403 

Ordinal values have been assigned to the years 1966 through 1974 Th 

values are 1 th~oug~ 9 and they are X values. For the Y values the a~~~al frequency of cr1m~ 1n each of the X years is used. As indicated above in 

1966 the X value,ls 1 an~ the Y value is 163 rapes. The only other vaiue 

we nehed to k~OW,lS N, WhlCh is the total number of years which will be used 

in t e compu~atlon. In Example A, the N is 9 years. 

To compute "b" . th NL:XY - (LX) (L:Y) uSlng e formula b = , NL:XY is , NEX2 - (EX)2 flrst part o! the,formula to be computed. To do this, first multi 1 

value of X tlmes lts corresponding value of Y as in the follow;ng: P y 

x v XV 

1 x 163 = 163 

2 x 224 = 448 

3 x 304 = 912 

4 x 330 = 1320 

5 x '474 = 2370 

6 x 434 = 2604 

7 x 368 = 2576 

8 x 461 = 3688 

9 x 403 = 3627 

EXY = 17 ,708 

the 

each 

Al~ these values must be summed, which is what the symbol sigma (E) means T(~lSXye~UalS 17,708. Th~s number must then be multiplied by N which is g' E - 9 x 17,708), WhlCh equals 159,372. . , . 

1 The next c~lcu1ation will be to solve the right hand side of the formu a. To do thlS, sum (E) all the X scores and sum (z) all the V scores, 

53 

.~----------------------... ~--
x V 

1 163 

2 224 

3 304 

4 330 

5 474 

6 434 

7 368 

8 461 

9 403 

(I:X) = 45 (I:V) = 3161 

and multiply these two numbers together, that is, (I:X) (I:V) = 45 x 3,161 = 

142,245. 

Now the bottom half of the formula for "b" will be computed. MultiiJly 

each of the X scores times itself (square them) and sum (I:) the squared 

values, and multiply them by N, which is 9. 

X X2 

1 x 1 = 1 

2 x 2 = 4 

3 x 3 = 9 

4 x 4 = 16 

5 x 5 = 25 

6 x 6 = 36 

7 x 7 = 49 

8 x 8 = 64 

9 x 9 = 81 

EX2 == 285 

NEX2 = 9 x 285 = 2,565 

To solve the bottom right hand side of the equation, take the previously 

computed sum of the XiS (I:X), which was 45, and square it: 

(EX)2 = 45 x 45 = 2,025 

Now all the component parts of the formula to calculate the "b" coefficient 

have been computed. 

NEXV = 159,372. 

(LX) (EV) = 142,245 

N2:X2 = 2,565 

(t:X)2 = 2,025 

54 

These values merely need to be plugged into the'formula to determine "b. 11 

b = ~XV - (I:X) (rV) 

NI:X2 - (I:X)2 

b = 159,372 - 142,245 

2,565 - 2,025 

b = 17,127 

540 

b = 31. 7 

The "b" coefficient (31.7) can now be used to compute the "a" coefficient in 

the formul a: 

a = I:Y - bI:X 

N 

All the component parts of this equation have been computed when the "b" formula was calculated. It is known that: 

LY = 3,161 

I:X = 45 

N = 9 

b = 31.7 

All that need be done is to plug these numbers into the equation for "a ll 

a = ~l§.L.:._ (31. 7 x N 

9 

a = 

a = 

a = 

3,161 - 1,426.5 

9 

1,734.5 

9 

192.7 

w~ n?w have the value of the coefficients "a" and "b" to enter into our predlct10n formula V = a + bX. To predict the 1975 incidence of rape from Ex- amp~e A, enter the values for "a" and "b" in the equation and enter tile 

ord1nal value for the predicted year. In Example A, the ordinal va1ue for 1~74 was 9 so the next year, 1975, will have the ordinal value of 10. Plug- gln9 the values into the linear prediction formula we have 

Y = 192.7 + (31.7 x 10) 

V = 192.7 + 317 

Y = 509.7 

so that the 197~ predicted frequency of rape using the data from Example A 

and the algebra1c formula for linear regression will be 509.7 or 510 reported crimes. 

55 

';i , 

.:j 

.; 

.' .; 

I 

t-

.,J 
Because this predicted frequenc~ of rape.(509.7) is an ~xtrapol~tion 

beyond the data on which the predictlon equat10~ was based, 1t also 1S a 

rather rough estimate. However, one may detenTllne the standard error of 

the estimate and use that information to build a rough accuracy range around the predicted crime frequency. ~ formula available in most social 

science statistics texts dealing with linear regression may be adapted for 

this purpose. It involves using the predicted value of Y (frequency of 

rape 'in 1975) and creating an interval of interest using. t.he Ilt" score for 

the desired level of confidence (two-tailed) and the est1mated standard 

error of the estimate. The interval around the, predicted frequency of the 

crime of rape in 1975 would be from 601:2 to 418.2.* Any ac~ual frequency which falls above or belo~' this level m1ght represent a mean1ngful change 

frulII the anticipated year'ly occurrence of the crime. As can be seen, the 

range generated using the algebraic formula is close to the range generated 

by the simple visual method previously mentioned. 

• Range for Simplistic Method = 571 to 429 Estimated Frequency = 500 

I Range for Algebraic Formula = 601.2 to 418.2 Estimated Frequency = 

509.7 

Given access to a calculator, the statistical formula can be used to 

derive the estimated frequency of crime. However, one can be fairly accurate using the simplistic visual graphic procedure. 

ADJUSTMENTS OF DATA FOR POPULATION SIZE 

Because actual frequencies of crime are somewhat mislead~ng,.in.using a 

linear prediction model (either the mathematical formula 0\ slmpllstlc ~eth­ od), it is advisable to adjust the UCR crime.data. Depepd1ng on the crl~e category, one can adjust the data to populat10n total, number of automoblles, 

number of women, number of residences, etc. 

In the example used it would have been more ap~ropriate to p\edict a 

rate of rape by using the yearly UCR data on the c\lrlle as a functlon of the 

population of women in the jurisdiction each year ln the form of a yearly 

rate of rape per 100,000 women. This would be a more accurate \epresentation of this crime problem and would.a1so probabl~ aff~ct the sTze.of t~e predicted Urange" by reducing it. Slm,larly, a s1tuatl0n could arlse wlth 

the crime of residential burglary where, for example, the frequency.of t~e crime would rise from 15,000 per year to 19,000 per year. In any s1tuatlon 

an increase of 4,000 residential burglaries from one ~ear to the next would 

represent a serious increase in the crime. However, ~f the.frequen:y of 

the crime is adjusted to a base of the number of houslng unlts and lt can 

be determined that the number of housing units increased, for example, from 

75 000 to 100 000 from one year to the next, the rate of residential burg- la;y per 100,000 housing units would be 20,000 for thl~ first year and 19,000 

for the second year. 

'~--'-··*Theactual calculation for the interval (or accuracy range) appears 

in the notes to this article. The confidence level selected was the .10, 

or 90~, confidence level. 

56 

i'l 

LIMITATIONS OF THE LINEAR MODEL 

One of the limitations of the linear prediction model is that the fUrther the yearly frequencies (or rates) ~r0 from the prediction line, the 

poorer attempts at extrapolation will be. In other words, the greater the 

deviations about the regression line are, the greater will be the error or. 

the e~timate .. In Example B (Figure 2), one can see graphic representation of thlS: In F1gure 2, the ac:Ut'acy range for the predicted burglary fre·,. 

quency 1S small because the dlstance between the yearly frequencies and 

the regression line is smail (i.e., the yearly increases or decreases in 

the crime do not uiffer that much from the prediction line). In Figure 3, 

the range of interest for the predicted robbery frequency is great because 

the distance from each yearly frequency to the regression line is large. 

This also holds true using the algebraic formula for least squares method 

of predicting. 

Another limitation of the linear prediction model is that interpola- tion (i.e., making estimates within the range of data already available) is 

necessarily more accurate than extrapolation (i.e., going beyond the range 

of data previously examined). Moreover, the further one attempts to predict into the future (extrapolate), the greater will be the potential for 

error. This has serious implications for the estimating procedures used. 

For example, in the algebraic regression approach outlined above, the 

standard error of estimate deals with int~rpolated data; accordingly, it 

will always underestimate the error of measurement associated with extrapo- lation. For this reason the regression approach outlined in this chapter 

should be viewed as a Ilrule-of-thumbil rather than a rigorous statistical 

procedure. 

An additional limitation of the linear methods presented here is that 

they do not account for a directional ~hange in the trend line. If, for 

example, as in the case of the rate of automobile thefts, the frequencies 

stop increasing each year and start decreasing, the utility of either predictor model is temporarily limited. Either method is designed for predic- ting the straight line function of the occurrence of some event. If, because of some drastic change like the advent of certain successful crime 

reduction measures (as in the case of the automobile with the innovations 

of anti-theft devices in the design of the new vehicles), the yearly incidence of crime begins to steadily decrease from year to year, then the 

1 i near predi cti on method becomes temporarily 1 ess useful. 

If the direction of the line changes, as in Example C (Figure 4), a 

new starting point must be selected for predicting crime incidence. In the 

example, the new starting point is the most recent highest frequency year, 1971. The frequency of crime in this year was sel~cted as the beginning 

point for the computation of the regression (predictor) line. It should be 

noted that it takes at least three data points (years in this case) showing 

a directional change to begin to plot a predictor line using either the simplistic method or mathematical formula. In any situation using linear procedure, the more data points there are, the greater the accuracy of the 

prediction will be, assuming that the basic relationship betwe~n predicted 

and predictor variables remains the same. 

One question that arises in using the linear prediction methods described is the utility of the models in determining change over time if the 

57 

J 
: \ 

>-

c:c 

loll 

m 

m 

o 

0::: 

Cl 

UI 

Ic:c 

2 

UI 

c:c 

lL 

() 

uoo 

HOC) 

700 

GOO 

>, 400 

l! 

'2 

w 

:.., 

C1 ~100 lU 

0::: 

LL 

200 

Figure 2. RANGE OF ACCURACY INTERVAL FOR PREDICTED 

BURGLARY FREQUENCY 

/ 

100 ~~------------------------------------------------------------ 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 .. 

Figure 3. RANGE OF ACCURACY INTERVAL FOR PREDICTED 

ROBBERY FREQUENCY 

58 

2000r---------------------------~------~t---------------~ 

FORMER 

1800 I--________________ ~~---PREDICTION I LINE ----l 

1600j-------------------------~~-------.~~~---------J 

1400.---------------~------------~--~a----J 

t NEW w PREDICTION 

~ 1200j-_________ ~~~=-----~L~I~N~E-~------------J 

f-

;:) 

« 

~ 1000t----------------j~----------------------------------------J 

>-

u 

z 

w a 800J------~~-----------.------------------~---J w 

0:: 

u.. 

600j------1~----------------------------------------~ 

400.-~r-------------~---------------------------------J 

~00r-----------------------------------------------------~ 

o ~ 

1966 1967 1968 1969 1970 1971 1972 1973 V/4 1975 

YEAR 

Figure 4. EXAMPLE "C", FREQUENCY OF AUTO THEFT 

59 
intervals of interest around the predicted values are large. Certainly, the utility of the prediction seems limited when the range of interest is 

us large as 183 (601.2 to 418.2), as was developed using the algebraic formula and the data from Example A. In this case, it should be noted that 

the lowest estimate of the interval of interest (418.2) is only 15.2 

greater than the previous year1s frequency of the crime (403). Anyoccurrence that would be equal to or less than the previous yearls frequency of 

the crime would represent a drop in the crime which would be of interest. 

Wide fluctuations of the reported frequencies over the years make the incidence of the crime less predictable. Adjusting the frequencies to a population of potential victims or targets may help to lower the degree of 

yearly crime fluctuations. 

QIJALIFICATIONS OF THE MODELS 

There are other mathematical formulae for accounting for directional 

changes or curvilinear relationships for prediction purposes using multiple 

regression techniques and second degree algebraic equations. However, because of the complexities of these models they do not warrant coverage here. 

One must assume that all yearly reporting of crime frequencies is subject to influence by extraneous variables. Such variables could include 

changes to statutory definitions of crime, changes in police reporting procedures, and extreme changes in population demographics or the environment 

of the jurisdiction. These extraneous variables are not usually anticipated 

in predicting crime incidence. Fortunately, however, in many instances one 

can go back and adjust the data by accounting for the proportional change incurred by the intrusion of the extraneous variable. A case in point would 

be the change in the definition of the UCR.category of larceny. Prior to 

1972, the UCR reports of larceny separated reported crimes where the loss 

was $50 and over. In recent years, the two categories have been combined to 

include all larcenies in one category. To adjust previous years I data for 

pred'iction of ensuing years, it is necessary either to add the frequencies 

of larcenies under $50 for each of the previous years or increase the pre- vious years I frequencies of larceny by the proportion of total larceny accounted for in the most recent year1s incidence of the crime by larcenies of 

under $50. 

In summary, to adjust crime data for the influence of the extraneous 

variable, first be aware of ~he presence of the extraneous variable and 

second, account for the proportion of the crime incidence influenced by the 

extraneous variable. No one will be able to accomplish both of these actions 

in all situations. In any event, any statement about yearly dramatic changes 

in crime incidence should be preceded by a vigorous investigation of the possible intrusions and effects of an extraneous variable. 

NOTES 

The fOY11lula for computing a confidence interval around a predicted 

score using the linear prediction model makes use of the predicted score 

plus or minus the standard error of the estimate of the predicted Y scores 

at each X point which has been multiplied by the t score for the desired 

ronfidence interval, or 

Yp ± 8Yp x t . 

In Example A we computed Yp which was 509.7, 

error of Yp we use the formula 

To calculate the standard 

8Yp = gy/X 

To solve for 8Y/X use the formula 

N 

1 + (x - X)2 

N N 

L(X.-x)2 1 

i=l 

N 

8Y/X = L (y._y)2 

1 

-b l:(X.-X) 1 (Y . -V) 

1 

i = 1 ;=1 

N -2 

Using the data in Example A to compute the confidence interval (prediction 

range) around the predicted score at the .10 confidence level would involve 

the following computations: 

1. solving the fonnula for 8Y/X 

N 

(Yi - V) 

L (Yi) - V)2 

;=1 , 63 351 = -188 35,344 

224 - 351 = -127 16,129 

304 - 351 = - 47 2,209 

330 - 351 = - 21 441 

474 - 351. = 123 1 5 ,1 29 

434 - 351 = 83 6,889 

366 - 351 = 17 289 

461 - 351 = 110 12,100 

403 - 351 = 52 2,704 

3,161 91 ,234 

V = 3,161 

9 = 351 

60 

~ _____________________________ .I. ...... _______________ 6_l __________________ _ 
N 

j; (Xi - X) 

;=1 

1 5 = 

2 - 5 ::: 

3 - 5 = 

4 - 5 ::: 

5 - 5 ::: 

6 - 5 = 

7 - 5 ::: 

8 - 5 :: 

9 - 5 ::: 

4! 

X 45 =-9 

N N 

(Yi-Y) I; (X i

=X)2 z: (X.-X) , 

;=1 

-4 

-3 

-2 

-1 

0 

, 

2 

3 

4 

i = 1 

16 -4x - 188 = 752 

9 -3x - 127 = 381 

94 4 -2x - 47 = 

1 -1 x - 21 = 21 

1 Ox 123 = 0 

1x 83 = 83 

2x 17 = 34 

3x 110 :: 330 

4x 52 = 208 

bO 1903 

8Y/X ::: 

N . 2 N - z: ( Y -Y) - b z: (X. - X) (Y i - Y) 

1= . 1 ' 

N - 2 

oY/X = I =-9~1,~2:::.-34.!--..-.;;3;-:-1~. 7~x --'.'-<-' 9_0_3 -i 9-2 

~Y/X =~ 91,234 - 60,325 

7 

6Y / X = .~ 4,41 5 

'f,Y/X:: 66.4 

This value is used in the equation for the ~tandard error for the predicted Y (1975 rape frequency) 

.... I 1 oYp = oY/X + {N_X}2 

~ N N 

z: (X i-X)2 

i =1 

.... J 1 (10 _ 5}2 oYp = 66.4 9 + 60 

tYp = 66.4 .~ .71 + .42 

.... 

oYp = 66.4 ~ .33 

tVp = 66.4 x .728 

8Yp = 48.3 

The prediction range or confidence interval is then detl~rmined by multiplying 

the t score for (N-2) degrees of freedom by the standard error of the predicted score and adding and subtracting this from the pl"edicted value of Y. 

In the computation for Example A, the 90% confidence level was used for t 

with 7 degrees of freedom (two-tailed test) so that the confidence interval 

for the predicted 1975 rapes is 

509.7 ± 1.895 x 48.3 

= 509.7 ± 91.5 

The prediction range or confidence interval equals 

601.2 to 418.2. 

REFERENCES 

Blalock, Herbert M., Jr. Social Statistics, 3d. ed., McGraw-Hill Company, 

no date. 

63 

_________ 6_2 ____________________ .... 1 ........ ___________________________ -------
/f 

\ 

INTRODUCTION 

CHAPTER 7 

A PLANNING-ORIENTED MEASURE 

OF CRIME AND DELINQUENCY* 

Burt Nanus and Luther Perry 

A great deal of money is currently being spent on problems of crime 

and delinquency in our society. Some of the money is allocated to programs 

designed to improve the general system of criminal justice, but most is 

earmarked for ~pecific policies or programs aimed at dealing with partic- ular problem areas. In order to determine the ultimate impact of these 

programs on the overall level of crime and delinquency, one must first 

have a suitable and generally acceptable yardstick or measure, or as they have recently come to be known,'social indicators,' The purpose of this 

note is to propose a new measure that would be useful both for analysis and for guiding planning on current and future crime programs in a given regi on. 

THE PROBLEM 

Very likely at present the best, most widely used~ and most readily 

accessible social indicator of crime and delinquency is the Uniform Crime 

Report (UCR) issued by the F.B.I. since the early 1930's. Its principal 

value is tnat it has existed for about four decades, is familiar to police 

officers and the public, and offers at least a broad overview of the general 

direction of the national crim~ level. However, there are many well-known 

deficiencies of the UCR, including its degree of aggregation, its inability 

to discriminate between various degrees of victimization and differences in 

reporting approaches over time by various jurisdictions. 

Thus, the UCR, while widely quoted, is of limited use as a social indicator of crime activity. Other measures have been proposed including victim 

surveys, recidivism rates, self-reported crime studies, the 'fear ind~x' of 

key groups such as shopkeepers, taxi drivers and police. All these measures 

have major shortcomings, not the least of which is cost and the absence of 

an historical data base for trend analysis. 

Thus, it is apparent that there is a need for new crime measures in- clud~ng the following properties: 

1. The measures should give a true reflection of the tre,nd over time 

of the most serious crimes in a given geographic area. Serious 

crimes are important because they cause the most harm in terms 

*Reprinted with the permission of the Journal of Criminal Justice, I, No.3 

(Fall 1973), pp. 259-263. 

65 

',I 

, ! 
of human suffering and contribute the most to a sense of fear 

and insecurity in the populace. They are also the most likely 

to be repo~·ted. 

2. The measures should be capable of being disaggregated as needed, 

doy," to very small: geographical and time units so as to be useful for detailed analysis as well as projections. In addition, 

it should be possible to construct historical indices on the 

same basis for planning and der:ision-making purposes. 3. The measures should be derived from sources as close to the events 

as possible. The closest official source is the police since they 

receive the first report of a crime. There are drawbacks in using only crimes known to the police--variations in reporting rates 

among types of crimes possible, inaccuracies, etc.--but compared 

to other data, many police records are reasonably complete and 

historically uniform. Some records are also available in an 

autoolated or semi -automated fashion. 

4. The measures should include consideration of the social costs or 

impacts of the crime, including costs to the victims and to 

society. This implies a need to weight crime for its seriousness. 

One measure that appears to meet some of these criteria was developed 

by Sellin and Wolfgang and published in a lClndmark book called The Measure-

!!1..!irt of Del i nquency.l These researchers di sl2(Jvered that there was a great deal of agreement among university students, police officers, judges and 

the general population in Philadelphia on the relative seriousness of 

different types of crimes. 

Having established that it is possible to weight the seriousn~ss of 

different crimes with consistency, they were then in a position to create 

a crime index. ro calculate the index, one must first calculate a"seriousness weighting' for each criminal levent' that took place in the time 

period of interest. Rather than use the legal label for an offense, the 

Sellin-Wolfgang Index ex~mines the offense itself for all the serious constituents of the act and assigns a weighting to each. 

The Index is compil ed by $unmi n9 a 11 the wei ghted events that occurred 

in a given geographic region during a specified period of time. In addition, 

various ratios can be calculated such as the average number of seriousness 

units per 10,000 population or per 10,000 juveniles, or the average number 

of seriousness units per event or per offender. 

These mea.sures appear to be very meaningful for evaluation of activities 

in small geog~~aphic areas or neighborhoods, particularly if they can be calculated monthly ave." a period of time. To explore further, a prototype 

project was undertaken in Los Angeles using a variation of the SellinWQlfgang Index. 

THE PROTOTYPE PROJECT 

The basic objectives of this experiment were to design a procedure to 

show trends in criminality in the Los Angeles area, and to design a prototype cOI11llul1ity crime seriousness compilation procedure that could be used 

for planning purposes. The compiiation procedure had to meet four basic 

criteria, 

66 

1. The cost and manpower needed had to be minimal. 

2. The data base had to be uniform over the historical time frame 

of interest. 

3. The data base had to have a good relationship to actual crime. 

4. It had to be possible to apply measures of seriousness to the 

crime data in such a way as to facilitate disaggregation and 

analysis. 

It was discovered that a very useful source of information for social 

indicators exist in a location that is infrequently tapped for the purpose-- namely, the modus operandi or MO files in a police agency that are normally 

kept to facilitate criminal investigations. There are several reasons why the MO files could be useful for this purpose. 

First, there is a high level of detail and completeness. F0r example, time, to the hour of occurrence and the day of the week, and geographical 

location, to a few block neighborhood area, are completely specified. Data 

can be aggregated in any combination on the time and location variables, and 

can be reaggregated many times to suit various needs. Moreover, the MO 

files represent most reported crimes, the reporting-abstracting-coding 

process has been relatively uniform over a long time span, and the files 

are often automated. 

Second, the form of data in the MO files contains sufficient detail 

on the crime itself to permit the application of weighting procedures 

simllar to the Sellin-Wolfgang Index. Also, the MO files can be crossreferenced to land use and census files kept by various other agencies 

that have geographical bases very close to the police reporting districts. 

Time and geographical coordination between different agency data sets is 

fairly good. 

There are three other adv~ntages that should be mentioned. The MO 

files are based on original first-hand crime reports and are prepared according to a standard policy guide by trained officials--i .e., police 

nfficers. The use of police MO files poses no danger to individual privacy since there are no names, addresses, or personal identifiers of any kind 

in them. Finally, MO files are in common use in many police agencies pro- viding a vast, largely untapped, historical base of information for studying 

and measuring crime trends. 

To test these assumptions, a prototype project to trace trends in the 

seriousness of crime in the Watts Model Cities Area was undertaken using 

the Los Angeles Police Department (LAPD) data base from late 1966 through November of 1971. A city-wide total of about 95;0.000 crimes over the five-" 

year period were available for analysis. An adaptation of the SellinWolfgang Index of Seriousness was applied to the MO descriptors to check 

the feas; bil ity of computer-based procedures and to obtain an indication 

of possible local trends. 

These adopted weights introduced two weaknesses into the) prototype 

project. First, the Sellin-Wolfgang Index has not been validated for 

Los Angeles or for the Model Neighborhood. Second, descriptors in the 

MO files, t~special1y for bodily injury, did not rnatch up directly with 

67 
the Sellin-Wolfgang measures. However, the researchers were less interested 

in the exact numbers than in determining whether it would be possible to 

create a social indicator that takes particular advantage of the characteristics and data available in a police MO file. 

A rather straightforward computer program was written to examine each 

record in the MO files, assign weights as indicated, and then aggregated 

the weights for specific time periods or small geographic areas. Comparisons were then made between the Watts Model Cities Area and other areas 

in Los Angeles. The seriousness was calculated in three forms: 

• raw seriousness 

• seriousness per 100,000 population 

• seriousness per MO card (approximating seriousness per crime) 

FiguY'e 1 shows the calculated monthly crime seriousness per 100,000 

population in four Los Angeles areas. Regression lines are shown for 

comparison with the all-city average. The data suggests that the all-city seriousness index did not change radically over the three year period, but 

that some areas are i ncreasi ng in seriousness while others are decreasing 

over time, and that month-to-month variations can be quite significant. 

Figure 2 shows a comparison of the regression lines for a single division of monthly seriousness per 100,000 population as compared to the UCR 

Part I crimes per 100,000. In this particular case, the UCR suggests that 

crime is increasing in the division while the new measure suggests that 

seriousness is actually decreasing. While it was not found to be typical 

for the UCR to be moving one way while the seriousness index moved another, 

this example does show that it can happen. 

In the process of the experiment several problems arose. It was not 

always possible to formulate a simple relationship between descriptors and 

weights. Several descriptors sometimes had to be combined logically to 

determine the appropriate seriousness weighting to apply. Cost was another 

potential problem. However, aside from setup costs (obtaining and validating 

the weightings anO programming the system), the experiment showed that a 

procedure can be implemented to give desired crime measures for any neighborhood a few blocks in size, for any time period in the past five years, for 

the cost of a very short computer run (under $100). Moreover, it would not 

be a difficult matter to make the calculation of a few specific crime indices 

an automai;;ic by-product of the normal running of the MO files. 

~UMMARY 

As this experiment has shown, it is feasible to use MO files to 

generate an index of crime that appears to ba superior to traditional 

measures in several import~nt respects. Once such an index is calculated, 

it becomes possible to use it for a variety of purposes, some of which 

are described below. 

68 

I 

.. 

r---------------------------------------_____________ ~ 

c 

o 

';:; 

~ 

::l 

a. 

o 

a. 

o 

8 

o 

o 

.... 

en 

en 

w 

Z 

en 

::;) 

o 

a: 

w 

en 

w 

:2 

a: 

u 

700°r-----~-------------~r--------------~----~ 

I 

6000 NEWTON 

5000 

77th STREET 

4000 - -/ 

3000 

200:

1 

- - - - - - ~VENICE J 

1969 1970 1971 

~-----------------------

c: 

0 

';:; 

..!!! 

::l 

a. 

8. 

0 

0 

0 

6 

0 

.... 

en 

w 

:2 

a: 

u 

..... 

0:: 

~ 

Cl. 

Figure 1. MONTHLY CRIME SERIOUSNESS PER 100,000 POPULATION 

IN FOUR AREAS OF LOS ANGELES 

" 

1400 6500 

1250 

1100 

5750 

5000 . 

.•......... ..... ...•............ ~R ........... \ ......... ....... 

SERIOUSNESS 

950 4250 

800 , 3500 

650 2750 

500 2000 1969 1970 1971 

Figure 2. MONTHLY SERIOUSNESS/100,000 COMPARED TO MONTHLY 

PART I CRIMES/100,000 FOR THE NEWTON DIVISION 

69 

(") 

:::0 

s: 

m 

en 

m 

:::0 

0 

c 

en 

Z 

m 

en 

en 

.... 

0 

0 

b 

0 

0 

'0 

0 

'0 

~ 
1 

I. 

2. 

3. 

Policy Evaluation--As long as funds are available for social 

experimentation in high crime areas, it will be necessary to 

evaluate the impacts of these experiments. For example, if a 

social agency introduces a new counseling program for juvenile 

offenders or a new outreach program for potential offenders in 

a particular geographic area and desires to study the impact on 

crime in that particular sector, one could define the geographic 

boundaries precisely, perhaps down to the few city blocks involved 

and then calculate a crime index per 1,000 juveniles for the last 

five years. This index can then be projected for several years 

in the future to give a baseline for the area to suggest what 

crime would have been had the program not been implemented. 

Actual readings can then be taken after the program has been 

implemented for several years to see haw the actua1 impact has 

differed from the projected impact. SimilJrly, a police department may wish to know whether c:,,~me in a particular area is 

getting more serious--that is, whether the crime index per criminal eventis increasing or decreasing over time. This would 

be a simple matter to calculate with the proposed measure. 

Monitor'ing Activities--It frequently becomes necessary to determine where in a city the rates of serious crime are increasing and where they are decreasing. The proposed index makes it 

possible to locate high crime and low crime areas dynamically 

with some precision and to set up control parameters that will 

alert officials when special action in a particular area appears 

warranted. Data can be calculated to observe trends over time, 

to correct for seasonality and even to analyze the frequency of 

events on particular times of the day or days of the week ;n 

certain aretls. Obviously, this would be very helpful for scheduling the deployment of police officers. 

Planning Decisions--It should be possible with an index of crime 

such as the one proposed in this report to tie budget allocations 

and manpower authorizations directly to demonstrated needs in 

terms of crime trends. The measures lend themselves directly to 

many operations research type of studies on resource allocation 

decisions, scheduling, dispatching rules, etc. 

The calculation of a seriousness index for crime and delinquency based 

on a city's MO files appears to be a flexible and powetful methodology for 

quantifying and analyzing patterns of crime in an urban society. Further 

work should be done to study the generalizability of these results~ the 

rep1icability of the Sel'!in-Wolfgang studies and the various applications 

to which these measures can be put . • -_o>.-.-_~. ____ ,_________________ _ 

REFERENCES 

1 Sellin, Thorsten and Marvin E. Wolfgang (1964). The Measurement of 

gelin_quency. John Wiley and Sons, 605 Thiy'd Ave., New York, New York 10016, 

70 

CHAPTER 8 

ANALYSIS OF CRIME WITH OFFENDER 

BASED TRANSACTION STATISTICS (OBTS) 

Susan Katzenelson 

INTRODUCTION 

. . 9BTS data allows cr:linal activit· lndlvldual offenders and with r· les to be analyzed with respect to 

originates at the local level a~J'c!'~'d to offense. Si'~ce OBTS information 

areas, a variety of offender- and ~~f:ggre6ated :or lncreasingly large 

fOdr the purposes of understanding crim~~~l as~~ ~ts~ues may be addressed 

an trends. ac lVl les and crime patterns 

Crime is a serious preble f' d . 

is ~ method based on modern te~h~Ol~n ustrlali~ed, urb~n societies. OBTS ~ellable Md accurate picture of .9Y that.tnes to 91ve a faster, more 

Justice system. Assuming some r~rlme and ltS treatment by the criminal 

reader, this Chapter will disc~s lO~hknowledge of OBTS on the part of the 

and how the data can be utilized ~or e s~op~ of O~TS implementation efforts, 

suggest approaches that can be used w~~~ 1Zt

1,n g crlme. It will attempt to 

OBTS data systems. 0 1 manual and computer based 

FORMS OF OBTS IMPLEMENTATION 

The concept of OBTS in its ori . 1 

level .und~r the Project SEARCH*- rog~~~ form was ~eveloped on a national coor~lnatlOn with seve\~al partic~p f. by a spec~al task force working in 

cons1derable flexibility in th f a 1ng states. Although there is 

are imple~enting OBTS, some im~o~~~~tand scoPl~ of.OBTS in the states that 

OBTS and lts application to cr,' 1genera lzatlons can be made about 

me ana yses. 

Da~a for an OBrS system are ori inat d b the pollce, prosecutor's office co 9

t e y law e~forcement agencies: 

Collects data for Os.rs is requ;;ed ~~'\~ a~d.correctlOns. Each agency that 

to conduct periodic updating and a .. ,~ ec _l~~ a~~~racy and completeness, security and pri vacy. UOltl ng or as t1 i es, and to guarantee 

Data collected for the crim' l' . purposes. As the National Advis~~ya C~~~l~lC~ sy~te~ can serve multiple . SSlon In ltS report on 

* . . For a more detail ed descri pt' f 0 " Cr'~'nal Justice Statistics S ste 1~~ 0 BTS~I see Imp1e',nenting1 Statewide ~roJect. SEARCH (Sacramento C~l ifm~ C \ ~~del. and ~mplementation Environment II 

oundatlon, January 1972):' .. a J ornla Crlme Technological Research' 

71 
Criminal Justice Standards and Goals (1973) pointed out: IIIdentical data 

'eIernentsshoul d be used to s ati sfy requirements for simil ar infonnation to 

be developed for either an 08TS or the Computerized Cri!11inal !/istory (CCH) 

IJY'item over all areas of the criminal justice system ll (Standard 7.1, page 

9[;). Hence. liThe coll action of data required to sati sfy both tht: OBTS and 

CCH systems should be gathered from operating criminal justice agencies in 

t1 single collectiDnfl (Standard 7.2, page 102). 

The establishment of an OBTS record is triggered by an arrest, as the 

fi.;t transaction between the adult defendant/offender and a criminal jus- tice agency (in that case, th~ police). The file is based on the person; 

it records his step-by"step transactions with the various agencies as his, 

case is orocessed through and disposed of by the system. ' 

The OBTS system identifies each offender by a finger print-based FBI 

number, used nationally to track and identify the individual throughout his 

criminal history irresp8ctive of his present and prior states of arrest or rcsidenc"~. 'In addition, the s'~~tes can use other identifying numbers fDr 

each anTS unit. 

As suggested by LEAA, the minimal elements of 08TS would ,include identifi cation data, information on charges, police and prosecutor actions, 

criminal court actions, and iinallYi'court and correctional dispositions (see f.'igure 'I. in IlAnalysis of the Criminal Justice System ,with Offender, 

Bused Transaction Statisticsll ). 

There is some variation regarding the types of offenses leading to the 

establishment of an OBTS fi18. Project SEARCH in its Technical Report No.4, 

page 15 (cited previously) suggested to use as the unit of count only adult 

felony defendants, defined as such by the most serious police charge at the 

time of ar~est. But this method might raise several problems. First, it 

describes only one form of crimes felonies, and only part of the criminal 

,iustice system's functions, that of handling felonies. It does not cover 

the large number of misdeameanors (which probably constitute over 50 percent 

of all known crimes) and the large amount of resources spent on their proc- QS5inq. Second, the category of felonies covers different offenses in different states, and such a lack of uniform definition would hinder the exchdngcnbil ity and comparative val ue of state-based OBTS records. Third, due 

tC'l the po~;)ibi1ity of reducing original felony charges to misdeameanors, 

OBT5 figures might include a sample of offenses that eventually ended up as 

Ill; Sde!MrmOrS J but thi s sampl e woul d probably be bi ased, not representi ng the univc~se of known misdemeanors. Therefore, the suggestion is made to use 

N(i.tlonal Cr1l1linal Information Center (NCIC) criterion offenses that inclu'de 

all feloni€b 'nd most misdemeanors, with the exclusion of traffic offenses 

and drunkenness. Finally, although there is need to handle juvenile justice 

data separately, wHh many more restrictions on access, these cases constitute u very S 1 gnifi cant part of the crime probl em and coul d eventually be 

inccH'pora'ted with the OBTS system of adult crimes. A complete criminal histOty should include the defendant's juvenile justice record. 

72 

, . 

The form and level at which the at. 

ries by state. Source documents can b~ omatl~~, of data is accomplished vathe.state. Special OBTS forms can be f·ffnJ a,lrectly by local agencies to 

thell'transfer. Local terminals' can se~d ~ /L!t o~ coded locally prior to 

or records can be completely comput . d ln ormatlon to a central computer 

actions ~an ~e reported to state jU~~~~~l at the~loc~l lev~l. Local trans-' 

tems, Wh1Ch 1n t,l(rn make data available fo~ro~~f~ectlOnal lnformation sysOBT? ~an be useful even for communitie '. . tem of f1l1ng and recording. Their d t s or age~Cles wlth a manual systo .be computeri zed; they can be manua f a can ~e sent to other state centers 

research purposes' or a data base bY ~t~dled and analyzed for special 

ture automation of a system. can e U1lt IIp as a first step toward fuTo summarize, the OBTS system ld h' . on ~ach individual arrested (as' an ~~ult) Ol~ 1nformatlOn on a state basis (pr~ferably defined by the· uniform NC~ ~n ~harged with certain offenses 

actlOns recording 'conta'c;:ts, as he r C cnter1a); the st~p-by-step transtern; and over a period of time thP oceeds through t~e cnminal justice S,YSof these individuals with the ~rim~naa~c~mUtl~ted entrles and criminal contacts . JUS lce system. 

OBTS DATA ANALYSIS. AND APPLICATIONS 

In describing criminal ~ctivit ° ~t least two meaningful ways' (1)~' Bi~ dat~ can be conceptualized in 

lndividual defendant/offende~ and (~o)m e pOlnt ?f view of the , from the p01nt of view of pffenses. 

From the moment of hi s fi rst 1 . , " thro~gh several steps of relabel i p~ 1 ce encounter, a person may go 

convlct, recidivist etc These rg~ las suspect, arrestee, defendant 

his initial behavio; i ~ w'h th a e

h

- s.only partially correspond with COI1lJ1'tt d ' '" e er e 15 a Ilcrim' 1 II 1 e an illegal act. Rather th 'fl ln~ , actually having about a person (e g the d .. " ey re ect ,varlOuS decisions made d ' . ., ec 1 s 10 n to a rr t h' 

an t~e decision to find him guilty k e~. 1m mak~s him an arrestee 

even lS we assume that onl erso rna es 1m.a convlct). Further, 

prosecuted, and conVicted Yth h ns that COnlmt a crime are arrested 

POint do not necessarily ;e r e c arges brought against them at each' ~. 

especially in jUrisdictionsPw~:~~t t~e p~rsonls actual criminal actions 

Accordingly, any analYSis based on ex enSlve plea b~r~aining occurs. ' 

as.OBTS, will not describe all the: system ~f.Offlclal records, such 

unwerse, but only the offici 11 ctual cr~mlnal behaVior in a giVen ~esu1ted in an arrest and theafo~m:rofn PO)'tlOn of all cr~mina1ity that 

1n the process) brought against the de~gadl chtarges (changlng even While 

en an" 

73 

~----_L ---------------

i) 
Keeping in mind these qualifying elements, OBTS will enable the planner or f.0searcher to look at "criminals," their demographic characteristics 

(age, race, sex, etc.), the types of crimes they are charged with, changes 

in the formal charges as they relate to the orginal cha:--ge, final disposi- tion of the case, and correctional actions, if any, taken against the offender. On a continuous basis, 08TS would permit analysis of the development of criminal ifcareers,ft with the reentrance into the system of an individual 

previously identified. These lirecidivists," depending on the purpose of the 

research, can be defined in terms of rearrest, reprosecution, reconviction, 

or reincarceration. Time analyses of recidivists would demonstrate existing career patterns.; i.e., the "professiona1 criminal" special-izing in burg- laries or prostitution; the lIeclectic addict" sampling various types of 

property offenses to support his habit; or the "crime-graduate" who starts 

out with gang membership and minor assaults and gradually grows into armed 

robbery, rape, or homidde. These various patterns could then be related . to personal characteristics and to previous dispositions of recidivists. A 

division of individuals into first and repeat offenders would give a picture of th~ proportion of crime, within a given time period or location, committed 

by reci di vi sts as compa:red to "llfi rst timers." 

When used in combination with other data bases 'f 08T5 can aid the ana'lysis of crime in general. In its relation to victimization surveys, OBTS can 

show what types of offenses tend to remain hidden and what types lead to 

more arrests, prosecutions~ and convictions. By comparing victimization, 

Uniform Crime Reports (UCR). and 08T5 statistics, the relative accuracy, reliability, and patter'ns of police charges versus prosecutorial charges or 

convictions can be analyzed. 

OBTS data t when accumulated over time would also build the basis for 

several. scientifically meaningful, comparisons: 

• crime trends in the same jurisdiction over time 

• similarities and differences in crime among jurisdictions or 

geographic areas 

• evaluation of policies and policy changes within and between 

jurisdictions or geographic areas 

• analysis, on a statewide basis of individual offenders over 

time with a readilY available and more accurate definition 

of recidivists, career criminals, etc. 

J4 

. The rat~s at which people are being arrested, charged, prosecuted convlcted, and ~ncarcerated can be computed, using as the denominatol~ the' 0n- ~ral pOPula~lon, personal characteristics of:pefendants, all the defend~~ts ln t~e prevlous step of processing, all defendants charged with that p~rtlcular.offense~ etc. Each of these rates would give a different 

Vlew of Crlrne and lts handl ing in a given community. 

For example, by comparing the rate of 

all defendants incarcerated for rapeall defendants arrested for rapi 

with the rate of 

all defendants incarcerated for armed rObbery 

all defendants arrested for armed robbery, 

on~dcoUld stud~ a~trition during criminal justice nrrncessina. t h ev~ ence, convlctlOn, degree of punitiveness etc : -" "oJ' s ~rengt. of crlme categories. ' ., ln these two violent 

* For a 

System with 

\., '. 

descri pti o~""~'f-"PROr;h_s':"~ ~~~""j~A;l';;i ~'-'~f the cri.,mi'na 1 Llus ti ce 

the Prosecutors Management Information System (PROMIS)." 

.. '_41 _________________ .. 1 _________ 

7

_

5 

___ _ 

" .. 
TABLE 1 

ARRESTS IN 1973 BY OFFENSE TYPE OF 

MOST SERIOUS CHARGE AGAINST THE DEFENDANT 

1. Crimes Involving A Victim Number 

A. Personal Victimizations Involving Violence --------- 5040 

1) Homicide ----------------------------- -- 259 

a) Murder---------,,------------- 200 

b) Manslaughter------------------ 49 

c) Negligent homicide------------ 10 

2) Assault -------------------------------- 2891 a) Aggravated------------------- 2002 

b) Simple----------------------- 684 

c) Assault on a police officer-- 205 

3) Forcible Sex Offenses------------------- 450 

a) Female victim 16 and over---- 357 

b) Victim under 16-------------,- 72 

c) Male victim------------------ 21 

4) Robbery -------------------------------- 1440 

a} Armed ---------------------- 726 

b) Other ---------------------- 714 

Percent 

32.6 

B. Personal Victimizations Without Violence ----------- 1898 12.3 

1) Larceny -------------------------------- 1337 

2) Auto theft------------------------------ 372 

3) fraud -----=--------------------------~- 189 

C. Crimes Against Residences or Household>------------ 1370 8.9 

1) Burglary -----------.------------------- 1174 2) Property destruction -------------~----- 164 

3) Arson ---------------------------------- 32 

D. Crimes Against Businesses or Institution>---------- 2099 

1) Robbery ------------.------------------- 217 

2) Burglary ------------.. ----------~------- 372 

3) Larceny ------------------------------- 1059 

4) Embezzlement and Fraud ----------------- 305 

5) Auto theft----------------------------- 74 

6) Arson ------------~--------------------- 8 7) Property destr:..:ction -----,-------------- 64 

J L Crimes Without an Identifiab1 e Victim ------ ------------ 4757 

A. Weapons Offenses ---~----------------------- 1042 

1) Gun------------------------------ 827 

2) Other weapon -------------------- 215 

B. Gambling ---------------------------------- 372 

C, Consensual Sex Offenses ------------------- 836 

D. Drug Offenses ----------------------------- 1872 E. Bail Violations and Prison Breach---------- 635 

Ill. Crimes Which Could Not be Classified------------------- 296 

All Cases 15,460 

-----------------

13.6 

30.8 

1.9 

~~ 

" 

_____________ 7

6 

____________________ ...... 10 - mf I _ 

Two and three dimensional contingency tables can be used to explore 

the relationship between selected variables' such as case disposition, by 

race and sex of the defendant (Table 2). ' 

TABLE 2 

PERCENT DISTRIBUTION OF OFFENSES BY TYPE OF DISPOSITION 

AND OFFENDER'S RACE AND SEX 

Offender's --

Sex & MALE FEMALE Race 

Disposition White Black Total % Wh'ite Black Total 

-. Open 8. 9. B.9 8.9 I 9.4 9.4 

No Paper 28.7 23.1 2.3.7 19.9 19. 1 19.2 

Dropped after papering 38.7 3'?.6 33. 1 35. 37.5 37. 1 

Not Guilty I 

2.4 4-.4 4.2 6.5 4.7 5. 

Guilty (plea or other) 21. 30.1 29.3 28.3 28.9 28.8 

Other 1.2 .7 .8 1.5 .4 .6 

Total Percent 100 100 100 100 100 100 (No. cases) (1195 ) ( 11405) (12601 ) (403) (2011 ) 1(2414) 

% 

anal F~nally~ more so~histicated multivariate methods, such as regression vari~~~s, can be app11ed to describe the determinants of selected depend~nt t~ ~ es. For example, one can study the strength and direction of . c~e ,mbP~ct that several demogr(l,p~ic and crime-related variables have 0;;1 a 

se elng accepted for prosecutlon. 

77 

r 

I 
USE FOR PLANNING 

The first step in planning. should be a description of the phenomenon of crime in order to obtain a knowledge of the scope, form, and type of 

the problems. The descriptive statistics derived from OBTS can fulfill 

this need by providing an aggregate picture of known crimes in a .J 

given area, or special cuts and segments of it, depending on the need and 

questions asked by the planner. 

By deriving a demographic and criminal profile of offenders, learning 

about crime patterns, identifying certain offender groups (such as violent, 

professional, career, etc.), and exploring the effects of various dispositions and correctional actions on rehabilitation/recidivism, a planning 

agency will be able to define the problems to be attacked. It will be in 

a better position to determine priorities and to assess programmatic 

alternatives and project proposals. It can then make the crucial decisions 

about allocation of resources and manpower, need for cooperation or changes, 

etc. Further, by viewing the criminal justice agencies as a system that . . processes the individual offender from arrest to final release, the necessi~1 

of synchronized plann~ng becomes evident. The success and effectiveness of 

any decision by a Single agency might be severely limited if that decision is.· 

not accepted and consistently followed up by the policy and action of other, 

related agencies. For example, the police might make a conscious effort to I 

apprehend and press charges against serious recidivists, but these arrests' 

will have little impact if the district attorney's office does not have the 

resources for sl'/ift and thorough preparation and prosecution of these cases, 

'if the cour·ts cannot mete out speedy and evenhanded di spos itions to 

offenders, and if corrections departments lack suitable programs reco~~ended 

for rehabilitation. 

The abiiity of OBTS to focus on the individual offender and follow his 

transactions step-by-step will facilitate IIsystemli decisions that are 

aimed at improved interagency cooperation. OBTS would aid in a concerted 

planning effort by the whole criminal justice system to deal with crimes and 

eventually reduce their volume and seriousness. 

USES FOR EVALUATION 

Once an area under consideration has been accurately described, its 

crime problems identified, and plans have been made to deal with them, the 

next step would be to set up a program to evaluate the effectiveness of the 

plans implemented. Although the definition of effectiveness originates from 

various social and political value judgements, it often can be measured by 

objective, quantified, statistical methods. 

For example, one value standard in our system is the administration . of justice in an evenhanded way. An OBTS system can offer several empirical: 

ways of defi·ning and measuring evenhandedness. For example, this could 

, 

I, 

! 

78 

be done ~y comparing dispositions for various offend T ' 

controlllng for other relevant variables l'k ff er ... ypes, wh~le and prior convictions. It could also b' 1 e ~. ~nse type, serlo~sness, at variations in pretrial release e accomp 1S ed throu~h looklng for legally relevant variables. ,by race, age or sex, agaln controlling 

Soun~ empi~ic~l findings could help the criminal evalua~e ltS eXlst,ng practices and policies, as 

effectl ~e~ess of :changes, and thereby serve as a and declslon-maklng. 

justice system to 

well as to measure the 

useful tool in planning 

I 79 I 
L 

T 

r 

r 

Part III. 

THE CRIMINAL JUSTICE SYSTEM: 

DATA, MODELS, AND 

TECHNIQUES FOR ANALYSIS 

~ ":.::.:....;;..-"-~' "W"';';"";"''''-''''YIIIIIIiiiIIli' ________ ,1_1.." __________ _ 

, '~ 
~ ~' 

i 

INTRODUCTION 

CHAPTER 9 

ANALYSIS OF THE 

CRIMINAL JUSTICE SYSTEM WITH 

OFFENDER BASED TRANSACTION STATI STIes 

(OBTS) 

Susan Katzenelson 

The 'Offender Based Transaction Statistics (OBTS) system can be used 

in numerous ways to understand) interpret, and evaluate criminal .trends . and their relationship to the criminal justice system. This Chapter 

suggests the types of analyses that could be used within an agency or for 

comparison purposes across agencies. These analyses may range from 

studies of single variables to more complex analyses in which given 

outcomes may be related to several variables that characterize the criminal 

justice system. The general structure of an OBTS file is presented in 

Figure l. 

As described previously (see Part I, Chapter 7) OBTS is a state-wide 

system based on individual files originated by an arrest. Each file 

includes transactions reflecting contacts of the person arrested with 

various agencies of the criminal justice system up to the point of exit 

from the system. Each file contains the relevant interaction between 

the defendant and the agency: the most serious charge brought against 

him, disposition of the charge, and correctional action. The OBTS structure 

suggested in Figure 1 is only a skeletal model with the minimum necessary information. Other items can be added according to the needs and interests 

of the jurisdiction adopting it: 

GENERAL APPLICATIONS 

Whereas the earlier section focused on the use of OBTS in analyzing 

patterns of crime, the purpose of this section ;s to show how OBTS can be 

used to analyze the working of the criminal justice system, including measure~ of its workload, productivity, and evenhandedness. 

One of the major problems, often cited by practitioners and researchers 

alike, is that the criminal justice system, despite its name, does not function as a well-coordinated "system." Policymakers, operations personnel, planners, and re~;earchers tend to view the system in a fragmentary way, dealing with each agl~ncy as a separate, independent entity. OBrs is especially - well equipped to deal with this problem, because it lends itself to a "sys- terns" view of the administration of justice. At the receiving end of the 

system, a pr'ocess is initiated with the lIinput" of an arrest for a criminal 

incident that was cleared by charging an individual with committing the 

crime. At the other end of the process is the "output II of the criminal justice system, the final action taken against the arrested individual related 

to that particular criminal incident. BRsed on personal identifiers (FBI 

83 

'~~~'-"""'''''"-'' ---.-.,...,.,..,.-~, ~"~' "~'==""; 1_---------1.---------------
......... -------. ........-,......-

OFFENDER BASED TRANSACTION STATISTICS (OBTS) 

~ ______ (O_B_TS_) ______ ~I I 

~DENTlF'CATION ELEMENTS 

State Idcntific(ltion No. 

FBI No. 

Stoto Record No. 

Sex 

Raca 

Duta of Birth 

I POLICE/PROSECUTOR ELEMENTS I 

Arresting Agency No. 

Sflquence Letter 

Date of Arrest 

Charged Offens~ (Most Serious) 

Police Disposition 

Prosecutor Disposition 

Police/Prosecutor Disposition Date 

I LOWER CRIMINAL COURT 

ELEMENTS 

Court Identification No. 

Initivl Appearance Date 

Disposition Dale 

Chmged OHense (Most Serious) 

(OBTS) 

Lower Court Disposition 

Release Action 

Release Acilon Date 

Final Charge (Most Serious) 

Type of Charga 

Plea (at Trial) 

Type of Trial 

Date of Sentence 

Type of Sen tence 

Confinement Term (Days) 

Probation Term (Months) 

Type of Counsel 

COUNTVPROSECUTOR 

GRAND JURY ELEMENTS 

Prosecu'trir Identification No, 

Date of Filing 

Type of Filing 

Filing P,ocedure 

Date or Arraignment 

Chargoo Offense (Most Serious) 

1 nitl:.;l Plea 

Release Action 

Release Action Date 

I FELONY TRIAL ELEMENTS 

Court Identification No. 

Trial [',te 

Trial 'l'1pe 

Fin(11 t-'Iea 

Trial Ending/Disposition Date 

Final Charge (~lost Serious) 

Type of Char ge 

Court Disposition 

Sentence Date 

Sentence Type 

Confinement - Prison (Years) 

Confinement - Jail (Days) 

Probation (Months) 

Type of Counsel 

I CORRECTIONS Ei.EMENTS 

Agency Identifier 

Receiving Agency 

Date Received 

Stotus 

Date of Exit 

Exit 

Figure 1. GENERAL STRUCTURE OF AN OBTS FILE 

34 

i 

, 

t 
I 

I 

I 

OFFENDER BASED TRANSACTION STATiSTICS (OBTS) 

[~ (OBTS) 1 [ (OBTS~ 

l mENTIFICATION ELEMENTS 

Statll Identification No. 

FBI No. 

Stute F,ecord No. 

Sex 

Race 

Date of Birth 

Lower Court Disposition 

Release Action 

Release Action Date 

Final Charge (Most Serious) 

Type of Charge 

Piea (at Trial) 

Type of Trial 

Date of Sentence 

Type of Sentence 

Confinement Term (Davs) 

Prpbatlon Term (Months) 

Type of Counsel . 

: I 

] COUNTY PROSECUTOR 

GRAND JURY ELEMENTS ------' 

Prosecutor Identification' No. 

I POLICE/PROSECUTOR ELEMENTS! 

Arresting Agency No. 

Sequence Letter 

Date of Arrest 

Charged Offense (Most Serious) 

Police Disposition . 

Prosecutor Disposition 

Police/Prosecutor Disposition Date 

LOWER CRIMINAL COURT 

ELEMENTS 

Court Identification No. 

I nitial APpearance Date 

Disposition Date 

CharGed Offense (Most Serious) 

Date of Filing 

Tvpe of Filing 

Filing Procedure 

Date of Arraignment . Charged Offense (Most Serious) 

Initial Plea . Release Action 

Release Action D'ltft 

(FELONY TRIAL E(~MENTS 

Court Identification No. 

Trial Date 

Trial Type 

Final Plea 

Trial Ending/Disposition Date 

Final Charge (Most Serious) 

Type of Charge 

Court 0 isposition 

Sentence Date 

Sentence Type 

Confinernent - Pnson (Years) 

. Confinement - Jail (Days) 

Probation (Months) , 

Type of Counsel 

I CORRECTIONS ELEMENTS 

, Agency I d aU ifier 

Receiving Agency 

Date Received 

Status 

Date of Exit 

Exit 

Figure 1. GENERAL STRUCTURE OF AN oars FILE 

\ 

\ f, 

" ! 

I 

1 

\ 

] 

t r,;,· 

nL.:mber and fingerprints), additional c'ycles of inter-action can be added to 

the individual I s fil e \,/ith each new reentry to the system. 

The processing in the OBTS system is a dual one: that of the 

defendant/offender, and that of the case. Both can go through several 

changes. The case can be redefined at each step by the number, type, and 

seriousness of the charges comprising the case. Similarly, the person 

can go through a sequence of relabeling from suspect to defendant, to 

convict, to inmate. The definition of the case and the label'attached 

to the individual will also depend on the varying length of the process. 

The case can be dropped anywhere before or after filing or prosecution. 

It can result in a plea bargain, a trial conviction "or nonguilty finding, 

a sentence of probation or incarceration, and any number of alternatives 

inbetween. 

OBTS enables the planner or researcher to supplement the conventional 

intra-ngency analysis with significant interagency observations. For examp1e~ one can look at police operations in single cases within a given location (precinct, city, state, etc.) or time period (day, month, year). 

In addition, one can also follow up on cases initiated and decisions made 

by the police as they affect the prosecutor's office, the court, and 

corrections. Individual cases can be traced from their intake at the 

police department through their handling and disposition. 

. Although in many states OBTS will be computer based, the feasibility of manual OBTS has been demonstrated in several cities. In any case, the 

establishment of a good manual system prior to Ciutornat~on is an advisable I step. 

USES OF OBTS IN ANALYSIS 

The considerable amount of data collected ~y an OBTS system can be 

analyzed in many ways, from simple descriptive statistics to more complicated measures of correlation and causality.* 

Using unidimensional methods (analysis of one variable at a time), 

questions about the number of cases handled by each agency, the relative 

distribution of manhours at each phase of the system, the mean number of 

convictions per month, and similar measures can be computed. 

Of special use can be system flow rates, such as: 

* 

Number of indiwiduals prosecuted 

Number of individuals arrested 

Number of individuals convicted 

Number of individuals prosecuted. or 

Number of individuals incarcerated 

Number of individuals convicted. 

For an illustration of some of these methods of analysis, see 

Part II, Chapter 8. 

t?A~··: 

... _'._._ .. ,.. 2.4 ___________ L---.6.---------------

85 
. erformance measures of criminal jus- These and similar indlces.can serv~ a~ P k spots" with the highest attrition 

tice agencies, and can ~olnt out.t ) .~e~he cvstem. Furthermore, they can 

rates (cases dropped Prlorh to tlrtl~l :/produ/-t'iVityll of one agency compared help the planner assess t e re a lve ~ 

to others. 

. h . (alyses of two or three Two and three dimenslonal tec nlqueSr~a~he relationship between 

variables at a time) c~n be used to eXP1~he relative role of guilty pleas 

selected variables of 1ntet re7t,o sUr~~o:~'''' or rates of conviction by leng~p as related to the prosecu or s w .. , . of time in the system for felonies and mlsdemeanors. . 

d t d causal relationships, one can Finally~ to.more fully un ers a~xam le the multiple regression 

utilize multlvarlate analYSe~~ FO~ tivePim~act of system variables--

method can be used to stud~ e re arosecutor the court, and corr~ctions-­ such as actions by the.Pdo~l~de, tlhe hile contr~lling for other variables. on the rearrest of an ln lVl ua , w 

Several questions about the characteristics of the system can be 

conceptualized and answered, using OBTS data: 

• 

• 

• 

• 

• 

What is thed.dire~~~o~?ofW~~~nf~ ~~em~~! ~:~~~~~n~~~~g~!tw!~~m arrest to 1SPOSl 10 . d b the District Attorney? police charges and thos~ pre~s~ 9 ~e reflected in the charges? 

In what way would plea argatlhnl~eductions most significant~ etc.? In what types of cases are e 

What 1 t s? In what way do release are the most common re ease ypeh

· and defendant types? 

~~:~u~h:~~e:m~~~~rO;nb~~~e;~!a~~a~~sCt~~~~~hout the process? 

What f guilty pleas, jury trials, is the relative frequency 0 ~ to offense and offender and bench trials, and how do they relaL€ ch~racteristics? 

What .. t b ffense ~ype by trial type, are the convlctlOn ra .es. yo. l" between .... 

and by defenda~t characterlst1~s~ha~h:;el~h~h~o~~PtYPiCal forms original and fln~l charges, an 

of reduction in charges? 

.. tences and offense and What is the relatlOnshl p, beptlweaeeonr s~~ia 1 cony; ctions, offend~rs offender characteristics, 

freed on bailor detained? 

What are the practices of correctional agencies fin granting parole, training and treatmen t ~rogr ams , types 0 probation, 

work release and halfway houses. 

86 

'~4 

• How does recidivism (defined as rearrest, or reconviction) 

relate to actions taken against that individual as recorded 

in previous OBTS cycles: Was he convicted in his previous case(s); incarcerated, tt~eated, etc.? 

• What is the average (median) time lapse between steps in the 

process for all cases, misdemeanors or felonies~ violent 

or property offenses, etc.? What part of the system is more time consuming than others? 

• How does the total number of cases for a given period, and the 

resulting caseload)affect the quantity of cases prosecuted, plead, and convicted; and the types of dispOSitions? 

• What is the "attrition rate" of cases, at what phase? What 

types of crimes are most likely to drop out? (e.g., dismissals). 

How are these factors related to caseloads at each agency? 

• Is there a clear and consistent interagency policy to put 

more resources in cer'ta in types of cases: far examp 1 e, more 

police work, special prosecutio~and speedier trial directed 

into the handling of violent crimes, reCidiVists, major misdemeanors, etc.? 

Performance measures of the various law enforcement agencies can be 

computed, viewed both as absolute figures, and as re1ative system rates, 

evaluating the performance of each agency on the basis"of input from the 

previous agen~ies (i.e., conviction rates based on arrest rates and on 

prosecution rates). Then, in turns the effect of that agency's. performance on subsequent actions can be reviewed. 

OBIS figures can be useful in comparing various system statistics on 

crimes with surveys on Victimization and reported offenses. Such analyses could paint to the morta1ity rates of various offense types before they 

reach the phase of arrest, to the ways in which the charges brought at each 

level depart from the actual criminal act as reported by victims. They can 

alsq help to estimate which agencies give a more accurate and representative sam~ling of the universe of crimes--arrest, prosecution, or conviction. 

Some of the decision areas and pressing problems of the criminal 

justice system are clearly implied by these and similar analyses, as 

win'.be pointed out in a fe\'I examples. FUrther, accOI'ding to the special needs of each jurisdiction, additional questions can be analyzed by OBTS 

data directly geared to deal with policy decisions and trouble spots in the system. 

87 
) -

For example, a community might experience a serious jump in the rate 

of armed robberies. First, with the use of OBTS, it can analyze the existing practices in handling robbery cases. The statistical analysis would 

give a demographic profile of people at'rested on a robbery charge; would describe the rate at which they are prosecuted, plead, and convicted; and 

would give the final charge for which they are found guilty, the types of 

sentences, etc. Linked to management and administrative data, it would also 

compute the relative amount of resources or manpower spent on dealing with 

robbery cases, as compared to other crimes. 

Based on the results of each analysis, decisions about changes can 

be reached. For example, experienced prosecutors could be assigned 

to handle the armed robbery cases or a special effort could be made 

by the police and the prosecutor's office to ensure victim and witness 

cooperation and a reduction of the number of continuances in these cases. 

The prosecutors and court might aim at reducing piea bargains and try to 

convict the uffenders, or at least the recidivistic ones, on the original charges. F;nal1y~ special programs could be available at the correctional 

level to rehabilitate these habitual offenders. 

As a third step, fol~owing the diagnosis of the problem and the 

implementation of some new decisions to deal with it, the effectiveness 

of the changes should be evaluated by comparing the before/after picture 

of the incidence and handling of robbery cases with the use of OBTS data. 

Using another example, the pattern of bail decisions could be reviewed 

using information from 08TS files. With the aid of descriptive and analytic statistics, the possible lack of evenhandedness in granting bail could be 

discovered. While bail should relate mainly to the defendant IS likelihood 

of showing up for trial, OBTS data might show, for example, that race might 

act as a determinant of setting and making ban. Further, 08TS data might 

indicate that pretrial detention increases the defendant's chances of conviction (controlling for the type of case and strength of evidence), prob- ably due to his limited ability to prepare a defense. Once the problem is 

identified, some decisions can be made to correct the system--like changing release conditions or improved defense services to defendants, etc. As a 

final step, the success of these ch~nges can be followed up after a given 

time interval to determir.~ from OBTS if indeed bail decisions are made in a 

" more evenhanded way and without hiasing subsequent dispositions. 

Another problem often encountered by law enforcement agencies is evaluation of the effectiveness of various programs: the question of IBwhat 

works. II One important measure of success appl ied by 013TS over time 'j s the 

rate of recidivism. Recidivism can be defined both as numbers of rearrests 

and reconvictions, and as type and seriousness of these rearrests. Comparisons of redidivism rates for offenders in diversion programs with those 

prosecuted, for defendants found guilty with those acquitted, or for convicts sentenced to incarceration with those on probation~ could illuminate 

88 

the impact of these actions on a pe I 

system. Findings from com arisons rson s return to the criminal justice 

strategies that would redu~e the ratSeUcohfas t~de~e ,may suggest changes in reel lVlsm. 

OSTS systems, when implemented t th 1 

important comparative information toa 

,e, o~al level, could supply PROMIS system *, first applied in Was~~nlclpa and s~ate planners. The 

sllch a working system. Similar to OBTsng~~~MI~'C" 1S an exa~lple,of law erlforcement agencies to analyze d', enable~ ~he D:stnct's 

system. Such a system meas r an lmprove the crlmlnal JUstice 

at the local level and cre~t:~ ;h~aP~rf~rmance of,the various agencies 

municipalities and counties within t~~\t~~e~ompanson between 

With the adoption of OBTS syst b . 

sons can be conducted between juris~~St'y varlOUS states, several.compari_ urban versus rural areas' bet lC 10ns at the state level; between 

number of states, or the'de rweenfseparate ~gencies, like the courts, in a 

agencies in those states' r~see 0. co~peratlon am?n$ ~aw enforcement 

incarceration; or other ~ariab~~~l~~ ;~~:~e~~.recldlVlsm, conViction, 

The SUccess of novel legislat' d 1 ' diction can be better monitore lon an .po ~cy change~ in one jurisother states. And finally cr~m~Yt~~m~arlngl !ts OBTS flgures with that of 

in underlying social value; and id ln~) po lCY ch~nges, and variations 

013TS data accumulated over time. eo ogles can be dlscovered by analyzing 

* SeMae: IIAnalysis of the Criminal Justice Systom with 

nagement Information Systemll w Prosecutors 

89 

.~' 
I, 

INTRODUCTION 

CHAPTER 10 

ANALYSIS OF THE CRIMINAL JUSTICE SYSTEM 

WITH THE PROSECUTORS MANAGEMENT INFOR1~ATION SYSTEM (PROMIS) 

Sidney Brounstein and William Hamilton 

A primary deterrent to the consideration of rational and systematic 

change in the criminal justice system is the absence of factual, ~mpirical 

data on the performance of the system. Virtually every major national 

inquiry into the health of the criminal just'ic:e system from the Wickersham 

Commission in 1931 to the National Advisory Commission on Criminal Justice 

Standards and Goals in 1973 has decried the lack of comprehensive, empirical data that are necessary to elevate the system. The Law Enforcement Assistance Administration took a major step to redress this data deficiency by encouraging all states to develop Offender Based Transaction Statistics. 

Systems (08TS). In this Chapter, a system called PROMIS, which contains 

much of the data conceived for inclusion in an OBTS, will be described and 

some examples of its use will be presented. 

THE SYSTEM 

Since January 197i the District of Columbia has had in operation the 

PROMIS system which has tracked every adult offender prosecuted or considered for prosecution in its Superiol' Court (thE! equivalent of a state 

court of general jurisdiction) .. The system not only tracks the primary or 

most serious criminal charge in each case as the 08TS system will do, but 

also tracks all related charges in each case. Very importantly, PROMIS also 

contains the reasons for every prosecl~torial discretionary decision from 

intake and scr~ening to final disposition in analyzing the criminal justice 

system. 

Essentially, PROMIS permits a prosecutor's office tu accumulate a 

wealth of infoimation on each case and to receive reports and analyses based 

on these data so that prosecutors can identify and concentrate on priority 

areas and exert positive and productive control over their workload, instead 

of merely reacting to it on a best-guess basis. Not only does this promote 

effective utilization of prosecutorial time and personnel, but it also 

serves to attract and retain experienced attorneys. And when local government is aware that existing pi~osecutorial resources are not wasted by being subjected to operational inefficiencies, requests for additional staff may 

be looked upon in a more favorable light. 

Although PROMIS is basically a tool for day-to-day operational management of the prosecutor's office, it has been designed to provide extensive 

data for planning and research purposes. It contains relevant information 

on the workings and decisions of the court process from arrest through final 

l' 

_____________ ------------... 1. .... --------------

9

-

1

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disposition. The perspective of the criminal justice system provided by 

the prosecutor is particularly relevant because: 

• The prosecutor controls the intake to the court ~ystem, ~eciding 

what cases brought by the pol; ce or other camp 1 a ~ nants wl11 be 

"fil ed" with the crimi na 1 court and what cases wlll be handl ed and 

di sposed of through other means such as diversi onary p'rograms; 

• The prosecutor determines how the matters presented to the crim~nal 

court are to be "packaged," deciding, for example, w~e:ther to flle 

lesser included charges, whether to include all posslble counts of 

a particular charge or just a representatiye numbe~, and w~ether to 

initially join codefendant cases together lnto a slngle tr1able 

unit; 

• The prosecuto~ c~n promote ~xpeditious ~is~ositions ~hrough the use 

of plea negotlatlon strategles and tactlcs, 

• The prosecutor can accelerate case processing times by varyin~ t~e 

court r;"'ocesses in certai n instances through sucll r:,eans as pnorl ty 

handl i n9 of cases of II repeat offenders!' or pr~sentl ng a case . " . directly to the grand jury instead of proceedlng through the lnltlal 

presentment and preliminary hearing. 

• The prosecutor can record actions ~aken in court proceedings, such 

as decisions on motions or sentenc1ng. 

PROMIS contains practically an the data needed for an CBTS from.arrest 

through sentenci ng. It shoul d be supplemented by o~hel~ data sources 1 n 

analyzing the criminal justice system. Some data w11l also be n~eded from 

police, court, and correctional ~ystems to fully meet those requ1~e~~nt~. 

The major point of this Chapter 1S to demonstrate that PROMIS does l-'~ov1de 

a ~owerful nucleus for a local government-based OBTS. Fo~ ~xample, ltS de~ign includes unique identifiers for each defendant, crl~lnal event, . court case, and principals in the court case (attorneys, w1t~esses, P?llCe 

officers, judge). Further information on the data bas~ and lt~ us~ Y!1l~ be 

presented later. Before discussing the.da~a base an~ ltS appl1c~t10ns ln 

analyzing the criminal justice system, 1t 1S app~oprlate,to cons1der data 

collection alternatives available to state plannlng agency staff personnel and others interested in such analyses. 

Data Collection Alternatives 

In developing comprehensive plans, conducting criminal justice systems 

research, or in related statistical studies, analy~ts could attem~t to. 

coilect data from various sources on an ad hoc basls. Th~y some~lmes lssue 

contracts to university or privatl2 consultants to engage 1n speclal data 

analyses. Such ad hoc data collection.and analy~is efforts ha~e.b~en found 

to be expedient in the absence of ong01ng opera~lonally ba~ed crlmlna~ 

justice information systems. One purpose of thlS Ch~pter 1S to descr1be, 

by example, how and why operationally based informatl0n ~ys~ems should. 

provide the data needed for criminal justice systems analysls.and plannlng. 

Even if computer based systems are not available, manual verS10ns of 

92 

( 

I 

i 

! 

! 

I 

! 

----------------~ .. 

operationally based systems are preferable to special data collection and 

reporting systems as a source of OBTS data. 

OperationallLJ3ased. Systems Versus Report'l~~9 Systems .2I.. Speclal Data Collections 

The common course of action for criminal justice planners and rese~rchers ~as been to devise a special data collection effort to develop the lnformat10n they need for :studying a particu'Jar problem. Such efforts 

are always l~mited by the time and cost constraints of the study. These 

data collectlon efforts ra.rely result in data that are as accurate as those 

collected as part of an ongoing operation. They tend to be limited with 

respect to the time frame that can be covered. Later, in t~e analysis, if 

~ny.gaps occ~r or c~anges in the data collection design appear desirablE!, 

1t 1S often 1mpract1cal to modify the data base, and the research objectives may need to be compromised. 

Some planning and eva'!uation projects acquirt~ data bases from ext€!rnal 

s?Llrces to st~dy a problem that is apparently reflected in the data. As 

wlth the speclal data collection efforts discussed above the analysis must 

be constrained by the content of the data base, both as to accuracy and 

comprehensiveness. The planners generally are forced to accept the do,ta at face value. 

Operationally based systems on the other hand, are those that support day-to-day operations. Any planning or research data provided by such ~ystems are b~-products of the operational functions. JIf a plarlrling process ~ s co~ducted 1 n close coordi nati on with an operationa 11y based system that 1~ be~ng ~aintained ~nd modified ~hen necessary as users obtain experience wlth 1t, lt can obtaln the follow1ng advantages over special or one-time data collection efforts: 

• improved accuracy, since data being used in day-to-day operations 

are sub,jected to validity checking by automated systems, manual 

data control personnel, and the users themselves; 

• familiarity with the data base on the part of the information 

systems personnel and the operational lIusersll so that nonobviOl!JS 

problems in the data and true meanings can be determined; 

• ability to structure and maintain longitudinal data following individual persons or cases over time (perhaps severa'! months or years); and 

• feasibility of modifying the information system to meet data needs 

newly determined by planners and researchers. 

The data needs of any research or planning program cannot be totally 

met by any existing operationally based information system because those 

needs cannot be totally predefined in building the system. The nature of 

research and planning is such that new problems are always emergring for ana~ys!s, and little may be known about the factors that might explain the 

varlatlon under scrutiny. Until the variables are properly defined, 'it may 

may be necessary to collect a large amount of data about each individual "observation. II 

93 
Further, a sample of "observations" is all that is n~cessary.for planning purposes- rather than the processing o~ all ob~ervat10ns as .1~ the case 

of most operational systems. For example, lnformatlOn.o~ all cnm1nal court 

cases is essential in an operational system for the cr1mln~1 ~ourt, but only 

a representative sample of cases is needed to develop stat1~t1cs fo~ mo~t 

planning purposes. These are some of the arguments often g1ve~ to Just1fy special data collection eff)rts by planners, ~ut the ~forementl,?ned 

advantages of linking planning and research w1th ongo1~g Dperat10~ally 

based systems are too significant to.ig~ore. The remaln~e~ of th1s.C~apter 

will demonstrate, by example, why th1S 1S so: .To a~pre~1ate the ut~l,1~y 

of a system like PROMIS for analyzing the crlm1~a1 Ju~t~ce system, 1t 1S 

important to first develop an understanding of 1ts ut1l1ty to the prosecutor in solving his daily management problems. 

APPLICATIONS OF PROMIS 

Struggling to keep pace with a massi~e 'influx o~ cases, h~rdpressed 

prosecutors often work assembly-iine fash10n:' each 1S respon~1ble for 

cases at a given stage in the proceedings--at screening, arra1~n~ent, or 

trial. No one is in overall control of a case from start to f1nlsh. Re: 

sponsibility and control are fragmented. Case~ are lost through cracks 1n 

the system: files are misplaced; witnesses fa~l to appear; ~umero~s.continuances result in court dismissals. The hab1tual, court-w1se c~lm1nal 

buries his recidivism in the anonymity of large-scale, assembly-l1ne case 

processing. He seeks one delay and postponeme~t after.another un~il .the 

government's witnesses are so exasperated and lnconve~,enced, or thelr 

memories of the crime so obscured, that charges are e'~her dropped.o~ 

dismissed. If the case goes to trial, the prosecutor 1S often obl1~10US 

that there are other cases pending against the accused, or that he 1S a. 

fugitive, or that the seriousness of his current offense warranted spec1al pretrial preparation of the case. 

In addition to the problem of seasoned career c~iminals who see~ to 

manipulate the system to their advantage, there are 1nternal mana~erlal 

and operational problems. For example, police officers, ex~ert.w't~esses, 

and defense attorneys are scheduled to appear at the same t1me 1n d1fferent 

courts on different cases, with the court too often unaware that the.co~­

flicts exist until the day of trial. With massive and constantly sh1ft1ng 

calendars, case principals ~re not notified o~ expected cou~t appearanc~s.or 

of changes and cancellations. Analyses of eV1dence.by chem1sts, ha~dwr1t1ng 

experts, and other specialists are frequently unava1lable on t~e t~lal date 

because of the difficulty of scheduling, coordinating, and monltor1ng the 

completion of those activities for a large volume of cases. Furt~er~ore, 

the chief prosecutor is often in the dar~ about whet~er resu~ts w1~h1n the 

prosecutor's office are caused by subordlnates adherlng ~o h1S.pol1CY or 

departing from it. For example, since the reasons for d1s~ret1onary 

decisions by screening assistants are not recorded, the 7h1ef pr?secutor 

is unable to determine if refusals to prosecute are cons1ste~t w1th, or 

contrary to, office policy. Oft~n havin~ ?nly a matter ?f m1nutes to _ rev; ew a case before presenti ng 1 t, a tn a I pros~cutor d1 scovers that essen 

tial case documentation is missing or that notatlons.by attorneys who 

processed the case at previous stages of the prOCee?lngs are not clear. ~e 

is not sure if essential witnesses are present nor 1S he reasonably certa1n 

94 

about what aspe~ts of t~e case their testim~ny will address. He is also in 

the dark regard1~g Posslb~e pr?b~ems.with the case--such as those relating 

to search and ~el~ure or ~dent1f~catl0n--and he is unaware of prior defenserequested cont1nuances ana thus lS not in a position to deteY~)ine if another such defense request represents an abuse. 

. ~aving ~een put to the test for several years by the pros2cutor 1 s offlce 1n Wash1ngton, D.C., PR9MIS has proven that it can effectively address pl~blems such as those descr1bed above and significantly upgrade the per- formance of urban prosecution agencies. 

KEY MANAGERIAL GOALS THAT PROMIS HELPS PROSECUTORS ACHIEVE 

The.first manager~al 09jective that PROMIS assists ~/ith is'in helping prosecutlng attorneys ldentlfy the more serious cases from among the 

thousands that flow through the Washington prosecutor's office each year. For example, out of dozens of pending assault cases, some may involve 

defendants who are career criminals and who inflicted serious injury' 

other assault cases may involve first offenders who are perhaps guilty of 

only t~ch~ical v~olations of the law. PROMIS cuts through these difficulties 

by asslgnl~g rat1~gs to cases on the basis of data obtained by attorneys 

and arrestlng o-:flcers at th~ sc~eening stage. This information pertains (1) to the gravl ty of the Cflme 1 n terms of the amount of personal injury 

property damage or loss, and intimidation involved and (2) to the serious~ n~ss.of the ~ccused's criminal history based on prior arrests and con- Vlct1ons, allases used, and the like. 

Several days prior to the trial date assigned to a group of cases by the court, PROMIS prints out a copy of the court's calendar for that date 

but instead of listing the cases in the order the court will call them 

(oldest first, alp~abetical ?Y'~er, in ascending order by docket number), 

PROM!S ranks them.1n descend1ng order of their seriousness according to the g~avlty of the prlor record of the accused and the gravity of the crime. Hlgh:rank:d.cas~s are ~ssig~ed to a special team of attorneys, called the 

Speclal Lltlgatlon (MaJor Vlolators) Unit, which assures that such cases 

receive a s~perior degree of pretrial preparation. When a specially pre- pared case lS called by the court, the team delivers a detailed case workup to the courtroom prosecutor. In Washington, the conviction rate for 

cases receiving this special preparation was found to be 25 percent higher than that for those processed routinely. 

A second major managerial objective PROMIS addresses is the control 

and/or elimination of scheduling and logistical impediments that tend to blo~k adjudication of cases on their merits. To this end, PROMIS auto- matlca~ly produces subpoenas, witness and victim telephone lists, a pendingcase llst for any given witness, and notices for expert witnesses so that 

all parties concerned can be routinely informed of scheduled appearance 

dates. PROMIS keeps track of postponements of individual cases and notes ~long with the reasons thereof, whether the prosecution, defense or court 1S responsible. In addition, PROMIS: ' 

• automatically alerts the prosecutor when the accused has other 

cases pending against him; 

95 

J 

/1 

J 
• regularly produces lists of fugit~ves so that the cognizant la~ enforcement agencies can systemat1cally seek to apprehend them, 

• routinely prints lists of cases pending at various stages of prosecution for more than a specified number of days so that problems 

of delay can be resolved promptly. 

A third principal management objective PROMIS focuses ~n is the . monitoring and enforcing of evenhandedness and cons~stency 1n the exerC1se 

of prosecutorial discretion. The ch~ef pr?secutor :n a.large, urban agency 

must inevitably exercise his broad dlscretlonary.author1ty through many. 

assistant prosecutors. His problem, of course, l~ to assure that the dlScretion exercised by assistants reflects ~h~ cons1stent and e~enhan~ed application of office policies. Such pollc1es could govern dlscretlon in 

these areas: 

• The decision not to prosecute 

• The decision to upgrade, reduce, ~dd to,.or subtract from the 

charges recommended by the arrest1ng off1cers 

• The negotiation and acceptance of pleas 

• The decision to allow defendants entry into diversion programs 

• The decision to nolle prosequi or dismiss a case 

• The initiation, m' concurrence in, case postponements. 

To monitor and enforce the proper application ~f discretion in these 

areas, the related decisions must be visible; that 1S, they must be recorded 

and retrievable for subsequent review. Not only must the nature o! the 

discretionary action be recorded (e.g., case reject~d for prosecut1on~ but 1 

also the reason the action was taken (e.g., case re~ected bec~u~e of lllega 

search and seizure). Only when reaso~s for d~s~retlonary de~1s10ns are 

known can supervisory prosecutors be 1n a pos1t'iOn ~o dete:m1ne w~ether . subordinates. discretionary decisions reflect compl1ance w1th off1~e ~011CY· This can be accomplished with PROMIS, because it can gen~rate s~at1st1cs . on the reasons for several different types of prosecutor~al act1ons, rang1ng 

from modification of polic":! char'ges to requests for cont1nuances. 

Finally, the fourth major management objective address~d by P~OMIS pertains to the analysis of and research on, problems aSsoclate~ wlth the 

screening and prosecution of criminal cases. Many of the quest10ns that 

PROMIS is probing for the prosecutor·s of!ice in Washington, D.C., relate 

to or interface with, police and correctlonal concerns as well: The . utilization of PROMIS data for planning and research purposes w1ll be d1Scussed at length later. 

PROMIS DATA BASE 

PROMIS was developed by the United States Attorney·s Office for the 

District of Columbia to encourage new management techniques for.the proces- sing of cases.1 Detailed data are accessible from the PROMIS f1le so that 

96 

'i&J 

the prosecutor may be fully aware of the siatus of a case. Managers (senior 

prosecutors) may a 1 ':,'0 ~se, t~i s i nform~t; on to evaluate the perfonnance, demands, needs, and pnontles of the1r sections. In order to aid the pro~ecutors i, n thei r routi ne eval uati ons of case pri ority, the computer a~slgns scores tO,both the gravity of the crime and the defendant's criminal 

hlstory.2 The obJect.of these scores is to regu1ate the decisions of prosecutors so that a conslstent set of standards is applied in the evaluation of 

cases .. The data base that provides the unique source of information about 

the crlmlnal court process has been, and continues to be, generated as a by-produc~ ?f PROMIS. ,The P~OMIS data base contai ns approx'imate ly 50,000 

closed crl'~nnal ~ases lncludlng about 170 different types of information on 

each case lncludlng: 

• Information about the defendant: This includes name, a1ias, sex, 

race, date of birth, address, facts about prior arrests and convictions, employment status s and alcchol or drug abuse ' 

• Information about the crime: The date, time and place of the crime 

the number of persons involved in the crime, i;lnd informat"ion exbout ' 

the gravi ~y . of the crime in terms of the amount and degree of persona~ lnJur'y, property damage Or' loss, and intimidation--using the senousness scale developed by the criminologists, ~'arvin Wolfgang and Thorsten Sellin3 

• 

• 

Information about the arrest: The date, time, and place of the 

arrest; the type of arrest; and the identity of the arresting officer 

Information about criminal charges: The charges originally placed 

by the po~ice against the arrestee, the charges actually filed in 

court agalnst the defendant and the reasons for changes in the 

charges by the prosecutor, the penal statute for the charae, the 

F. B.1. Uniform Cri me Report Code for the charge, and the P~'oject 

SEARCH Code for the charge 

• Information about court events: The dates of every court event in 

a cas~ from.arra~g~ment through ~otion hearing, continuance hearing, ~nd flnal .dlSposlt1on t~ sent~nclng; the names of the principals 1nvolved 1n each event 1ncludlng the defense and prosecution attorneys and judge; the outcomes of the events; and the reasons for the 

outcomes 

• Information about victims and other witnesses: The names and 

addresses of all witnesses, the prosecutor's assessment of whether 

the witness is essential to the case or not, any indications of 

reluctance to testify on the part of the witness, a.nd other witness 

characteristics, such as whether he is related to the victim or 

defendant. 

. To make longitudinal analysis work. PROMIS employs a unique offender ldentifica~ion number assigned by the police and based on fingerprints and ~hotographl c checks. The uni que ID is carri ed on every case record, fo 11 owlng the offender through court processing in PROMIS. and even later in the 

corrections system. Whenever the offender is rearrested, that same ID is 

used. This enables tracking the offender oller time, identifying and 

97 
measurina recidivism, and analyzing relationships with recidivism. The 

unique offender identification numbers are used in.merging historical files 

of closed cases. Similarly unique numbers are asslgned to each court case 

and criminal event. The criminal event number is used to tie together 

codefendants and all matters related to a specific criminal incident. Thus, 

this code is useful in preventing the artificial inflation of crime and 

court caseload statistics that arise from assigning multiple charges and 

docket numbers pertaining to a single incident. 

PROMIS DATA INPUT 

About 80 percent of the data contained in PROMIS is "captured~ at the 

intake and screening stage as the byproduct of the case documentatlon 

process. Carbon copies of various forms completed immediately before or 

during the caS8 screening stage serve as input documents for PROMIS. As a 

case moves through the subsequent proceedings, additional information about 

its status is fed to PROMIS. This is achi~Ved through turnaround documentsforms generated by PROMIS in advance of a court event- on.which the results 

of a given proceeding (e.g., preliminary hearing, sentenclng, etc.) are 

recorded and then entered in PROMIS. 

Because of the extent of its acquired data, PROMIS dovetails very 

closely with the informational requirements of other criminal justice 

agencies and information systems. For example, PROMIS contains the vast 

majority of data needed by the Computerized Crimina1 4 Histories and . Offender-Based Transaction Statistics components of LEAA's Comprehens1ve 

Data Systems program. And, of course, the numerous facts store~ in 

PROMIS explain why it can contribute so significantly to the achleve~ent 

of the four major prosecutorial management objectives discussed.earl1er and 

why it 'is capable of generating the many types of reports descrlbed below. 

REPORTS PRODUCED BY PROMIS 

As imp'lemented in Washington, D.C., PROMIS g~nerates on a' \"ecurrin~ 

basis five categories of reports that are of partlcular value to supervlsory 

and trial prosecutors: misdemeanor calendars, felony calendars, case status 

reports, workload reports, and special reports. Many of these reports are 

of assistance to police as well. 

Misdemeanor Calendars 

These calendars assist management and trial prosecutors to process~ in 

a timely and orderly manner, serious misdemeanor cases scheduled for tr~al 

or sentencing. The calendars inform management of the ca~e workload, ald 

in the preparation of case documentation foy' trial, a~d identify ca~es 

weu'ranting special pretrial preparation. There are flVe types of mlSdemeanor calendars: 

1. Five-Day ~'isdE.'meanor Calendar. Listing all misdemeanor cases 

scheduled for trial five days hence, this calendar contains such information 

as the defendant's and codefendant~sname(s), case number, charges, arrest 

98 

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I 

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dat~, witnesses and their addresses and phone. numbers , arresting police 

offlcers, number and dates of continuances and reasons for them prosecutor's an~ defense attorney I s names, and r-ati ngs refl ecti ng the gravity of the 

cnme and of the defendant's criminal history. (See Figure 1.) 

2. One-Day Misdemeanor Calenaar. One day in advance of the trial 

date, PROMIS produces a calendar similar in content and format to that in 

Fi gure 1. 

3. Five-Day Misdemeanor Priority Calendar. This advance calendar ran~s upcoming cases according.to to their seriousness as determined by ~heH·. P~OMI~-comp~ted case ratmg. Thus the most "important cases are 

,dent,fled 1n a tlmely manner and receiVe special pretrial preparation by 

a team of attorneys called the Special Litigation (Major Violators) Unit. 

, 4. One:Day r'iisdemeanor Priority Calendar. The purpose, content, and 

format of thlS calendar is similar to Number 3 above. 

5. Mi sdemeanor Sentencing Calendar. Issued one day prior to the date 

of sen~encing, this c~lendar alerts prosecutors so they can prepare documentatlon for se~tenclng recommendations. (See Figure 2 for the content 

and format of thlS calendar.) 

Felony Calendars 

. PROMI~ gen2rates four types of felony calendars: (1) One-Day Prelimlnary H;a~'ng ,Calendar, (2) /'ive.-Day Felony Trial Calendar, (3) One-Day 

Felony Ina1 Calendar, and ~4} One-Day Felony Sentendng Calendar. The 

purpose,.content, and format.of the first three felony calendars are similar 

to the m1sdemeanorcalendar lllustrated in Figure 1; the felony sentencing calendar corresponds to Figure 2. 

Workload Reports 

The following reports allow management to nlonitor the progress of 

cases that are specially assigned to individual prosecutors for preparation and trial: 

1. Misdemeanor Specially Assi gned Cases. This report 1 ists, by a~torney, all misdemeanor cases assigned to the SpeCial Litigation (Major' 

y,olators) Unit, which gives intensive pretrial preparation to the most 

lmportant_cases. With this report, the chief of the Unit can assign cases 

on an equltable basis. The report contains the information for each case 

handled by a given prosecutor: judge, docket number, defendant's name) 

defense counsel, indictment dates, charges, witness data, and other pending 

cases against the defendant. 

2. Felony Specially Assigned Cases. This PROMIS-generated workload repor~ provi~es ea~h prosecutor with a listing of all pending felony cases 

to WhlCh he 1S asslgned. The report contains the same type of information 

as noted for Number 1 above. 

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3. Attorney Felony Case Workload by Type 'Of Charge. Listing the types cf felony charges by number and as a percentage of total caseload for each 

prosecutor, the report can assist the manager of a felony trial division in 

assigning new cases to prosecutors. 

Case Status Reports 

In addition to calendar and workload reports, PROMISgenerates four 

different summaries on the status of cases pending at various prosecution 

stages. A series of three reports lists defendants who have had bench 

warrants issued against them for failure to appear before the court as directed: 

1. Misdemeanor Fugitive List. Noting all misdemeanor cases involving outstanding bench warrants for defendants, the report furnishes to 'police information contained in PROMIS about any given fugitive-defendant. 

2. Pre-Indictment Felony Fugitive Listing. The cases of the fugitive- defendants in this listing are pending between screening and the grand jury stage. 

3. Post-Indictmer.t Felony Fugitive Listing. This report lists 

indicted fugitive-defendants whose cases are awaiting felony trial. 

These three fugitive listings contain the following information: defendant I s name; po 1i ce fi ngerpri nt-based ; denti fi ca ti on number; court docket 

number; police criminal complaint number; date the bench warrant was issued; 

the name of the judge issuing the warrant; charges; and'defendant's race, 

sex, date and place of birth, and home address. 

4. The fourth case status report lists all cases pending in the grand jury, oldest cases appearing first, and contains the following data for 

each defendant: case trlumber, defendant's name and police identification 

number, arrest date and date bound over to the grand jury, release status, 

defense counsel, arresting officer, charges, and other pending cases. 

All of these case status reports are of obvious importance to the 

prosecutor inasmuch as the cases remain at a standstill until the grand jury acts or the defendant is apprehended. 

Special Reports 

Comprising this fifth major category of PROMIS reports are Witness 

Subpoenas, Subpoena Sumnary Listing, and the Stat; sti cal Report. 

Special preprinted subpoena forms are generated to advise witnesses 

where and when to appear for a scheduled misdemeanor trial upon entry into 

PROMIS of a new or modified trial date. If PROMIS detects an error in the 

name or address of the witness (e.g., no street number) rr if there is 

insufficient time for the subpoena to reach the witness uy mail, this will 

be noted on a Subpoena Summary Listing. Thus alerted, the Washington, D.C., 

prosecutor's Witness Notification Unit, staffed prima~'ily by paralegals, 

... 

~ ........... ______________ 

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10_1 __________ ~ i 
---

 

telephones or otherwi se contac ts wi tnesses to whom subpoenos have not been 

issued" 

The Statistical' Report provides management with an O'verview of the 

case workload fcY}" a 9iven periO'd with regard to': 

e Screening- t~isdemeanor and felony cases considered, charged, 

rejected, or reduced; 

I Preliminary Hearings - Cases bound over, dismissed, aborted through 

a nolle prosequi action, or reduced; 

, Grand Jury- Cases indicted) ignored by the grand jury, dismissed, 

or referred to mi sdemeanor prosecuti.on; 

• Dispositions- Dispositions are separated into four groups- felony trials, unassigned misdemeanors, Special Litigation (Major Violators) 

and total misdemeanors. Within each of these four groups, the total 

cases are separated into types of outcomes (e.g., guilty, not guilty, 

dismissed, aborted through nolle prosequi); 

• Delays-- Average delays for cases disposed between arrest and 

indictment, bet~"'een indictment and disposition for feloniEs, and 

between arrest and di sposi t';on for mi sdemeanors; 

• Pending Cases- The number of pending cases at various points in 

the proseclJtot'i a 1 process; 

• Fugitives- The number of bench warrants issued and quashed during 

the per'jad and the number of pending fugitives at the end of the 

period. 

THE PRONIS RESEf~RCH PROJECT 

Recognizing the value of the PROMIS data base, the National Institute 

of Law Enforcement and Crimi nal JusticeLJ· funded a research grant to conduct 

anal,},ses in six topics that are of national interest. Before characterizing 

the' data needs formulated for this research pv'oject, the si.x topics vfill be 

outlined. These are: 

• Prosecution Per'fot'mance. The evaluation af prosecution performance 

is goTn~,rbeyond gtosS conviction rate statistics to examine such 

factors as the rates and associated reasons far declinations to 

prosecute and other court or prosecution terminations of prosecutions prior to trial. Also, patterns of plea negotiations will 

be examined so that conviction statistics are conditioned by the 

nature and gravity of the crime charged and the crime pled to, as 

well as the nature and gravity of the defendant's prior criminal 

record. Finally the performance of a public prosecutor will be 

scrutinized from the standpoint of consistency and evenhandness af 

discretionary decisions. 

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i Police/Prosecutor Relations. Traditional indicators of police 

system performanc~, such as clearance by arrest rates are inadequate. B.Y e~t~ndln~ the.measures of police performance (e.g., 

arrests survlvlng flrst Judicial review). the quality of police 

arrests.and o~her measures of police performance can be improved. 

The.proJect wl1l attempt ~o.learn about the relationships between 

pollce and prosecutor pollcles and decisions, and the performance 

measures. 

, Patterns of Criminal an~ Rela~ed Community Behavior. Expanding 

upon ~he work of other ~n~estl~ato~s, the relationship of criminal 

behavlor patter~s to.crlmlnal Justlce agencies is being explored. T~e effect of sltuatlonal variables (place of occurrence, familiar dls~a~ce between.adversaries, availability of weapons, etc.) on the d~cls:ons of pollce, p~ose~uto~s a~d jurists will be meas4red. 

Llkewlse, the g~ographlC dlstrlbutlon of victimization, crimes repor~ed and cr:mes prosecuted w'il1 be analyzed in studying patterns of crlme, behavlor of offenders, and citizens. 

• Pl~a.Barg~ini~g. Plea bargaining maintains its position in the cr~mln~~ Justlce proc~ss despite the clearly recognized jeopardy in 

wnlch It places the rlghts of the accused. This is largely a result 

of the fact that ~l~a b~rgaining is viewed as a necessary expedient. B~f?re ple~ bargalnl~g ~~ excised from the system of justice, emplrlcal eVlden(2 on l~S functioning will be examined. It is important to know whether lt 1S generally employed in an evenhanded man~er and whether the bargains are generally reasonable and fair. 

It ~s ~lso necessar~ to assess the likely effe~ts of curtailing or 

modlfYlng the practlce of plea bargaining. 

• Jud~cia~ Decision Making. The evaluation of judicial decision ~ak!n~ 1n large, u~ban court systems has generally concentrated on lnd~v~dual s~ntenclng records. To obtain a true picture of judicial 

declslon makln~. t~e ~v~luation will address composite empirical 

data on.al1 maJor JUdlCl~l .functions including the administration 

of contlnuances, the admlnlstration of pretrial hearings trials 

and sentenc i n9. ' , 

• S8eedy Trial: Court Delay and Defendant Flow. The National Advlsory Commission on Criminal Justice Standards and Goals has reco~ended maximum limits of 60 and 30 days respectively for felony and mlsdemeanor delay between arrest and trial. However before 

arbitrary 1 imits are imposed on court del ay, it would be 'well to 

examine empirical data on th2 causes of delay, on the nature and 

extent of delay, and on the effects that delay has on the rights 

of the acc~sed and on the public safety of the community (as 

expressed 1n the relationship of recidivism for example). 

Within these major topics, some examples of special analyses being undertaken are: evaluations of diversion programs, recidivism, 

gun cases, and the female offender. 

_______________________________ '.°.2 __________ .................. 1

8 

....... , .. ____________________ 10_3 __________ ___ -'*I C£&&£ 
PROMIS Research Methodology 

Before characterizing the data needs of the PROMIS Research project, 

it will be helpful to outline briefly the methodology being employed. A 

primary task of the research project is applying multivariate statistical 

analysis techniques to a research data base extracted on a random s~mp~e 

basis from the historical files (closed cases) of PROMIS. The statlstlcal 

analysis is concerned with testing theoretical models hypothesized to 

explain causal relationships between a dependent variable (sayan effectiveness measure such as conviction rate) and a set of independent variables, 

such as case and offender attributes, time in queue for courtrooms, and case 

processing actions. Some of the independent variables may represent discretionary actions (policy variables) on the part of the cri~inal justice 

agencies, and the idea is to determine what changes these agencles can make 

to improve effectiveness. 

A simulation model of the court system is being built to incorporate 

relationships and test changes suggested by the multivariate analysis. The 

model generates cases with attributes in accordance with patterns detected 

in an empirical analysis of the closed cases for a base year. IICasesll and 

offenders flow through a network of court proceedings and decision nodes. 

Equations developed through the multivariate analysis w~ll be used to set 

"branching ratios," i.e., the proportions of cases flow1ng out of a 

decision node along the alternate outcome paths. For example, based on 

the case attributes, the model will simulate the proportions of offenders 

being released and detained in jail as a result of bail decisions. Other 

examples of branching pOints are (1) cases p~stponed for ~arious rc:asons, 

and cases going to trial; and {2} trial acqultta1s and gUllty verdlCts .. Court resources will be considered. Monte Carlo techniques will be used 

to analyze queues and statistics will be gathered to measure system 

performance. 

Changes in policies and procedures on the part of police, prosecutor, 

and court components that appear to offer potential for improving criminal 

justice effectiveness will then be field tested under actual operating 

conditions using controlled experimental designs. Eva1uat~ons.of resu~ts 

will be made in coordination with a criminal justice coordlnatlng commlttee. 

PROMIS Research Data Needs 

The data needed by the PROMIS research project may be characterized 

as follows: 

1. 1 ongitudi na 1 ana lyses (tracki ng over several poi nts i 11 time) 

2. 

• case processing events from arrest through various court 

proceedings and final court disposition 

• offender based tracking including all pending cases against the same defendant, as well as the ability to track closed 

cases and pri or crimi na 1 hi story of the same defendant. 

cross sectional analysis (single time frame) 

• offender, victim, witness attri butes and rel atiollshi ps 

104 

.,,1 

• criminal event attributes and sericiusness scores 

• geographic distributions 

SOME EXAMPLES OF PRm~IS STATISTICAL ANALYSES 

Creation of Variables for Research Purposes 

. In resE.l,arch studies, one often attempts to analyze relationships among varlables. It may be necessary to construct a meaningful variable from 

several datu elements, depending on the nature of the study. An exalnple 

is provided by the witness cooperation study, recently conducted by the 

U.S. Attorlley·s Office in Washington, D.C.S 

Uncl~t PROMIS, the reasons that a case is rejected for prosecution or 

that a trial is delayed (by reason of continuance) to another time are 

listed by the prosecutor. Among these reasons for negative action in a case 

are several that involve witness cooperation in the prosecution of a case. 

For example, there are five witness-related reasons why a case may be dismissed at its initial screening by an Assistant U.S, .. I\ttorney. These are: 

the complaining witness signs off in writing (~he cnmp1aining witness does 

not want to prosecute}) the complaining witness does not show at the screening (and does not notify the officer as to why), th'~ complaining witness 

refuses to come to the screening, the witness is no~ consistent in his story 

(raising the issue of credibility). and the witness garbles his story (seems 

to be confused). In sum, these reasons may be defined as composing a IIwitne:ss problem area" for the prosecutor. These reasQ,ns may be defined as 

indicators of witness "noncooperation" at this stage in the case prosecution. 

Now it becomes possible to argue that witness behavior may be defined with 

respect to noncooperation. If we assume no (or few) cases are prosecuted 

in which the witness does not cooperate at the screening, we have created a 

variable. That is, we have created a quantitative or characteristic description of some phenomenon that possesses more than one value or category. A variable allows for measurement. 

The ability to create variables that make conceptual sense also is a 

condition necessary for the researcher to conduct an analysis of a given 

phenomenon. Using the variable created above--witness cooperation--the 

analytical question of why some people cooperate and why other people refuse 

to cooperate in the prosecution of a case can be asked. With the development of a management information system we get the information needed to 

make operational definitions which in turn allow for the creation of variables. These give us a basis on which we can clearly conceptualize and 

analytically measure the problem of witness noncooperation. 

Once variables are created. however. we must be sure that we are in 

fact.measuring what we claim to be measuring. Continuing with our example 

of wltness cooperation, this point is easily made. So far we have consider- e~ cooperation only from the eOint of view of the prosecutor, not the 

wltness. From the p~nt of Vlew of the w;tness~ cooperation may be perceived quite differently. For example, in the study of witnesses to common 

law crimes in the District of Columbia, it was found that in a field 

105 
interview several months after the fact, 78 percent of those labeled 

IInoncooperative" by the prosecutor realized that.th~y were witnesses~ and 

of this group 84 percent stated that they were wllllng to serve as w1tnesses,: 

Research Strategies 

An interesting example of how research methodology can be used in 

information system design occurred when considerati?n was be~ng given_to 

redesigning a form for use in P~OMIS. In for~s des:gn, one 1~ often faced 

vlith evaluating the cost of ask1ng more quest10ns (In ~he extreme, the 

respondent may refuse to fill out.too long a form) aga~nst.the val~e.of ~he 

information provided by the quest10ns. One way of.estlmatl~g the value 

of the information is to determine whether the vanables belng measured by 

the answers to the questions under consideration have any effect on the 

outcome of the cases. 

The new form under consideration was called the PROMIS Worksheet. It 

collects data used to compute "scores" for the seriousness of the crime and 

the recidivistic tendencies of the defendant, both of which m~y be subse- , quently used by the prosecutor as a basi~ for resour~e al~ocatlon. Th~ form, 

collects data on offender, victim, and w1tness relatl0nsh1p~ .. In ~art1cular,: 

it was redesigned to capture more data on witness ~~aracter1stlcs ~n the 

light of research findings in a previous study of wltness cooperatl0n. 

In the process of redesigning the PROMIS Worksheet, one of the issues 

that arose was whether a detailed set of questions should be asked o! the 

second most essential lay witness (in addition to asking these questlo~s of 

the primary victim and most,essential lay ~itne~s) .. While th~se quest10ns 

may produce useful informatlon, each questlonnalre lte~ ~hat ~s :omplet~d 

adds to the processing time and cost per case. In addltlon, lt 1S poss1ble 

that as the number of data elements grows, so will the recording error,and 

omission rates. In other words, would it be better to ask fewer questl0ns 

of more witnesses or many questions of fewer witnesses? 

In view of the several types of costs in obtaining information~ it was 

deemed desirable to try to determine what is gained with e~ch q~estl0n that 

is asked. For example, does the fact that the most essentlal w~tness has a 

physical or health problem affect the rate at which property ct1mes a~e ! 

successfully prosecuted? Is it useful even to have a new most essent1al 1~.! 

witness, much less ask questions of him? Issues of this sort can be addresse\l 

by applying appropridte statistical methods to the PROMIS data base. 

A first attempt at building a model of the value of witnesses to the 

prosecutor was the following: The probability of prosecutor succ~ss, fo~ " 

a q;ven type of case (e.g., stolen vehicle, aggravated assault), 1S a pnorh 

re1ated to the number of witnesses in the case. A measure o! pro~ecutor 

"success" was the conviction rate achieved in the cases stud1ed; 1.e., the 

proportion of guilty pleas and guilty verdicts to total ~ases a~cepted for 

prosecution. It is necessary to introduce Ilcontrol li vanables lnt? such a 

model; i.e., variables that are entered to ensure that we ~o not mlstakenly attribute to a particular independent variable X, systematlc effects upon 

106 

the dependent variable Y, that are in fact att~ibutable to other variables 

that are also determinants of X. 

After structuring sufficiently controlled regression models using alternatively as the dependent variable "no papering ll rate (cases filed 

by the police, but rejected at intake by the prosecutor) and I'conviction 

rate,JI it was possib1e to explore the effects of such independent variables 

as the number of witnesses and information gathered in questions about each 

witness. The results obtained seemed to indicate that each additional 

witness contributes noticeably to the conviction rate, even after as many 

as four or five witnesses have been named in the case. 

By partitioning th\~ total cases observE!d into the categories of 

"felonies and misdemeam»~s" and into the categories IIcrimes against persons" 

and "property crimes," it was possible to establish whether the ge,neral 

pattern also held for selected major categories of crime. The findings 

suggested that the marginal contribution of each witness is about the same 

for felonies as for misdemeanors, but gn:~ater for property crimes (i .e. ~ 

arson, extortion, burglary, laY'ceny, stolen vehicle, forgery, fraud, 

embezzlement, stolen property, or property damage) than for crimes against 

persons (i .e., homicide, kidnapping, sexual assault, robbery, or assault). 

Next, the issue "What information should be obtained from witnesses?" 

was examined. Fifteen of the twenty-one explanatory variables which were 

considered resulted from interrogating victims and witnesses, and only one 

of these fifteen was significant above the .10 level. The result was fewer 

significant variables than we would normally obtain at the .10 level simply 

by chance from fifteen variables that were absolutelY ~nrelated to the conviction rate. This casts some doubt on whether even the one variable found 

significant is, in fact, systematically related to the conviction rate. 

Thus, the results indicated that it was more important to obtain information on an additional witness rather than to ask more questions of fewer 

witnesses. The form was kept intact, but implemented on a sampling basis 

rather than for all cases. This was practical since the form was useful 

primarily for research purposes rather than operations. 

Use of Data Base as Basis for Sample Survey 

To conduct a field survey in the witness cooperation study, it was 

decided to use the PROMIS data base as the sample frame to select cases. 

To draw the sample from PROMIS, a file which contained only those cases 

eligible for inclusion in the sample was extr-acted from the PROMIS master 

file. This file contained 6,266 cases and of these, 3,170 had been rejected for prosecution either at screening or later in nolle prosequi actions or dismissals prior to trial by the U.S. Attorney1s Office. Of 

the cases rejected, 38 percent had been rejected for witness noncooperation. 

This subfile was then used to select a probability sample that would 

guarantee the inclusion of noncooperators for comparison with cooper'ators. 

107 
Cross Tabulations 

Even though research studies generally rely on statistical ana~ys~s. 

and deal in aggregates, it is important to record measurements for lndlvldwlls (names can be blocked out for privacy and security purposes as these 

are not needed for research). This capability is necessary to be ab~e to 

develop cross tabulations and regression models. For example, one ~lght 

want to study what happens to black male defendant~ (within a certa~n age 

group, who a.re unemployed at the time of ar~est) .wlth respect to ball 

decisions, pretrial detention? case process1ng tlme, and outcome. Such an 

analysis requires unique identification of individual offenders and the 

ability to relate their characteristics and cases. 

Passing the Entire Research File 

Sinte operational data systems are charact~rized b~ re~rieving and 

processing ane record at a time, on-line real tlme appllcatl0ns are popular 

in such systems . In research or p 1 anni ng app 1 i c~ti ons, o~e ~enera 11y needs 

to read the entire data file to develop the requlred statlstlcS. The 

research data base itself may be constructed by means 0: a random sample of 

records d}'Blfin from the operational data base. Alternat~vely~ the researcher 

m.ay {kaw his sample sil"lMiltaneously with readi ng the entl re fll e to generate s.tatisti cs. 

GeocodiD9 Applications 

The PROM1S data base contains street codes ~nd street addr~sses for 

the locatiori of the offense, the ane·s t, the reSl del1lce of the vn tnesses ~ . and the resi·dence of the defendant. for the analy~is, 0: patter~s of cnmlnal 

behavior .and citii.z.en i!1i\loivement, the research proJect l~ planmng geograph- '~cany I{lriented stwci'jes of victimizati?l1! offense report:ng, arrest, and 

prosecution patterns. Wha1t types of cltlZens: are mo~e 11 kely ~o re~ort 

CdIN?S .and cooperate in prosecution? What ci1aractenzes the sltuatlOns of 

';the '{} i ffel"ent types of crime? 

To accomp1:ish such studies., it is planned to link data records from a 

victinization survey conducted in Was.hington, D.C. by the Bureau of the 

Census for LEAA, ,a citizen opinion survey o~ crime con~ucted by the D:C. 

Office of Crimina.1 Justice Plans and Analysls, the pollee offense reports 

ta.pe, a crime ~ndex fHe, census tapes, social indicator fi1~s, and PROMIS. 

Each of these files contains data which can be accessed at dlfferent geo- q~"aphic leveh;. for this, geocodi 119 may ~e neces~ary to m~ke. t~e r~cords compatib1e. for exam;:lle, the level of deta.il to' winch the vlctlmlzatlon survey ~e$ults can be made available will be census tract level at best ~ue to the 

sjze of the sample. In orner to relate PRot·nS data records on cnmes pro~­

·£!£utedin a partkula.r census tract J' it wil 1 be necessary to ~se a geocodl ng pr'~$raM such as the Bureau of Census 11 AOHATcHli t~ ma.tc~ certal n street ad1,'I"esses itn PHCHlS records against an address co€:hll1g gUlde prep~red by. the 

,VetmvoH ~>ln Wa,s-hington Council of Governments, Once a match 1 s Qbtal ne~, 

t.,ri4'! r.Q'I"reo:;pof,tjing census tract codf! can be a.ccessed from the ~ddress Codlng 

':l'.-9idte afi~$ ';:;HJ~nded to the PROPHS rf:cf"H"d for subsequent analysls. 

lOB 

Improvements in PROMIS 

Recently, a new version of PROMIS has been implemented. Whenever 

a systems change of this magnitude is made, it will have an effect on ~esearch projects that require historical data spanning several years. It 

1S necessary for the researchers and planners to analyze any changes in 

data elements and make sure they can establish equivalencies between old 

and new definitions and codes. Similarly, it is important to understand 

new data items and discontinued data items. Any statistical interpretation 

of longitudinal data covering data collected under the old and new systems must consider system differences. 

The new Promis deSign resulted largely from research appljcations. Under the old PROMIS, each charge in the case was placed in a separ~te trailer record. Actions, such as continuances, also formed traiier records 

reqated to each charge. There were a varying number of charges per case 

and a variable number of actions per charge. Thus, it was a little ~umber~ some to determine the current status of the case. The file organization 

was acceptable for operational purposes since the processing programs were 

tailored to the file organization. For research purposes, it is desirable 

to be able to apply standard statistical packages, such as Statistical 

Programs for the Social Sciences (SPSS), Harvard's Data-Text, or UCLA's 

Biomed systems. Moreover, such data record formats are difficult to process. For example, the statistical packages cannot handle a varying number 

of trailer records. The new PROMIS provides a case header record which 

contains ~ase status and summary data such as current continued date, case 

dispOSition, number of continuances, and number of witnesses. Many research 

data needs can be met by extracting those case summary records. There are 

still some variable length records in the file (charges, witnesses) but a 

special program has been written to analyze the file and develop fixed 

length formats for application of. the statistical packages. 

Another problem under the old operational system was that the court 

assigned a new case number to a felony case after indictment. For operational purposes, it sufficed to track by the new case number. For statistical purposes, it was desired to show thiJ as one case under the new 

number in order to allow tracking all events in that case from arrest through final court dispOSition. This merging of the data into one case record has 

been accomplished under the new PROMIS. In addition, numeric codes are used 

in the new PROMIS in lieu of alphanumeric codes to facilitate the application of standard statistical analysis packages. 

Conclusions 

What can be concluded from the experiences in utilizing PRor-nS for 

research and planning purposes? The conclusions may be sunllla.rtzed as fOllows: 

• The PROMIS data base has been useful as a sample frame 

for designing field sUl'veys and in-depth resean~h studies. 

Data from the operationa'! data. base has been effect;v.ely 

l'j nked with survey data. 

109 
• When certain data items required by planners and researchers 

were weak in reliability or missing, forms were modified and 

procecures installed for gathering the required data. 

• Multiple regression techniques were effectively used to 

analyze the criminal-justice system and to test forms design alternatives by showing how certain data items contributed 

to criminal justice system performance. 

• Using the data for research purposes has pinpointed data inaccuracies and inconsistencies and needed improvements in file 

structure. It also has sharpened the user's understanding of 

the importance of certain data and procedures, such as longi- tUdinal data and those data measuring witness cooperation. 

• The planning agency for the District of Columbia has utilized 

statistics drawn from the PROMIS data base in developing the 

annual Comprehensive Plan for criminal justice programs, for 

progress evaluation during the year, and for special research 

studies. 

Effective communication among planning, research, information systems 

and operational personnel has benefited the research, the planning, the 

information system, and office policy and operations to a greater extent 

than could be accomplished by isolated efforts. Only through use of an 

information system can that system be validated anG refined. ---

------------------------------------------------------------------

NOTES 

1 For a complete discussion of the development of PROMIS, see William A. 

Hamilton and Charles R. Work, liThe Prosecutor's Role in the Urban Court 

System: The Case for Management Consciousness ,II Journal of Criminal Law 

and Criminology. Vol. 64, ~lo- 2 (June 1973), 183-189. 

~ The gravity of the crime is scored according to a modified version of the 

Sellin-Wolfgang seriousness index (see Thorsten Sellin and Marvin E. 

Wolfgang, The Measurement of Delinquency [New York: John Wiley, 1964J). 

The defendant's criminal history is evaluated according to a scoring technique that reflects the IIbase expectancy II scoring system developed by 

Donald Gottfredson (see Donald Gottfredson and Jack A. Bonds, A Manual for 

l!)take Base Expectanc.¥~ Scori n9. ~pril 1, 1961 (Form CDC. - BEGLIA), 

Sacramento, Ca 1 ifornia: Cal iform a Department of Correctl ons, Research 

Division). 

110 

~. Ibid. 

i The PROMIS Research project· f d d 

by the National Institute of L~~ E~~o~ce~~~~ra~~a~t.N? ~4~NI-~9-0008-G Enforcement Ass; stance Admi ni s tration U C" 0' . t nm1 na Just~ce, Law 

ton, D.C. ) • .). epar ment of JUstlce, Washingt: 

.:=.. IIA Study of Witness Cooperation in the District of ('olumb' It 

Cannaval e, Jr., Institute for taw and Sod 1 R h v 1 la ~ Dr. F .. Street, N.W., Washing+'on 0 C • fOl"thcom' a esearc, 125 Flfteenth ~ " . ., • ' 1 ng . 

6 

-( U.S: Department of Commerce, Census Use Study, ADMATCH User1s 

Washlngton, D.C.: Bureau of the Census, 1970). - Manual, 

111 
INTRODUCTION 

CHAPTER 11 

STATISTICAL TECHNIQUES AND THEIR LIMITATIONS 

IN THE ANALYSIS 

OF CRIMINAL JUSTICE DATA 

Brian Forst 

Many crucial questions in the criminal justice system that have been 

addressed previolflsly by means of conjecture and anecdote are now bt':!ing confronted factually and systematically. Some examples: 

• What effect does the pretrial release decision have on whether 

a defendant is convicted? 

• What effect does the use of a diversionary program have on 

whether a defendant recidivates? 

• To what extent does delay in court processing increase the 

likelihood that a witness problem will develop in a case? 

I How many homicides are deterred, on the average, with each 

execution of a convicted murderer? 

The fundamenta'ls of the scientific method,'·-constract a theory, collect 

pertinent data, subject the theory to empirical validation using the 

data, and compare the findings with previous work--still form the basis 

for addressing such questions. However, the specific tools used in 

carrying out thi, research have progressed dramatically. Many of these 

improvements have taken place since the 1950's, following the advent 

of high-speed, large-capacity computer technology. Comp1icated data 

manipulations that were totally infeasible using a calculator, scratch 

pads, and sharp pencils are now commonplace. 

These developments in quantitative research have p'!aced new burdens 

on policy setters in the cdminal justice system. Police officials, 

lawyers, court administrators, judges, corrections officials, and lawmakers are being confronted more frequently than ever with elegant theories 

formulated in cryptic mathematical language and tested on the basis of 

formidable sounding statistical criteria (e.g., the ratio of the regression coefficient to its standard error to test for part-ial effect, the coefficient of determination adjusted for degrees of freedom to test for 

goodness-of-fit, the likelihood ratio chi-square to test for statistical 

interactions, and so on). In this Ch~pter we attempt to cut through such 

esoteric jargon and describe in simple terms the problems involved in 

analyzing data, the basic techniques for dealing with each problem, and the 

limitations of these techniques. 

113 

j 
UNIVARIATE. ANI) r~ULTIVARIATE STATISTICS 

Cons~~~~ th~e i~!~e d~~C~~~:y t~~/~~e~;~~n~'~ ;~~~~/~~ ~~: ~~~~~c~o10f process c. ..t .... tics or we can concern ourse ves 

~~{h o~~:r r:~~~~~n~~~~~ ~~~~:~~ai~e 5 \I:~~~~ ~e ~ca~~/~~~~!s~~g t~!m:~t a~~ ~~~~~r~:~;o~~a~~!~1c~~1~~~~~~ai~atS;~ ~~l~~~~riate statistics, remains 

today essentially as it was a century ago. 

Univariate Statistics 

Univariate statisti~s a~e useful in pro~~~!n~t~:~~~e~h;h~;u~~s~~ibe the way that cas~s arelddlst~l~~t~~c~v:rd;~~ribution (for convenience, based 

process them. Flgure ep1c 

on imaginary rather than real data): 

cx: Ul 

WW 

ro Ul 

~5 ;:)tL 

GO 

o 

~ __ r_4-~'~$~#--~~ I 

50 

PROCESSING TIME 

lin days) 

Figure 1. DISTRIBUTION OF CASES OVER TIME 

100 

. ost ictures Figure 1 describes case processing time better 

~~r~~~! ~:m~: ~~~! ~ ~o~~~~~!~~ ~~r~! !~~;. ~~ l~e~~~~~~t~~~ z~ i ~~~i b~~!~~e . ~f ca~e rocessing time in terms of certaln statlstlcal measures.. ne 

might sa~, for exampled' that moreb ~a~~ys 1~~~~ i~: ~~~~:~ ~~ ;~: ~!~~t or 

day than on any other ay--more ne, . d m re briefly that half the cases are processed within th~ flr~t ~5 ays-- 0 ) number' 

that the "medianii is 45 da~s ;50~r tha;h~~e t~l~e~~ st~~ b~ti o~v~~a~~Olymod~ 1, II 

of days to process a case 1S ,or ~ . ) 30 

with peaks at days one (due to rejections of cases at screenlng , 

114 

.. .n"......--____ ------

(the mode number of days to mi sdemeanor trials), and 80 (the mode numbey· of 

days to felony trials); or that the °range,1I a measure of the total 

dispersion or spread of the distribution, is 150 days. 

Such measures are useful for making definitive quantitative comparisons of case processing times in one jurisdiction with those in another. 

These statistics also serve as important components of multivariate 

analysis, which will occupy the remainder of this Chapter. 

~ultivariate Statistics 

Of mote policy relevance than the univariate characteristics of 

case processing time are the multivariate relationships between case 

processing time and other factors. For example, if the mean cas~ processing time for felony cases increased from 70 to a 100 days, what would 

happen to the percentage of cases that go to trial? Or, if witnesses 

were paid a sum of money to appear in court so that continuances were 

reduced by, say, 30 percent, what would happen to the mean case processing 

time? We can see that it is useful to know both how cgse processing 

time affects other factors (e.g., witness cooperativeness, convictions, 

and recidivism), and what factors influence case processing time (e.g., 

experience of the prosecutor, defense counsel, and judge; existence of 

tangible evidence; and degl'~ee to which the defendant is a II career 

criminal!!). In the forme.Y· case~ we would be looking at case processing time as a cause. In the latter case, we would be looking at case 

processing time as f.n effect. Such issues as these can be addressed 

using tools of multivariate statistical analysis. 

EXPERIMENTAL AND NONEXPERIMENTAL DATA 

One way to learn about causal relationships is to conduct a controlled 

experiment. Be(.~ause of ethical and legal considerations, however, 

controlled experiments have extremely limited appropriateness in the 

criminal justice system. But we can gai~ insights into causal relationships by applying multivariate techniques to nonex erimental data (i.e., 

data that accumulate in the normal course of operations. Nonexperimental data are becoming more abundant in the criminal justice system. particu- larly due to the growth of automated record keeping. 

The challenge in trying to draw inferences about causality from nonexperimental data stems from the fact that there'are, typically, countless 

hypothetical causal relatiorships one could dream up that could hnve pro- duced the resulting data. It is by no means certain that we \-,i11 identify 

the true set of causal relationships. Even if we do iden;l;ify this set for 

consideration, we may still incorrectly conclude that some other set is 

the true set, due to measurement errors, the omission of important factors 

from the data, or random errors that result from having too few observations 

for the analysis. 

115 

) 
CAUSAL RELATIONSHIPS AND STATISTICAL ASSOCIATIONS BETWEEN TWO FACTORS 

Statistical techniques are well s~ited for revealing the existence 

of an association between two factors. Measures such as the Itcorrelation 

coefficient" are cOO11lonly used to reflect the strength of this association. 

Unfortunately, the simple statistical relationship between two variables 

is so often misleading that it is generally unwise to take it very 

seriously as an indication of a true causal relationship. 

An example based on real data will serve to highlight the danger 

of drawing inferences about the causal relationship between two factors 

based on a simple statistical association between the two. It has been 

found that felony arrests are 8.7 percent more likely to be accepted for 

prosecution when the victim is a business, ignoring every other factor. 

Is it appropriate to conclude from this statistic that the prosecutor is favoring businesses over private citizens? No, since it is easier 

for the prosecutor to obtain convictions for felony arrests when stolen 

property ;s recovera~ or when there are witnesses, and those cases involving 

victims that are businesses are more likely to have recovered property and witnesses than other cases. In fact, when these and several other 

additional factors are included in the analysis, the observed statistical 

association between the factors "victim is a business" and "case is 

accepted for prosecution" disappears. Hence, it is reasonable to conclude 

that the simple two-variable relationship observed above is spurious. 

A statistical association between two variables is called a simple 

relationship when. it ignores every other factor, and a partial Y·elationsh..i.2.. 

when it takes explicit account of other factors. For the example just described, the simple relationship between the factors "case is accepted 

for prosecution" and "victim is a business" was significantly positive, but 

the partial relationship observed was not. 

Another example of a spurious statistical relationship that has been 

found in criminal justice data is one between the defendant's attorney 

and the outcome of the case. When every other factor was ignored, it 

appeared that cases handled by public defenders were more likely to wind up 

as a conviction against the defendant than cases handled by other def~nse 

lawyers. Does this mean that public defenders do not serve their clients as 

well as other defense counselors? Not at all. In fact, in the jurisdiction 

studied, the opposite appears nearer to the truth. The public defender was 

found to take a dispropottionate number of cases which involved serious 

crimes (Le., involving weapons, personal injury, etc.) and defendants with 

extensive arrest records. The findings indicate that these types of cases 

are more difficult to defend. After taking account of these and 1ther 

pertinent factors, the statistical indicators suggested that public defenders 

are mo!'e likely to get their clients "off of tne hook" than othe.r counselors. 

In the jargon,:'while the ~imple relationship observed between the factors. 

"conviction" aY}(1 "publ ic defender" was weakly positive, the partial relat1onship observed ~as significantly negative. 

116 

, 

TECHNIQUES FOR TAKING ACCOUNT OF OTHER FACTORS 

How do we take account of other f t ~ , experimental data? The sim lest ,ac o.s ln the analysis of nonbased on the factors that c~~se waY.ls throu~h cr?ss classification, simple relationshi s part~al.relatlonshlPs to differ from 

favors businesses,Po~e ~~~l~X~~~;:;el~a lnve~tig~t~ng whe~her th: p~osecutor other types of cases fo d'ff . ses lnvo vlng buslness vlctlms and 

f~~o~~~~gec~{ion) afterd~iv~di~~e~~~Sc~~e;h~n~~r~~~~a~~t~;o~~~~~ts(f)c~h;~~ nesses an recovered property (2) th ' l' , bu t no recovered property (1) th '. 1'. ose 1 nvo v 1 ng Wl tnesses 

witnesses, and (4) tho ~. . ose ~ nvo Vl ~g recovered property but no 

Similarly, in the exam~~el~~O~~~~;z~~~t~~~ w~ine~~es ~or Y'ecover~d property. 

~~~e~i~~~e~o~~~~~ ~~~~~fiyi~~gr~ytesthebetwe~n ~hee~a~~~nt~~yO~a~~~~:~n~e~~~~~rs, t . serlousness of the case and the 

ex enSlveness of the defendant's arrest record. 

Comparing percentages between var" t . . 

in the preceding paragraph, is the ba ~ou~ ca ~~orles~ 1n the.ma~ner suggested known as contingency table anal s1 slc,lngre l:nt of a ~tatlstlcal technique 

to infer whether differences belwe!~ pThlS ~echnlqUe provJdes an opportun'ity 

are real or simply due to an insuff' .ercen ages of selected characteristics 

into account other factors that mayl~~~~~tn~~be~.~; observations, after taking The test for inferrin Ih th e 1 erence, as noted above. 

to qualify as one thaf ~sere~~ ~~~h~~s~~ved didffer~nc~s is sufficiently large statistic. ,an ran om lS oased on the chi square 

A commonly used alternative to t' regression analysis or sim 1 II con.lngency t~ble analysis is multiple 

th~ factors that ca~se part~ar r;~~~~~~~~~p~n~lY~~~fll ~n reg~ession ana~ysis, Shlps.can be accounted for eiV)er by using th ~ ; ;r rom slmple ~elatlOn< groUplng the observations into cat a' ese ac~ors as the basls for ~ncorporating them as independent ~a~~~~~e;o(?e ana~~zed s~parately,. or by 

~~m~~~ ~~~~~~i~. Regre~s!on analY~i~ provide~'~'basi~ ~~~ ~~~!~~~nf~~t~rs) that a change i~eo~~g~:~~~~nhcoefflclent, which is an estimate of the effect 

been acounted for statistical~y.on another factor, after other factors have 

, We can ~et ~ general sense of how regression analysis works by retuY'n- lng to our tlme 1n system example A ' l' , e:-fect of case processing tim~ on'the n~~l~~tl~ :-ehgtreSsion an~lysis of th~ mlght produce a . , . 00 t a a case w111 go to tnal 

each additional ~~~~~s~;o~ foefflclent of -.01, which would suggest that 

trial b e ay, on ~ve!'age~ reduces the probability of 

ex ect ihone percent .. We ~egard thlS example as simplistic because we 

ofPtrial ~t thel~e1atlo~shlP between case processing time and likelihood 

two month~st~e~hr~en~~~~~ea~; that is, while t~e. one month increase from 

s 0 process a case mlght cause the probability 

117 
of a trial to decline from .15 to .10, an increase from seven months 

to eight months might increase the probability from .08 to .09. 

More powerful (and more complicated) versions of regression analysis are 

capable of dealing with such problems. ' 

In both contingency table analysis and regression analysis, the lIother factors accounted for" are called control variables, so named 

because it is the absence of a controlled experiment that requires another 

means of statistical control. 

An important problem in controlling for other factors consists of 

determining which factors are the most important to control for. This 

problem is generally addressed in two ways; theoretically (Which variables 

seem to be the most important to take account of?) and empirically (Which 

set of control variables provides the most accurate estimateS?). Since the 

empirical approach is the more objective of the two, it is genera11y the 

preferred approach, although bizarre empirical results that defy a reasonable 

theoretical explanation should not necessarily be taken as correct. Both 

contingency table analysis and regression analysis provide objective methods 

for determining the "best" set of control variables. These go beyond the 

scope of this Chapter, but the reader can consult sources cited at the end 

of the Chapter for specifics. In any case, the mere existence of control 

variables in an analysis of causal relationships, without a reasonable 

justifiction for that particular selection of control variables out of a 

larger set of variables, is a very bad practice that should arouse the 

reader's suspicion. 

POINT ESTIMATION AND HYPOTHESIS TESTING 

There are two basic modes of statistical inference--point estimation 

and h1pothesis testing. In analyzing relationships among two factors, the 

former is used to estimate the desree of relationship between the factors. 

The previous discussion about estlmating the average effect of a change 

in case processing time on the likelihood of trial is an example of point 

estimation. 

Hypothesis testing, on the other hand, is used in analyzing causal 

effects to infer from a samp1e whether a relationship exists between two 

factors. In hypothesis testing, we might say, for example, that if case 

processing time and the likelihood that a case goes to trial were, in fact, 

absolutely unrelated, the probability of obtaining a statistical association 

at least as close as we have obtained is .03. If we regarded .03 as being 

suff-iciently small, we would reject the hypothesis that the two variables 

are unrelated", The determination of what constitutes a"sufficiently" small 

probability to cause a rejection of the hypothesis is totally arbitrary~ 

although the numbers .01 and .05 have become popular, for reasons that 

are not campletely clear. If we accept the proposition that any two 

118 

I 

~,\ .~' 

variab~es are at least remotely rel d ,'", then glven an arbitrarily high leve~t~f' ~ow~v~r ,nflnltesimallyso, 

sample will always causi us to dec'd thslgnlflcan~e. a,large enough 

1 e at a relatl0nshlp exists! 

Point 'estimation and hypothe . . are combined. Either used alohe ~lS t~~tlng are most useful when they 

estim~te ~ays nothing about the re~i!~il~~ of much ~a~ue. The point that 1S, 1t says nothing about the h £ or p~ec~s10n of the estimate; 

of the same size will produce a Sim~l:~ces t~at d dlffere~t r~ndom sample ~he,other hand, says nothing about the v~esu t. T~e hYP?cheS1S test, on 

It 1S of little value to know on1 that lue that lS e~tlmated. For example, 

percentage of cases won by publicYd f dthe observed dlfference between the ~efense lawyers is significant at e en e~s.and the percentage won by other 

interested in knowing what the d'f'fsay , . ~, presumably, one would. be more 

1 erence 1S, 

COMPLICATED RELATIONSHIPS 

If we c?nsider again the matter of an l' ~ .. case proCesslng time on the likelih d ih a YZ1ng tHe~ffect of changes in 

we see yet another roblem' 00 trlat t~e case wll1 go to trial 

variables is likelyPto ru " the causal ,rela~10nship between these tw~ 

in the likelihood of tria~ ~~g~~etopp~~~te d1rection as well. Changes In particular, the weakest o,a ect cas~ pr?cessing time, too. s~em unlikely to be in th~ ~a~~!~ ~~;h small llkellhood of trial, would 

l,kely to leave the system a~ leas lon~. Ve~y strong,cases may be more 

to be the fairly strong cases ~hat sOf ~Ulltl pnor,to tnal. It would seem urVlve onger 1n the court. 

. It is appropriate, but rare to t k ~n analyzing a particular causal'rel t~ e f~count of such confounding effects 

interested in one phase of a -, a lons 11 p. If one were p~'imarily 

factors, a mis1eading conclus~~~p~~~r~e~r~latio~shiP between two or more 

separate the major components of the f l~ rawn,f the analys'is fails to 

u set of causal relationships. 

There are two basic types of l' , recursivity. Circular rel' t' .comp lcatlons: circularity and 

~ng time and the likelihoo~ ~~n~~~~f' ~u~~ a~ that between case processlnvolved in the circle. Recursive ~'1~' sh~rt ~nd end with each factor 

causally related factors, with one a~fa l?nS lpS 1nvol~e a chain of 

manner. An example of a '. eC~1ng ~ne next In a noncircular ex~erience of an arrestin~e~~~~~~eo~~~atl0ns~lp is that between the 

eVldence obtained in a case, and the ~~~ri'~ edamount a~d guality of su~ably. the first of these affects . 1 e 1 00 of conv'/ctlon. Pre- ~~lrd, and any other direct rela ' th~ second. the second affects the 

Ilkely to be negligible Circult10nShlp.amon~ these three variables is 

analytic problems than ~ecw~sive a~n~~~atlOnsh'ps generally pose greater' 

119 
It should not be a surprise that the techniques for dealing with 

such problems are as complicated as the relat~onships themselves: 

These techniques have been developed mostly Slnce 1960 by economlsts 

who have greatly advanced the techniques of regression analysis and 

by sociologists (in particular, Leo A. Goodman, University of Chic~go) 

who have made contingency table analysis more powerful. Further dlScuss ion of these techniques is beyond the scope of this Chapter. 

HOW TO CHOOSE BETWEEN STATISTICAL TECHNIQUES 

The array of multivariate statistica1 methods is bewildering. 

Not only are there various techniques within conting~ncy.table analysis 

and regression analysis; there are also related multlvarlate tech- . niques such,as factor,analysis, the pr~ncip~l c~m~onents meth~d, multlple classificatlon analysls, cluster analysls, dlscrlmlnant analysls, 

and canonical correlation analysis. 

Incredibly enough, the choice of one parti~ular technique over 

another is often as arbitrarily made as the cholce of a level for 

rejecting an hypothesis. Unlike the choice of a level of signific~nce, 

however there are objective criteria for selecting among alternatlve techniq~es of multivariate analysiS. The important questions to be 

answered are 

• How accurately does each technique predict or describe 

the phenomenon in question? 

• How sensitive (or "robust") are the results to various 

modifications within each technique? 

One way to assess the accuracy of each technique is to use a 

split sample validation procedure. Under this procedure, part of the 

observations are used to estimate the relationships among factors, 

and the remainder are used to measure how closely each technique (and 

each variation or model within each technique) predicts the true values 

of the phenomenon being described. A standard measure of predictive accuracy is a statistic called the IImean square error. at 

IIRobustnessll is determined by examining each reasonable modification 

of the analysis to see how sensitive the results are to each modification. 

Modifications typically consist of the use of linear and nonlinear 

assumptions, the use of alternative sets of control variables, and the 

inclusion and exclusion of "outliers" (i.e., observations that are so 

unusual as to be of questionable validity). When the results are sensitive to such modifications. it is appropriate to investigate which modification ;s the most accurate and to doubt whether any is accurate. 

120 

I It i~, .pe~haps~ mo~t common to select a technique on the basis of 

one s.faml~larlty wlth It.O~ t~e r~alism of the assumptions on which the 

techmgue lS fra~ed, Faml11anty 15 obviously an inferior c)'iterion for sel~c~ln~ a p~rtlcular technique j for much the same reason that one's 

famll1anty ~lth a hammer woul~ be a poor rationale for using only a 

hammer to bUll~ a h~use .. Rea~ 1sm of the a.ssumptions of a technique may 

be an equall~ lnferlor crl t erlon for selecting or rejecting a technique. 

If one.te~hnlque th~t happens to be based on supposedly restrictive or 

unreallstlc assumptlons conSistently predicts or describes a phenomenon 

more accura~ely and efficiently than every other technique, it is difficult 

to see why ltS results should be ignored. 

SOURCES OF FURTHER INFORMATION 

The following ~ooks ar~ recommended for further reading both for 

general background lnformatlon and specific topics, as indicated in the titl es: 

Hubert M. Blalock, Jr., Social Statistics (New York: MCGraW-Hill, 1972). 

James A. Davis, "Multivariate Contingency Tables: An ExegeSis of 

Goodman's Recent Papers~" in Soci..2l.2..gjcal Methodologl~ edited by 

Herbert Costner. (San Franciscr.: Jossey-Bass, 1974)~ pp. 189-231, 

N.R. D~aper and H. Smith, Applied Regression Analysis (New York: John Wl 1 ey & Sons, (966). .. 

J, Freund, Modern Elementary Statistics (Englewood Cliffs M J. Prent';",,,.- Ha 1i. 1967). ~ IV. •• • .... , 

William J. Goode and Paul K. Hatt~ Methods in Social Research (New York: 

MCGraw-Hill, 1952). -

Donald F. Morrison, Multivariate Statistical Methods (New York: McGraw- Hill, 1 96 7) . . 

Sidney Sie~el, NonparametY'ic Statistics (New York: McGraw-Hill,1956), especlal~y ~ag~s ~95-202 for a discussion of the use of the chi 

sguare statl~tlc In analyzing relationships among factors in con- tlngency tables, . 

!-fern'; Theil, Pri~ciples of Econometrics (NeItJ York: ~lohn tlji1ey & Sons, 

1?7l1 especlally pages 429-539 for a discussion of the analYSis of 

clrcular and recursive relationships among variables. 

Hans Zeisel, Say It with Figures (Ne~';f York: Harper & Row, 1968). 

121 
I 

CHAPTER 12 

DATA RELIABILITY AND DATA PURIFICATION 

Joyce Deroy 

INTRODUCTION 

Before applying sophisticated analytical techniques to any data base, 

it is important to understand what the data are ~easuring and whether 

the data are sufficiently accurate to be useful in analysis. One way 

of detecting erroneous data is through automated data editing and 

verification techniques. Any errors detected through thi~ orocess can 

be listed for manual correction by checking against the contents of the 

physical source data forms, such as those in court case jackets. This is 

most effectively accomplished as part of an ongoing operational process 

where errors are corrected during the current period's processing cycle. If this is not possible, either through the lack of extensive automated 

verification techniques or clerical personnel shortages, then the task 

has to be accomplished with a sampling of historical files. Thus it is 

important for research purposes to maintain the historical computer files 

and the closed court case files in a form where both can be sampled in 

conjunction with each other. 

TYPES OF ERRORS 

The experiences in using PROMIS (see Chapter 9 ) are useful for 

i 11 ustrative purposes. PROMIS was originally design,ed in 1970 to sati sfy primarily operational needs, but secondarily research needs. As a result of experience in both operational and statistical applications, PROMIS was redesigned, reprogrammed and implemented. The new PROMIS contains much more extensive automated data editing and error detection pro- cedures. A number of forms and action codes were st:eamlined. In addition, several record format improvements were made to simplify analysis. 

Naturally, for longitudinal analysis, it is important to track 

offenders and cases over time. A research project estab1ished to 

utilize historical PROMIS records had to assure that the codes used 

corresponded to comparable actions. Data records processed under the 

old PROMIS had to be transformed into a comparable format with the new 

PROMIS. Also, the data editing and verification conditions had to be 

made consistent. 

A computer program was prepared to convert the "old" PROMIS records 

to simulated input transactions in the new PROMIS data format. These 

transactions were then used as input to the new PROMIS data edit and 

verification program. This program structured master file records in the 

new format for "correct" transactions, and rejected and listed erroneous 

transactions for manual error correction. The types of errors detected 

123 
by the new PROMIS include omissions,.invalid.co~es and incons;stenc~es, 

such as the date of the trial precedlng the lndlctment. Errors, Whl~h 

appeared to be conveniently correctab1e by computer,.were corrected 1n 

a subsequent run. Statistics were also run ?ff.s~owlng the erro~ rates 

and the pattern of errors to assess data rellablllty for develoPlng descriptive statistics. 

In some projects, data errors wi~l be randomly distrib~ted over. 

the observations that form the basis for policy recommendatlOns. ThlS 

type of error tends to obscure measurement of the ~rue d,:gre,: of 

association that exists between the factors under lnvest1ga~lon. In 

other instances, the errors are systematically d~stributed.ln so~e w~y over 

the observations. This type of error produces blases o~ dl~tortlon 1n 

the inferred relationships. In either case, the determlnatlo~ must be. 

made balancing the cost of data purification and the cost of 1mplementlng 

an erroneous research finding. 

One example of erroneous data that is difficult to d~tect and correct, 

but quite important to verify, is the street address of wltnesses. ~n 

a study of witness cooperation, it was found that about 30 perce~t oi the 

recorded witnesses could not be l~cated due to erroneous addresses 

recorded in the PROMIS data base. It was important to attempt to 

isolate whether these errors resulted from: 

• 

• 

• 

false addresses deliberately given the police by witnesses, 

erroneous recording by the police, 

erroneous recording by the prosecutor, or 

clerical errors in transcribing from source data forms 

to machine-readable inputs. 

Each of the above types of errors impli,:s a q~ite different problem. Time constraints precluded a thorough analysls dur1ng the study, b~t a 

cursory review indicated quite a few obviously false ad~resses de~lberatelY 

given. One common example of' this was "1600 Pennsylvanla Avenue. ~t 

was of concern to note the lack of attention given to accuracy of thlS 

data item by police and prosecutor alike. For pur~ose~ of that stud~, 

a sample analysis showed the errors were equally d1strlbuted.among Wltnesses labeled cooperative and witness,:s label~d noncooperat1ve by the 

prosecutor so it did not appear to serlously blas the sample. However, 

*A Study of Witness Cooperation in t~e District of Colum~ia, Dr. Frank 

Cannavale, Jr., Institute for Law and Soclal Research, 1125 F1fteenth 

Street, N.W., Washington, D.C., forthcoming. 

124 

such errors can be crucial i~ locating witnesses for trial appearances, and both police and prosecutlon management were accordingly advised. 

AN EXAMPLE OF A RELIABILITY ANALYSIS 

To estimate the reliability of the PROMIS data 100 cases were randomly selected from the more than 15,000 screened* by the prosecutor in 

19!3: An.att:mpt was ~ade to locate documents prepared by four separate cr1mlnal Justlce agenc1es (police, prosecutor, court and bail agency) on 

these 100 cases. The purpose of such an exhaustive search was to duplicate to some extent sources of a particular data element so that in addition to.reporting the incidence of agreement and disagreement between 

the data 1n PROMIS and the source document, the reliability of ~he source 

documents themselves could be assessed. It was not possible to locate 

documents prepared by each of the four agencies for every case though often a second or third pass through the files uncovered a document missed previOUSlY. 

When as much data as could reasonably be located on the 100 cases 

had been gathered and recorded, the task of comparing each of the 120 

~ata elements for each case as recorded in PROMIS to the corresponding 

1tem.on one or more of the source documents was begun. (It should be 

ment10ned that between recording and comparing, all fields which were 

coded for entry into the computer system were recoded so that coding e~rOlls would also.be detected.) Figure 1 is a sample of the form deslgned for recordulg each of the possible outcomes of the comparison 

f?r (Iach Of. the 120 fields. A separate fom was completed for each case Slnc~ tallYlng responses on a master sheet would not have permitted re~overy if the.t~tais for an~ field failed to equal 100. In addition, 

thlS method faclhtated checkll'lg by a supervisor for \:c!!qistency across coders. 

Reliability estimates wel~e produced by tallying for each field the r~sults of the comparison for each of the 100 cases. Separate concluS10ns could b0 made about the incidence of omiSSions, unknowns, agreement and disagreement of each data element compared to the source document. 

In some cases the researchers were interested in the reliability of 

a set of data: for example, defendant characteristics or arrest information rather than the reliability of each data element contributing 

* The prosecutor screens or reViews each arrest made by the police and elects to file charges in approximately 75 percent of the arrests. 

125 
Com Check only 1 box In applicable plete be~ Complete 1 of 3 c.togorle. below. d 2nd bo>< may be chacked.). et ory On the 1st category, 1n an lowi t entry 

r C 4fQ PROMIS l(,,' 13 otho r than 

I PROMIS blank 

Ca$® Number btftn k/unk "p' $7()Of pear son mor PROMIS other 

than blank/unk unknown than 1 saurct! 

.., 

PDIO 

doc umer.t 

.., C 

i 

'fE~ ·F Q) .. C (]) Q) .., .. "" 11)., '"' E'ro 0.>< • ~ C 

ro '"' 0.>< ' .2~ C:C 

No Papered Case 

c: " 2! '" ID..!:! E'ro :00. ":0 

., " C:c: 

;,.:0 

'" E g ", ~ :00. " :0 "E '"' ..... 

Q) ., ._ C 

>:0 ........ ........ c"" 

U '" 11) C '"":0 U E " ..... c"" a 0 ...,.;,,; "c 

" Vl ~ 0 1-' .... 

-88 '"'.>< "0" c:c: 

"'0" on PROMIS sheet means blank ~ ~~ '" c "'" Q) C 

=~ 

Q) 

0>.., " c: c: 

00 (l) '" 

o Q) Q) Q) c: Q).!!1 =~ ,""' :0 <t.o ~~ 8l 0 ~ U 

~j.~ .!!1 °0 ~.o <::.0 Z I" 

l.eading zeroes are suppressed 

Ole Z.., -Ol .- E <to .0 

on PROM IS sheet 

<1;", 0", 

Field Nome Notes 1 2 3 4 5 6 7 8 9 10 11 

Current case number 

POlO number 

M/F Code or Bkdn date =~ V 

Arrest date _\I: 

I 

Arrost ti~._ ~/ 

V 

_tL 

Arrest address 

.~~ 

Offense date .-~ 

Offanso time 

Otfens.'6ddress 

Defend, nt address -~ Defendcwt sex _rL. 

Defenda nt race V 

Defendant OGS Compare ycar oniy If 

No. of codefendants V 

Urine test results ~ Arraign Appear - t/ 

"C':" 

Bail type granted 

Bor,cl amount 

Defensecoun~el ,c:ode 

- " V 

Defense counsel ~ _ 

Screening f"USA - Reviawing AUSA V - Papering date tf 

Prop/Evld recovered ./ 

Arresting PO badge V 

Assisting PO badge V 

Arresting V PO unit 

-~ 

V ~ AssistingPO unit 

Number of witnesses PO only for no papered case 

;·i Final disposition V - Trial type Jury or Nonjury -look out 

Number of continuance for "ont. array V - Disposition date ./ 

Sentence date Look at Cant. Array 

Specie I assign ment 1 in PROMIS-Spec. as sgn 

Action date 1 - Action reason 1 

Action date 2 

--= - " , 

. "I'"'"'I"-Inal 

Charge 

disp/NP.reason 

5 

_ 

show 

Pre·breakdown 

final dlsp chargBs 390 or 391 - Final disp/NP reason 

Charge 6 

Final di~NP reason 

1 {l-J 2=K. 3=L. 4=Ul Look at Charge also 

Z 

3 

Figure 1. A SAMPLE FO RM . FOR RECORDING POSSIBLE OUTCO MES 

126 

! 

'"' c 

" E'ro " :00. 

U E o 0 "CJ U 

00 Z ... 

12 

-

-

-

V 

V 

t/ 

t/ 

-

-

~ 

V 

t/ 

L 

V 

.1L 

-

h, .... 

to that set. For this purpose the statistics for groups of related data 

elements were grouped so that estimates of reliabll tty could be stated 

In more general terms. Figure 2 shows the reliability of defendant identifiers as an example . 

r---________________________________________________________________________ ~ 

DEFENDANT IDENTIFIERS: . (POLO, SEX, RACE, YEAR OF BIRTH) 

RELIABILITY OF SOURCE DOCUMENTS 

Ins ta nc es Where 2 0 r Mo re Sou rce Doc umen ts We re Loca ted = 82 

Ag reemen ts == 77 

Di sagreements =: 5 

OPPORTUNITIES TO COMPARE PROMIS AND SOURCE DOCUMENTS 

Agreements 

Disagreements 

Agree and Disagree 

UNKNOWN IN PROMIS 

Source Document Indicated Data Known 

Unknown in All Source Documents 

BLANK IN PROMIS 

All Source Documents Blank 

Source Document Contains Data 

NO COMPARISON DOCUMENT 

98.4% 

1.0% 

.5% 

100 % 

100 % 

100 % 

(379) 

( 4) 

( 2) 

( 0) 

( 2) 

( 0) 

( 0) 

(385) 

( 2) 

( 0) 

( 13) 

.. ----------------------------------------------------------------;---------

(400) 

Figure 2. RELIABILITY OF DEFENDANT IDENTIFIERS 

127 
.' .~ .... -m- .. ~~~ ,," c-olrfnm ~h1'p Few data elements \'Jere omltted frequentlY· .:>Ullt:: were ;:,u."" ..... ,'-'v':' -:.-

to be located on the source documents for campa rl son that then re 11 abllit could not be assessed in the 100 case sample. yse ~of such .data . ele~ents in the research project was suspended pend1ng Turther 1I1vestlgation. 

In some cases the reliabi1ity of the ~ata could be ~reatlY ~mproved b loosening the definition of agreement, 1.e., by countlng a Sllg~t v~riation from the source document as an agreement. For example) lf the 

dat e of a court event was allowed to vary one day from the source document 

and still be counted as an agreement, the percentage of agreement (versus 

disagreement) rose from 95.6 percent to 98.~ percent. 

In ather cases the reliability af a data element cou~d be greatly 

increased by collapsing the categories defined by the vanables. O~e instance of this was defense attOt'neY,type .. PROMIS had.attempted to 

collect three different values for th1s .va~lable: publlC defe~der~, ,CJA 

(an attorney appointed to represent an wdlgent defenda~t), a~," pnvately 

retained. In the initial pass the reliabilit~ of the.fleld glven the 

three values was found to be extremely low. l~ was dls~~ve~~d that the 

individuals coding this information for entry lnto P~OMI~ dlO no~ have 

available the data to distinguish between CJA and.prlv~tely reta1ned, 

though the pubiic defender identification was eas11y dl~cern~ble. W~e~lr the variable was redefined as having only two values: publlC defen et 

d Hnot publ ic defender II the instance of agreement (v/here there was an :~try in PROMIS and a so~rce document for comparison) became 100 percent. 

The majm~ benefit of this reliability analysis was to demo~strate the quality of most of the data fields and as~ume that.only rel1abl~ d~ta were used in the statistical analyses. One slde benef,t of the rellabllity study vias that insufficiencies in the l1}anu~l and computer system 

for collection and recording of data were hlghl1ghted. T~e kn?wledge 

gained from closely examining the ~ystem wa~ turned back l~tO ,t so r 

that data collection techniques WhlCh were lmperfect were lmproved fo. 

future use. 

128 

CHAPTER 13 

A ~lODEL TO AID IN PLANNING FOR 

THE TOTAL CRIMINAL JUSTICE SYSTEM 

Alfred Blumstein 

INTRODUCTION: THE PLANNING PROCESS 

In this paper, 'tIe examine the criminal justice system planning process~ 

describe and discuss one tool (the Justice System Interactive Model) that 

can be u valuable adjunct to the planning process, and indicate how it has 

been used in certain jurisdictions. Other jurisdictions can then determine 

haw they might make use of it. ' 

Planning is an analytical process in which an organization attempts 

systematically to make rational choices for the future. The emphasis is 

on the process by which those choices are made, rather than on the choices 

themselves. In this case the choices are "rational ll in the sense that 

they address the objectives of the criminal justice system, as opposed 

to other objectives that may well influence behavior in the criminal justice system (for example, the desire of political figures to be re-elected 

and the desire of civil servants to preserve their jobs). Even though 

these objectives are real ~ (and are certainly rational from the perspective of the people who are concerned with them), rational planning in the 

criminal just"!.ce system involves concern for question,S like crime reduction) enhancement of 1iberty) and reduction in the total social cost of 

crime control. -

The second principal characteristic of the planning process is the 

orientation to the future, making choices now for implementation in a 

future--and therefore uncertain--world. The planning process must face 

up to that uncertainty. In the criminal justice field~ such uncertainty d~rives partly from the inability to predict the variety of social and politlcal changes that will influence behavior within the criminal Justice 

system, and partly from the effect of its own actions on criminal behavior. 

Furthermore, the mutually adaptive behavior between crime and the actions 

of the criminal justice system results in each of them influencing each 

other in ways that are only minimally predictable, while general changes 

in the political and socioeconomic climate will affect both crime and the 

criminal justice system. 

Be that as it may, the presence of uncertainty does not preclude the 

use of analytical techniques and techniques of statistical estimation any 

more than the uncertainty about the roll of a pair of dice precludes the 

experienced gambler as well as the statistician from being able to project 

that the roll of a "7" is six times more 1 ikely to occur than the roll of 

a "12," even though the next roll might well be a "12." Because the 

processes in the criminal justice system, however, are far more complex, 

no analyst can make anything like comparable projections, and so considerable judgment must be required in obtaining and using any such projections. 

129 
The thi rd prine; pa 1 emphasi sin the pl anni ng pr:oce~s i s _o~ the n~~d 

to make choices, an operation which involves a combinat1on ot torecast1ng, 

prediction of "impact, and attribution of the cost bei1efits of a given. 

action. Thus, the planning process inherently involv~s the value c~ns1derations of the community. It would be even more useful, however. "If that 

valuation process were applied to factual information about the consequences of alternative courses of action once those consequences are 

i 11 urn; nated. 

Such a planning process of "making rational choices for the future ll 

involves the following steps: 

1. Describe the current system; 

2. Project the future environment; 

3. Develop alternatives among which to choose; 

4. Analyze the impf.lct of the alternatives ("pre-evaluation"); 

5. Allocate resources to the choices and implement them; 

6. Evaluate the impact ("post-evaluation"); 

7. Repeat the process on a regular and continuing basis. 

The need for these seven steps is reasonably logical. First, i.n 

order to consider sensibly any revisions or changes in the current system, 

it is necessary to have some characterization of it. It is necessalCY t~ 

know what the present crime situe.tion is, how the current criminal Ju~t1ce 

system works, what resources are applied, where and how they.a~e ap~lle~, 

and how the budgets are distributed over the parts of the crlm1nal Just1ce 

system and over the various crime types. 

Because the planning process is concerned about the future, the next 

logical step would be to project the d~script~o~ of the ~urrent system 

to the future. This involves forecast1ng ant1c1pated crlme rates, as 

well as projecting the future behavior of the criminal jus~;ce s~stem. 

The projection of crime rates might be undertaken by a var1et~ ot.means, 

including simply assuming that current crime rates w~uld pers1st :nt~ the 

future, extrapolating the last few years of data, uS1ng more SOph1st:cated 

time-series analysis techniques, or using more sophisticated models 1nvolv;ng the crime rates per demographic group (e.g., age, race, sex 

combinations) and projecting the demographic mix. Each of these methods 

has its virtues, the simpler ones being the easier to understand and to 

implemen"?, the more elaborate ones imposing a consid~rably gr~a~er demand 

for data and for computational power, although somet1mes provld1ng considerably greater predictive power in return. 

For the features of the criminal justice system other than crime rates 

and future behavior of the system itself, it would be desirable to find 

parameters that are reasonably stable over time. Thus, for example, the 

number of cases processed will vary from year to year, and so the number 

130 

of guilty or innocent dispOSitions will vary accordingly. In many settings, 

however, the proportion found guilty or innocent may well be far more stable than the total costs and workloads, (which may depend much more on 

total number of units processed). Here again, the easiest projection 

would be to assume that next year's branching ratios, costs, workloads, 

etc., are the same as this year's. If a longer time series on these 

parameters were available, then one might project that time series in 

ways similar to the projection of crimes. In other cases, however, the 

introduction of new programs, or new court decisions, or new legislation 

will force a qualitative change in some of those parameters, and that 

qualitative change must be taken into account in the planning process. 

In some cases there may be empirical evidence to indicate how a new program affects the value of a system parameter (e.g., experience in other 

jurisdictions or evaluative reports). In other cases, the best estimate 

is simply some judgmental guess. 

Since the planning process involves choices, one must first define 

alternatives among which to choose. The development of such alternatives 

is inherently a creative process by which new ways of doing things are 

proposed by staff members of the existing agencies (although they often 

seem to have a compulsion to continue doing things as they have been); or 

by staff from the planning agencies through scanning innovations that have 

been used successfully in other jurisdictions; or by other means as diverse 

as citizens' suggestions, brainstorming sessions, etc. 

The choice among these alternatives must be made before they are 

implemented in the local jurisdictions, and so a "pre"7€valuation" process 

must be undertaken. In that pre-evaluation, a judgmental process is again 

called for, but that judgmental process can be enhanced significantly by 

the use of appropriate analytical tools. The next section contains the 

discussion of one such tool. the JUSSIM model, which can aid significantly 

in assessing the impact of a particular change (e.g., a pre-trial diversion 

program) on different parts of the system. Because this pre-evaluation 

stage occurs prior to implementation, it is inherently an analytical or 

abstract process, and so can better employ some form of mathematical "model" 

that represents abstractly the behavior of the criminal justice system, 

rather than an experiment imposed on the operating criminal justi~e system. 

The pre-evaluation process provides an initial assessment of the impact 

of a change both in the part of the criminal justice system to which it is 

applied, and also in other parts of the system. Such an assessment should 

include not only thle "downstream" parts (e.g .• the effect of a change in 

police arrest practice on court workloads), but also the feedback or 

recidivist effects. Normally, once a planning agency has made such an 

impact assessment, it then allocates resources to new programs--taking 

into account the estimate of these ;mpacts--and weighing impacts along 

with the more direct costs and benefits associated with alternative 

choices. 

Having made the choice and implemented it on at least a pilot basis, 

or possibly throughout a jurisdiction, the planning process can then undertake an experimental "post-evaluation" of its impact. This eva1uation is 

modeled on the classic random, controlled experiment. But such experiments 

in the criminal justice system are limited by the difficulty of their 

131 
being truly controlled, by the inevi1..able s~lection effects that.cr~ep into 

the experiment, and by the multiplicity o~ 1nfluences that preva1l 1n a 

social setting--all of which influence crlme rates ~r o~her mea~ures of 

effectiveness. Despite these problems, the evaluatlVe 1nformat10n repr~­

sents an important part of the planning process and shoul~ be fed back lnto 

improvement of all the previous stages for the next plann1ng cycle. 

A JUSSIM MODEL FOR THE ANALYSIS OF IMPACTS (JUSSIM I) 

As described above, a central part of the planning process is .the 

ana1ysis of the impact of alternative choices, ~nd as ~uggested t~lS can 

best· be accompl ished with some form o~ model ~nor to 1mpl ~men~atlOn. The 

general method by which this p~ocess 1~ d~ne 1S reflect~d 1n Fl~ure 1. A 

proposed system change is consldered; lt 15 transla~ed lnto estlm(at~s of 

parameter effects which are then put through some klnd of model WhlCh. 

may be a mathematical model or some judgmental pr~cess) t~ generate estlmates of the system effects or IIsecondary effec~s .resultlng from that 

chan e. After such a calculation and after rev1ewln~ those syst~m effect~, 

a us~r might conclude that the initial parameter est1mat~s were lnapproprlate, 

and this reconsideration might result in estimation of dlfferent parameter 

values. Or he might conclude that the model does not adequately reflect 

some of the complexities he is certain do occur, and so he may well ~ha~ge 

the model. After several cycles through this process, he may be satlsfled 

with the estimate of those system effects and go through the same process 

for another system change. 

J 

PROPOSED SYSTEM 

CHANGES 

Reuise Estimates .J 

+ 

ESTIMATE PARAMETER 

EFFECTS 

(himary Effectsl 

Reuise Model J 

1 

MODEL 

1 

] ASSESS SYSTEM 

EFFECTS 

(Secondary Effects) -

I L [ .. -.. 

Try 

Another 

Change 

~-~--'-------------::-------' 

Figure 1. GENERAL PROCESS OF IMPACT ANALYSIS 

132 

----

. - .• ~,'!..',. , 

This is a totally general characterization of any analysis of a pmposed system change. The critical aspect, however, is the use of a model 

that provides the basis for translating a programmatic change in the criminal justice system to an estimate of the impact of that change on performance measures of the system. In a sense, the model may be viewed as a 

"black box ll in whi~h the parameter estimates are the knobs (reflecting 

measures like arrest policy, punishment policy, resource allocation, etc.) 

and the system effects are the dials (representing measures of output 

effectiveness such as crime rate, costs, etc.). This conceptual "black 

boxJl simulation of the criminal justice system enables the planner to 

twist the knobs, note the dial readings that result, and then to use that 

experience to help him choose the next knob settings to explore. 

The crucial stage of this process is the formulation and use of the 

model. One such model that has been used in a number of jurisdictions is 

the Justice System Interative Model (JUSSIM). The model uses a computer 

in an lIinteractive" mode, with a user sitting at a terminal, calling a 

stored data base characterizing his criminal justice system, and interacting 

in a conversational way with the computer program, with no special requirements for technical training or computer programming skills, since the 

entire process goes on in plain English. 

Single-Stage Analysis 

The operation of the JUSSIM model can best be exp1ained by examination 

of a single stage in the flow diagram of cases through a criminal justice 

system. Consider, for example, the jury trial stage for a single crime 

type (say, robbery). Let us assume for the moment that 100 robbery cases 

come to the jury trial stage, a number calculated based on flows from the earl ier stages. 

As shown in Figure 2, there are two output flow paths from the jury trial: "acquittal" and "guilty'l. If the branching ratios from jury 

trial to these two paths are .4 (probability of acquittal) and .6 (probability of conviction) respectively, then the jury trial stage feeds 60 

offenders to the sentencing stage (which also receives offenders coming 

from guilty plea and bench trial). This same branching and collection 

process at the earlier stages provided the basis for calculating the 100 

cases coming to jury trial. In this simple computation, the branching 

ratios (the probabilities of conviction and acquittal) are required input data. All input data are shown circled in Fi~ure 2. 

We are now interested in calculating the workloads, costs and resource requirements associated with the two principal resources used to 

process cases at jury trial: judges and prosecutors. Let us assume that 

the average unit workload for judges in robbery jury trials is 6 hours, 

and that the prosecutor (with more preparation time) must spend an average 

of 20 hours per case. These numbers also are necessary input data, and 

so are also circled . 

Focusing on the judges now, their workload in handling robbery jury 

trials, at 6 hours per case, for 100 cases, is then simply 600 judge-hours. If a judgeship-hour (including his support staff and facilities) costs 

133 
, ' t datum', then the cost of the 600 judge-hours i~ simply '~QO_~~lso.~n ,~p~_~ 4c ~~~il hlp 1 000 hours per year for processlng $bU,UUU. 1T a JUU!jt,; .~ ~.-,.~.- - , ,. A k d (600 iudge-houY's) 

cases, then the l:esource r~9.ul~er~ent is th_ WOf

a 

l~~n_timeJjUdge is required 

divided by this ann~al,..?~a:l~blllN~i~~rO~~l~~tations are made for prosecu- te process robbery Jury na s'. /i"'" 

tors, and these are also shown In ~lgure 2. 

.... -~.. . . . th's the sum of the reSOUl"ce costs 

"!,he ro!)~ery JW"y tnal S c~~t6oo ~g~ Judges and $40,000 for pr?secutors, 8SSoclated wlth that stage, .. ' osts associated wlth 

for a total of $lOO,~O~. s~rllarlY'o~h~h~o~~~o~rce costs for all stages 

tCltLl?.ehI.:L c(lrltbe CstomemPul.eThBens . ~~e S~~ta 1 court costs woul d simpl y be the sum HI t e cour sy., . of those costs for all the various types of crlme. 

100- JURY TRIAL 

Acquittal 

(40) 

@ 

Guilty (60) 

L...... _____ ... ® r--=: 

L-____ _ SENTENCING 

Unit Workload 

Workload 

Unit Cost 

Cost 

Annual Availability 

Resource Requirement 

Guilt}' Plea -

Bench Trial --

Judges 

@hrs. 

100 X 6 = 600 judge hI's. 

@Ihr. 

600x 1 00= $60,000 

Prosecutors 

@tt's. 

1 OOx 20 = 2000 prosecutor im. 

@hr. 

2000x20=$40,OOO 

@t~(s/judge-year @rs/prosecutor-year 

600/1000::: .6 judge 200~/2000 == 1.0 prosecutor 

Stage Cost: $60,000 + $40,000 '" $100,000. 

I 0 CI't'I",} Illmll.wr" mp_re_se_nt_r_[Jq_:,J_'.r0_;d_jn_p_ut_d_ata_< ____________ -:::-:-~ ___ --' 

L_·~~~_-<"~·«·-·~i~ur~-~. AN ILLUSTRATIVE JUSSIM STAGE: Robbery Jury Trials 

134 

., 

Basic Operation. 

The operation of the JUSSIN model begins with a "base casel> ref'lecting the current operation of the system. All the data on the base case parameters must be collected and stored. The user of JUSSIM--the criminal 

justice system planner himse1f--then creates a "test easel! by making 

changes in any of .the base case parameters at the computer tenninal. The 

program then reports to him the changes in flows, costs~ workloads, and 

resource requirements resulting from the changes he introduced. He thus 

uses the model as a very fleXible design tool by making contemplated changes~ rapidly obtaining an assessment of the effects of those changes, 

and then trying another change suggested by reconsideration of the previous 

try. If the designer does not like the impl ications of a proposed change, then he can reject it immediately and try another. 

Operating the model to assess the consequences of a system change, a 

user necessarily has to make assumptions about the detailed primary consequences of the change as they are reflected in changes in the system's 

parameters. For those changes that initially appear desirable to make, 

he may then want to explore the assumed consequences more carefully. 

Several system planners can explore the same system changes, each using 

his own assumptions. If a threshold of acceptance or rejection lies outside the range of consequences calculated by this group, then acceptance or 

rejection is clear, and further exploration is not necessary. If it lies 

ItJithin the range, then closer examination is required to assess the validity of the various assumptions made. 

One of the virtues of this process is that it fOi'ces the planners into 

a debate over their assumptions and estimates rather than on the generalized 

goodness of a possible change. Thus, the model serves the same fun~tion as 

any other model, it lowers the level of argument to issues that are more fund~mental and empirically testable than they would be without the model. 

Model Structure 

To generaHze the previous example. we describe here the operation of 

the model in terms of its inputs, its outputs, and the relationships between them. 

Inputs 

The 

1. 

2. 

3. 

4. 

basic inputs for JUSSIM are enumerated below: 

A listing of the crime types considered 

An enumeration of system stages 

For each stage, a 1isting of the stages.i~ feeds and spe~ifica­ tions of the branching ratios characterlllng the proportlon of 

flow along each of those flow paths for each crime type 

A listing of resources, and their associated 

A. ~al unit 'availability (say, hours per year per resource) 

B. cost per unit time (say, dollars per hour) 

c. the maximum amount ("capacity") of the resouce that might be made available 

135 
I ' 

-----.-----------~. 

nm 

5. 

6. 

A tabulation of the unit workloads, or times for processing a

l

· d 

unit of flow, at any stage or flow path by each resource app le 

for each crime type 

A reference flow, typically the number of reported crimes by ~ t e (or the number of arrests) that sets an absolute . ~~~~~ o~PflOW throughout the rest of the system when the b~anchlng 

ratios are specified. 

The branching ratios, the unit processing times, and the reference flow are 

all specified for each crime type. 

QJ:Li:.Pll..t.§-

The outputs of the JUSSIM.m~del are presented to the us~r in whatever 

order and organization he speclfles. The potential outputs lnclude the 

following variables: 

1-

2. 

3. 

4. 

5. 

Flow through each processing stage 

Costs associated with each stage for ~ny ag~regation Ofls~ages grouped into .specifi~d "subsystems," lncludlng the comp e e 

aggregation wto a slngle, "total system!! 

. t Lle costs associated with each of the Resource costs, indicatlng r 

resources. 

Resource workloads, the number of manhours per year imposed on 

each of the resources 

Resources required, the numbers of each of the specified iesources 

that would be required to handle the workload. 

t · f l'me type, and so each 

All of these output measures are f~nc lons 0 cr 'd 1 t pe or as a single \alue can be presented for each indivl ua crlme y , 

summed over all the crime types. 

Basic Relationships 

Assuming knowledge of all the input p~rame~ers, one can then calculate 

the output measures by the following relatlonshlps. 

• Knowing the basic input flow (say, report~d crime~) a~d th~h branch'lng ratios the flow at each stage lS calcu ate as e 

sum fr~m each of the stag"es feeding it. These a~e the flows in 

the preceding stage, multiplied by the correspOndlng branching 

• 

ratio. 

The resource workload ;s the flow a~ each, st~ge or .flow path d 

where the resource ;s applied, multlpled by ltS un,t workloa 

there The total resource workload is then calculated as the 

sum o~ th~se resource workloads over all the stages and flow 

paths. 

136 

• 

I 

The number of resources required per year is the resource workload divided by the annual availability of an individual unit of 

resource. 

The processing ~osts at a stage is the flow through the stage, 

multiplied by the workloads and the unit costs for each resource 

applied at that stage. The subsystem costs associated with a 

"subsystem" (or any aggregation of stages) is simply the sum of 

all the costs associated with its constituent stages. 

Operation of a Run 

In the operation of a JUSSIM run, the user's basic role is to create 

a "test case" to compare with a "base case" already stored. The base case 

is a complete set of data describing a criminal justice system and is 

developed individually for each jurisdiction. Once he has drawn a base 

case from a data file, the user then generates a test case as changes to 

the base case. Sitting at a terminal, the user is asked a sequence of 

questions about what changes he wants to make. Each of these questions 

is an entry gate to a "phase", which leads the user by a sequence of 

hierarchical steps directly to the parameters he wants to change. A 

separate phase is provided for changing each of the following parameters: 

1. Branching ratios 

2. Unit workloads 

3. Annual unit resource availability 

4. Resource unit costs 

5. Resource capacity constraints 

6. Reference flow. 

Once a phase is entered, further detailed questions permit the user to 

specify precisely which parameters he wants to change and the crime type(s) 

for which he wants to make the change. All the questions are in clear 

language, and the answers regarding stage numbers, resource numbers, crime 

groups, workload numbers, and other code numbers are based on the code 

numbers in the base-case data file. Th"e program then displays the basecase value for the parameters the user identifies and asks him to type in 

the new or test-case values. 

In deal ing with the multiple parallel channels of flow for all the 

crime types*, most users, at least initially, do not want to sit through 

the complete detailed output for each crime type. In a separate phase, 

JUSSIM permits them to choose one of a number of standard complete partitions of the crime types (e.g., into felonies and misdemeanors, Part I 

* The FBI's Uniform Crime Reports (UCR) system organizes its data into 29 

separate crime types. JUSSIM permits 30 crime types. 

137 
and Part II crimes, etc.) or to specify their own crime groupings. Thus, 

a user may choose to have the complete details on one or a few individual 

crime types but combine the remainder into a single group. The standard 

groupings are specified in the data file. 

In another phase, the user specifies the output tables to be displayed. The output tables present calcu1ated results on flows, costs, 

workloads, and resource requirements. The data are listed for the base 

case, the test case, and the absolute and percentage change in going to 

the test case. These results can be presented by crime group or surrmed 

over all crime groups. In the early stages of an exploration, a user will 

presumably want to conserve time and will examine results only for the 

total system or for a few critical subsystems. At the end of an explora- tion, the user is more likely to want more detail. 

At the end of a run, the user is asked if he wishes to re-run the 

model. In addition to using this to explore a new issue, he will do this 

iteratively if he is dissatisfied with the implications of some of the 

assumptions he has made and would like to reconsider some of them. In a 

re-run, he is given considerable flexibility in respecifying his new base 

case (e.g., calling a data file, using his latest test case), and then 

begins again to create a new test case. 

THE FEEDBACK MODEL (JUSSIM II) 

I. 

The JUSSIM linear model discussed so far looks only at the downstream 

consequences of a change in the criminal justice system. It cannot explicitly 

calculate the effects on the future load on the criminal justice system of new 

programs that might change either the probability of rearrest of released indivl' j 

duals or the time between release and rearrest. For example, on the one hand, a : 

pre-trial probation program might reduce the probability of arrest for some 

of the treated individuals. (This could occur if the threat of prosecution 

for the deferred char'ges acted as a deterrent, or if the treatment provided 

during the probation were more successful in rehabilitation than any of 

the a.lternative treatments that the defender might have received.) On 

the other hand, these released individuals would return to the street more 

quickly than they might otherwise, and so the opportunity for earlier 

rearrest is increased. 

Because so many prograrrmatic and procedural changes under consideration 

by criminal justice planners involve these kinds of considerations, an 

extenti'on of the JUSSIM model (deSignated as "JUSSIM II") incorporates these 

feedback flowpaths into the model. 

A Simple flowchart of the feedback version of JUSSIM is shown in 

Figure 3. The new feedback flowpaths can now be characterized simply as 

a branching ratio, i. .e., the average proportion of individuals released 

from some point of the criminal justice system who will ever be rearrested. 

We must also associate an average time until rearrest for each of the feedback flow paths. Such a concern for elapsed time was not necessary in the 

linear model. 

138 

Also, since a vital aspect of th", JUSSIM t '. . 

specific ~haracterization of systems ~peration s .~~~t~re ~s ~t~ Crlme- mu~t speclfy the new offense for which- re 'd' ~ t eed_ac.. l.USSIM mo?el 

q~lre~ the inclusion of a "crime-switch m~~i'~~,~si~ ~~e re~rrested. ~ril.s ~e­ dlstrlbutes the flow of individuals released from thee m? ~ 'la.mat~lx WhlCh 

after arrest f ' t . Crlmlna Just1ce system 

th ( or.one crlme ype lnto a flow of individuals rearrested for 

ano er or posslbly the same) crime type. 

qUire~ns~~~~}~~~t~~n t~~ :~OW.Of recidivists~ t~e feedback model also rearrested for the first ~;m: ~~put flowbof vlrgln arrests: the ~ndividuals This is a distinction r~relY m~dma~ s~hseqUently re~ppe~r as recidivists, 

justice system, and less often i~ ~~ail:b~~n~:~!~al'zatlons of the criminal 

Thus~ the principal new elements required to extend the 

model to lncorporate the feedback flow of recidivists are: linear JUSSIM 

1. 

2. 

3. 

4. 

The probability of rearrest by crime type of first arrest 

The average time until rearrest by crime type of first arrest 

A crim~-switch matrix linking the prior and subsequent types for recidivists crime 

The number of Virgin arrests by crime type. 

Virgin Arrests Total Arrests 

Recidivist 

Arrests 

r--------W COURTS r-------M PRISON 

NON· 

r----ft R ECI 01· M---i 

VISTS 

Figure 3. BASIC FEEDBACK MODEL 

139 

r 

I 
-

In addition to these extensions to the b~sic model, the feedback 

JUSSIM II model requires significant changes 1~ the use of ~he m~del. Th~ 

1 inear JUSSIM model is a one-period model, typ1cally one year. ecause ~ 

the Si9~i~icant time delays in the recidivism pro~ess, the feedb~c~ ~er~~on must be a multi-year model. Thus, a new user optlon has been ad e 0 e 

feedback version. The user can request a printo~t of selected resu~ts at the end of each intennediate yea~ in the.mult1-year run, and he can 

examine these results before their lmpact (1n the form 0Tfh.a ~ew.qU~~:p~~g 'd' , t ) has boen registered on subsequent years. lS 1S ln ~~~~ ~~~Si~teracti~~ character of the JUSSIM planning ~ool that encourages th~ user to reexamine his assumptions if the results e1ther do not seem 

plausible or satisfactory. 

The most significant change in the operation of the model, however, 

are the new data demands p 1 ace d on th e . user Obviously the user must . . now 

come to grips with the problem of identifying virgin arrests, re~id1v1~m 

probabilities, recidivism time cons t an t d s, an 

elements in the cr1me-sw1tch 

matrix .1 

This feedback versi8n, JUSSIM II, is currently operating, but itfh~~ 

ot been im lemented as widel~ ~s JUSSIM I. This 'is.partly because 0 1 S 

~reato~ ~~t~demandsand partly because its use requ1res.somewhat m~re sophi~ti~~tion on th~ part of the user as a result of its more camp ex 

structure. 

IMPLEMENTATION 

As expected) the response to the interactive JUSSIM model .was.far ~ 

more ositive than to a previous batch-processed model from w~l~h 1t wa deriv~d . A number of states (e.g., California and Alaska), c1t1es (e.g.> Pittsbu~gh, Philadelphia, and Denver), and countries (e:g., Ca~ada and 

Sweden) have organized data collection efforts to descrlbe.the1~ ~~stem~ i 

as a base case for the JUSSIM model and plan to use a verSlon o. e rna e 

for their own planning. The Alaska model focuses on the correct10ns ~YStem and therefore represents the police and cou~t subsystems as ~~lr 

small number of stages, and therefore also prov1des gr~ater d~ta1t e.

g

i

, 

a se' arate stage for each institution) for the correctlons.su ~ys em: n so~ePcases, staff changes in these organlzcitions resulted 1n dlscontlnuation in the use of these models. 

An important use of the model is as a teachi~g to~l for system.pl~nners. 

The Urban Systems Institute of Carnegie-Mellon Un~v~rs1t~ ha~ use1 1t.ln 

one-week short courses on systems analysis for c~lm1nal JustlcedPtan~~~g. 

The lanners readily learned to operate the term:nal ,.to re~pon . ~ 

prog~am's interrogations, to tran~late thei~ p~~~e;~d~~e:}f~~i~v~¥~w:~n~s about model-parameter changes, an tOh o~era ~ t ole of forcing the planner design tool The model then plays t e lmpor an r . . . h' 

to consider' the entire system and the interdependency of 1tshPa~~s ~n~.ls lanning. One retired police officer commented, "This was tell lrs l.lffie 

ilve thought much about what happens to the people I arrested. 

140 

7 

In Pittsburgh, three masters degree students at Carnegie-Mellon's School of Urban and Public Affairs developed a base-case data file of tr;7e 

Allegheny County criminal justice system over a three-month summer internship with the Allegheny County Regional Planning Council of the Pennsylvania 

Governorls Justice Commission. A fOllow-on student team, as part of a secondyear project course, used tgat data base to perform a variety of analyses 

regarding potential improvements in the system. 2 New projects considered 

included bail reform, diversion of less serious cases from the higher court 

to the minor judiciary, and diversion from a juvenile detention home. Upon 

graduation, one of the stUdents became a member of the Planning Staff of 

the Council. There he continued to work at integrating the model into the 

Councills planning process. Upon his departure after one year, the model 

was continuously maintained, used, updated, and extended by a staff member 

with no special technical training. 

SOME TYPICAL USES OF JUSSIM 

Once one has a JUSSIM model, there are a variety of kinds of uses to 

which it might be put. For example, it could be used to estimate the downstream implications of an incremental decision at any stage by calculating 

the average or expected cost per additional arrest (or reported crime, 

charge, conviction, etc.) taking into account the fact that some of the 

arrestees will b~ dismissed without charge, some will be acquitted in the 

courts, and some will penetrate through to a correctional institution. An 

average cost would reflect these various probabilities of penetration and 

the costs associated with each degree of penetration. 

Users are often interested in the distribution of 'costs (or workload) 

associated with the various portions of the criminal justice system, since 

knowing those distributions in a jurisdiction might suggest a reallocation. 

Such a reallocation consideration might be influenced by comparing their 

status to national distributions.3 

An important value of the JUSSIM model is the fact that it provides the "entry price" in the development of a statistical data base for a wide 

variety of research projects. Any Single project may not be able to justify the cost of collecting the total base of information it needs, but the 

aggregate needs represented by a range of users who might then make use 

of that collected basp. could usually more than justify that expense. Even 

more important than the expense is the time and effort on the part of 

various potential users that would be requited to collect their information, 

thereby effectively deterring any serious quantitative inquiry into the 

operation of the system. With the ready availability of a planning data 

base in a well-structured format such as JUSSIM, any potential user can 

inquire directly to obtain an estimate of a particular parameter OJ"' of any calculated value, or to obtain a quick estimate of the impact of one or 

another potential system change. 

The JUSSIM model is particularly helpful in evaluating a wide variety of potential system changes. The user 'introduces these changes one at a 

time or in combination, and receives a quick estimate of the downstream 

implication of such changes. Different users, particularly those on 

different sides of a particular policy question, can come togehter to 

141 

·1 
develop estimates of the favorable a~d.unfavorable values of parameter changes associated with a system reV1Sl0n. The cost, resource, and ~low implications of those alternative estimates can then be calculated wlth 

the JUSSIM program. In many cases, the original basis .of .d;sag~eement may disappear. Each of the advocates may develop new lnslg~ts lnto some 

of the implications of the change he was endo~sing or 0ppOSlng; or the 

basis of disagreement may boil down to a part1cu1ar p~r~meter va~ue, and 

that value might then be developed more care~u~ly emp~rlca11Y, elther by a 

test project in that jur~sd~ct~on.or by obta1nlng est1mates that may have 

been derived in another Jur1sdlctlon. 

The JUSSIM program also has significant potential .value fo~ conducting 

cross-Jurisdictional comparisons. Since the.da~a are ~n ~ cons1stent,. 

comparable format across jurisdictions~-partlcu,ar1y wlt~ln a sta~e WhlCh 

has the same criminal justice str~cture--the state Plannln~ Agen~le~ (~PA) can 

generate data files for the different jurisdictions.or reglons Wlt~ln ltS 

purvi ew and can conduct cros~-j uri sdi ctiona 1 compan sons by searchl ng for 

jurisdictions which have devlant parameters. 

As it develops new standards and goals procedures, eac~ SP~ wi~l be 

seeking to find a means of assessing the cost,and workload 1mp11cat:ons of the various standards and goals being consldered by state or nat1ona1 

commissions, or promulgated by them. One of.th~ shortco~i~gs ident~fied in the reports of the National Advisory CommlSS10n on Crlmlnal JUstlce 

Standards and Goals has been the absence of some of the cost implications 

associated with the standards promulgated. With a JUSSIM model for the 

state and its component jurisdictions, the SPA as well a~ th~ St~ndards and Goals Commission can begin to address some of those lmpllcatlons, particularly for those standards or goals that can be translated into 

branching ratio, workload, or unit cost parameters. A study c~rrently underway by the American Bar Association Commission on Correctlonal 

Standards is using the JUSSIM model for precisely that purpose: to assess 

the cost impl ications of the correctional standaj'ds in the National Advisory 

Commission's report. 

See the Appendix at the end of this chapter for a case study of a 

particular use of JUSSIM in Pennsylvania. 

JUSSIM LIMITATIONS 

It should be recognized that ~ll models are finite and limited. The , imits come from the inherent necessity to abstract reality rather than to 

represent it literally in any computational model. Give~ this, one could 

try to build an extremely elaborate and complex model Wh1Ch pushe~ toward 

an extensive representation of reality. Experience in such model1ng efforts has shown that almost always such models fail to be very useful 

because of their elaborateness and complexity. Such elaborateness requires more data than can be reasonably obtained; the size of the model 

implies large costs per run of operation; and enorm?us com~lexity c~ouds the bases for an observed calculated result. Experlence w1th modellng of 

other systems has made it clear that it is important to limit the size 

of any model. 

142 

, This philosophy has been reflected in the JUSSIM model by limiting its 

Slze to 32,~OO words of compute~ sto~age. D~ing so makes the computer program access1ble to most commerClal tlme shanng computing services. More fundamentall~, however, it restricts the complexity by restricting the n~mber of cr:me types, stages, flow paths, resources, workloads, etc., 

tnat ~an be lncorporated. If a user wants to increase the complexity of 

one klnd of structure (to create more stages, say), then he must give up 

on an?ther (decrease the number of resources being considered or the number 

of cr~me type~, for example). Of course, a user with greater computer cap~clty who ~s prepar~d tO,deal with 'the greater complexity can readily 

go ln ~nd reVlse the dlmenslons of those parameters if he is prepared to 

deal wlth the more complex model. 

, In its operation, J~SSIM I (and the downstream portion of JUSSIM II) 

19ncres.the passage of t1me. In particular, this calculates the downstream lmpact of a crime in the year in which it was committed. Thus, a 

three-year sentence for a robbery committed in 1975 is counted in 1975 e~en though part of the time is served in 1976 and later. This would ~till glV~ a good esti~ate o~ corrections cost if the system is not changing ~adlcally over tlme, Slnce people sentenced in 1974 and earlier serve time 

1n 1975~ the~0by roughly compensating for the 1975 crimes whose impact 

occurs 1n later years. 

.This structure makes the model simpler and much of the computation conslderab1y easier and less expensive. But the structure does not allow 

the ~USSIM model to deal ~ith queue~ng or delay problems] which explicitly focus on the passage of tlme and WhlCh are often of considerable concern 

to courts. There are a variety of "job shop simulation tl programs which 

can be used: much more effectively for this purpose. These include the G~neral .Purpose Simulation Systems (GPSS) models, and various other 

slmulatlon programs that specifica]ly address these delay issues .. When 

he focuses on queueing issues, it would be far more efficient for a 

planner to u~e a sepa~ate model addressed to that purpose rather than to 

try ~o com~llc~te a slngle model with all the complexity necessary to 

provlde thlS wlde range of flexibility. 

REFERENCES 

For those interested in more detail on the JUSSIM model than can be 

presented here, a number of references are available. The original flow 

model from(fbich the JUSSIM model was derived was developed by Blumstein 

and Larson ) as an o~tgrowth of the work of the Science and Technology 

Task Force (2) (especlally Chapter 5) of the President's Commission on ~aw Enforcement and Administration of Justice. That original model existed 

ln batch processing mode, which made it relatively inaccessible to most 

non-technical users. The JUSSIM model was re-programmed in an interactive 

mode by William Glass and is described in Belkin, Blumstein and Glass (3) 

and Blums~ein (4). Its particular implementation in Allegheny County 

was descrl~ed by Cohen et a1.(5) and in the juvenile justice system there 

by B1umsteln a~d Stafford (6). Other implementations have been described 

by Gordon Cassldy (7) for Canada, and by others. Sections of some of 

these references have been used in this paper. 

143 
User's Manuals for yUSSIM I (the downstream model) (8) and for JUSSIM 

II (the feedback model) \9) are available from the Urban Systems Institute 

at Carnegie-Mellon University. Copies of the JUSSIM programs, written in 

dialect-free Fortran IV and dimensioned to a computer with 32k words of core 

storage, are also available. The programs are well documented internally 

and contain a basic data file so that they can be operated immediately. Interested agencies can write for order forms to the Urban Systems Institute, 

Carnegie-Mellon University, Pittsburgh, Pennsylvania 15213. 

Parts of the materials in this paper are derived from various segm~nts 

of these earlier documents. 

(1) 

(2) 

(3) 

Blumstein, A.~ and R. Larson."Models of a Total Cr·imina·1 Justice 

System," Operations Research, Vol. 17, No.2 (March 1969), pp. 199-

232. 

Task Force Report: Science & Technology, Report of the Task Force 

on Science and Technology of the President's Commission on Law 

Enforcement and Administration of Justice, June 1967. 

Belkin, Jacob, Alfred Blumstein, William Glass, and Michel Lettre. 

"JUSSIM, an Interactive Computer Program and Its Uses in Criminal 

Justice Planning," Proceedings, International Symposium on Criminal 

Justice Information & Statistics Systems, Gary Cooper, ed., 

Project SEARCH, October 1972, pp. 467-477 . 

. (4) Blumstein, Alfred."Management Science to Aid the Manager: An 

Example from the Criminal Justice System," Sloan Management Rev'jew, 

(Fa 11 1973), pp. 35-48. 

(5) Cohen, Jacqueline, K. Fields, M. Lettre, R. Stafford, and Claire 

Walker. "Implementation of the JUSSIM Model in a Criminal Justice 

Planning Agency," Journal of Research ir:L Crime and Delinquency, 

(June, 1973). 

(6) Bl umstei n, A. and Ri chard Stafford. "App1 i cati on of the JUSS'iM t~odel to a Juveni'le Justice System," ProcE~dings of the National 

Symposi um on Comput.er Appl i cati ons in the Juvenil e Justi ce System, 

December 6-8, 1973, Atlanta, Georgia, pp. 60-84. 

(7) Cassidy, R. Gordon, R, Geoy'ge Hopkinson 1. and William Laycock. 

"A Preliminary Description of the Canadian Criridnal Justice 

System," Report of the Min'istry of State for Urban Affairs and 

Mi ni stry of the So 1 i ci tor G(~nera 1, Ottawa.; Canada, October '1973. 

(8) Belkin) Jacob, Alfred Blumstein and Wnliam Glass. "JUSSIM~ An 

Interactive Computer Program for Analysis of Criminal Justice 

Systems," Urban Systems Institute, Carnegie-Mellon University, 

July 1971. (JUSSIM I User's Manual). 

(9) Belkin, Jacob, Alfred Blumstein, and ~4illiam Glass, "JUSSIM II, An 

Interactive Feedback Mode1 for Criminal Justice Planning," Urban 

Systems Institute, Carnegie-~1el1on University, January 1974 

(JUSSIM II User's Manual). 

14.4 

I 

NOTES 

1 ~o~e JPrelimi~ary estimations of values of these parameters 

111. • Belkln, A. Blumstein, and W Glass "Recid" are explored pr?c~ss: An ~nalytical Model and E~piricai Validat~~~s~ as a Feedback 

rlmlnal 'Justlce, I, No. 1 (March 1973). ' Journal of 

2 ~:~~ri ~~1o~e~~e~~/lfl ~~he~~C~~~nt~· C L~t~re i ~nd Ri chard Stafford, "A 

3~~~~~~i~~s~~~~~e, Carnegie-Mellon un~~~~~~ty,U~!~~~a~~si~;2~ ~~~an County Criminal ju~ti~~t~;~te~~d ~~e~!~ifgrd, ~~nalYSids of the A~legheny 

3 

Programs," Urban S t I' . ~era lons an AlternatlVe 

ary 1972' (3) Jacq~~l~ms cn~tltute, Carnegle-Mellon University, Febru-

~~~~ ~~~aD;~i~~~:~c~~~~~~: f~;~~i~~·A9~~~~:~m3~~~~~~no~fR!~~~~~~S;~ 

As refl ected for example i II Ed' Criminal Justice System: '19~2_7~p~nN~~~re ~n~ ~m~lorment ~ata for the 

tion and Statistics Service Re 0 ' 10na rlmlna Justlce Informament Printing Office, WaShingt6n:\~~~-EE No.5, February 1975; Govern-

.----------_______ .•• __ • ____ ._ •••• _ e· 

145 
Foun·j()TE TO BLUMSTE I N I S itA MODEL TO A I DIN 

f'LANNItK:i FOF~ THE CRIMINAL JUSTICE SYSTEM" 

(JavirJ L, Dougherty 

The "approximatell nature of the JUSSIM model is further reinforced by 

I"ho dah colleci"ion process necessary in most jurisdictions to establ ish the ba~e case for JUSSIM. 

In Prince George's County, Maryland, where JUSSIM was establ ished in 

1974, considerable efforts were required to (I) obtain data from the entire 

:,.;ysi"Eml and (2) make the data comparable among agencies so that the model 

cou I d opera'ra. I n Mary I and, as inmost states, some cr i m ina I just i ce agencies are entirely local and maintain their own statistics for management and 

planning, whi Ie others are regional or statewide and maintain statistics 

larGely on other than local bases. In Prince George's County, the data collection problem was compounded by the existence of several municipal pol ice 

departments with concurrent jurisdiction with a county-wide department which 

hand I ed most cr i m ina I cases and a I I invest i gat ions. 

For the development of the base case, it was necessary to obtain data 

from three levels of government. Municipal ities provided repolted crime and 

arrest data for a I imi-red number of crimes. The Coun-ry provided -rhe majorI-ty of crime and arrest data from the pol ice departmen-r, warran-r service 

datafrol11 the shefiH's departmen-r, and Circuit Court (highest level cour-r) 

procossing dara from both the Circuit Court and the S-ra-re's At-rorney. S-rate 

aqoncies provided da-ra from the District Court (where al I defendants were 

Initially charged and mlsdemeanants -rried). The District Court's statewide 

duta processing center provided information on pretrial detention. S-rate 

agoncles provided informa-rlon concerning Publ ic Defender operations on the 

counly level, probation on the county level, and incarceration. AI I juven110 data was provided by the sta-re Office of Juveni Ie Affairs. 

In most cases, da-ra was not automated and in other cases avai lable auI-omalod drri'lJ was reorgan i zed by manua I procedures for use by the mode I. 

Diven the variations in methods of collection, storage, and reporting 

of duto by the individual agercies involved, a necessary first step was the 

devol0pment of an offense matrix through which to organize al I crime classifkntions il1'l"o standard groups which could be used by JUSSIM. UCR classificarion \'lQS u~,od as the basis for groupings although some groups consisted 

(If more Ihan one UCR type. For example, the Dis-rrict Court did not distin-

(juh:;h between aggravated and simple assault; thus a single category __ of assault was used. Nor was ihere a distinction in the Distric-r Court -data 

betwoen auto theft and larceny; thus a single category of larceny was used. 

further inconsistencies existed between UCR classifications and corrections 

d8ta. Some additional combining was done because of the low volume of offonses of a port i cu I or type. The f ina I number of groups es-rab I i shed was 25 

01-thou~h the model can handle 30 groups. Further refinements of agency 

datn cal loction processes could provide more detai I among crime types in the 

-future. 

Havintl established a sei of offense groups, data was collected based on 

111.1"1 sot, d process which called for special programming in the case of 

146 

automa-red data, or hand tal I ies when data 

programming was no-r feasible. - was pot automated or when special 

Po lice data from UCRs was reorga' d' t . I ished and hand -rail ied S . I nlze. In 0 !he new crime types esi"abamoun-r of manual reOrga~iza~~~~awa~r~~:~m~lng which re~uir~d a limited 

for the county An addit" I fO generate District Court data 

provide detention informa~~~~. program was used with District Court data to 

Circuit Court data was not auto t d t 

was thus h<Jnd tal I ied from docket bom~ e. o~h he county or state level and 

the categories establ ished. Publ ic g ~ I~. ~ Court ~Ierk!s Office into 

I ied from attorney reports avai lable ~ne~her tlntformat~on was al~o hand talDefender. • e s a e office of the Public 

Data concerning proba-rion and I d' . f rom automated reports but requ i rel:~~ i ~ i ~n I I ncarcel rat I on, was. ava i I ab I e 

na manua organization for use. 

from Due to -rhe time necessary for a defendant to move through the system 

ered ~~r~~: ~~s~iSPosit!on and ~omplete sentence, data for the periods covthus I't was case ~Id not, In aI' cases, involve the same defendants' 

necessary 10 further' t'f th d ' 

was done by establ ishing the numbJUS I y , e ata between agencies. This 

and calculating the branching ra-r~~sOf crl~~s r~ported as the base figure 

which was largely a manual task. accor Ing 0 actual data avai lable, 

outpu~twi~~s,~~~o~=!n~~!:n+~a~s:~r:e~~:~ ~~: inpu:' a~ wei ~ as t~e model's 

data do not vary t f f "require manipulations of the 

not provide an act~~1 ~~ct:~~ ~~a~~ty whl'.;:.recognizing,:that the model does 

e opera I Ion of the system. 

147 
APPENDIX. A CASE STUDY--ALLEGHENY COUNTY, PENNSYLVANIA* 

* 

The practical utility of the JUSSIM model can 

perhaps best be demonstrated by citing an example of 

its implementation in a local CJS planning agency. 

A 11 egheny County is located in western Pennsyl van; a 

and incl udes the city of Pittsburgh. The total pop,· 

ulation of the county is about 1,600,000. In 1969, 

a Regional Planning Council was established there 

under the auspices of the Pennsylvania Governor's 

Justice Commission. This council was charged with 

the responsibility of developing plans to improve 

the CJS in Allegheny County (AC). The council itself 

is composed of various officials from different parts of the CJS and some "citizen members. II There is also 

a full-time professional planning staff. 

The JUSSIM model was applied to AC and the 

county's CJS was described in a data file for the 

JUSSIM computer program. The research involved collecting and refining the necessary data and exploring 

various proposals for improving the county's CJS. 

This discussion includes only a sample of the full 

analyses provided to the planning council. l 

The Model of the AC-CJS 

A simplified flow diagram of the AC criminal 

justice system is presented in Figure 1. This chart 

indicates the processing of all crimes committed in 

the county which come to the attention of the CJS. 

The process is initiated by a crime report or police 

arrest a.nd is terminated in the corrections subsystem. The blocks on the diagram represent processing 

stages in the system and the arrows; ndi cate the fl ow 

paths. The quantities indicated on the chart are 

1. Kenneth, Fields, Michel A. Lettre, and 

Richard Stafford, IIA Description of the Allegheny 

County Criminal Justice System" and Jacqueline Cohen, 

Michel Lettre, and Richard Stafford, "Analysis of the 

Allegheny County Criminal Justice System: Present 

Operations and Alternative Programs," Urban Systems Institute Reports, Schoo1 of Urban and Public Affairs, 

Carnegie-Mellon University, Pittsburgh, 1972. 

The material in this section is quoted from "Implementation of the JUSSIM 

Model in a Criminal Justice Planning Agency," by Jacqueline Cohen, K. Fields, 

M. Lettre, R. Stafford and Claire Walker; Journal of Research in Crime and 9_ttJjn9~nct, June 1973. 

148 

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the estimated annual flows through each processing 

stage for the county. 

The AC-CJS is made up of five major subsyst~s: 

the police, the minor judiciary, the Court of COlT1T1on 

Pleas, adult correction, and the juvenile subsystem. 

A unit of work typically enters the system in the 

police subsystem in the form of an arrested individual. After arrest, juveniles are referred to the 

Juvenile Court and enter the juvenile subsystem while 

adults are referred to hearings in the minor judiciary subsystem. 

All hearings in the minor judiciary subsystem 

are conducted by magistrates. Those adults arrested 

for minor sUlT1T1ary offenses receive sUlT1T1ary hearings before a magistrate. At the sunmary hearing the 

magistrate determines the guilt or innocence of the 

defendent and sentences guilty offenders. Adults 

charged with the more serious indictable offenses 

are arraigned before a magistrate and bail and counsel 

decisions are made. At a subsequent preliminary hearing, the magistrate decides whether there is a 

prima facie case against the defendant. The defendant may be dismissed at this point or held for the 

Court of Conmon Pleas. 

If the defendant is held for court, a bill of 

indictment is prepared and sent to the grand jury. 

Indictments upheld by the grand jury are sent to the 

court. Before trial the defendant is arraigned in 

court where the charges may be dismissed (indictment 

quashed or demurrer sustained) or the D.A. may choose 

not to prosecute~ If the case goes to trial the defendant may plead guilty or elect to be tried by a 

judge or jury. 

Convicted offenders may be sentenced to probation or to a state institution. The persons in 

institutions can then be paroled before completing 

their sentences. If the conditions of probation or 

parole are violated, the individual may be sent to 

a state institution. When t~,~ term of the sentence 

is completed the convicted offender is unconditionally released back into the community. 

The juvenile subsystem is generally less formal 

than the adult subsystem. Almost all cases referred 

to Juvenile Court are first reviewed by a probation officer. After this review, the probation officer 

may deal nonpunitively with the case himself or hold 

the case for a hearing in Juvenile Court. At the 

hearing the judge can release the juvenile or refer 

him to a correctional agency. 

150 

Variou~ d~ta were needed to qua~tify this structural descr~Pt1on of theAC-CJ~. These included the flows 

through ea:n sta~e, ~h~ ~ranch1ng ratios, the workloads. 

the_ resource avallab~11t1es, and the unit costs. These' 

datd we:e collec~ed trom a variety of different sources. 

EXdct f1gures were often obtained from the office files annu~l reports, and budgets of the various agencio~ , L;ck1ng these sources, many of the officials and ;~~loyees o ~he system wer~ able to supply reasonable estimates. 

The",e we:e the pnmary SOUf'ces of data; the secondary 

sources 1ncluded estimates from other jurisdictions2 and 

Table 1. SUMMARY OF DAJA ON RESOURCES UTILIZED IN THE 

ALLEGHENY COUNTY CRIMINAL JUSTICE SYSTEM 

'. , 

Total Units 

Available in Stages a Year Per Where 

Resource Units Individual Resources Cost/Unit Resource Are Applied Source of Data County Detective Hours 

Co $ 7.16 1,700 2, 3 unty Patro I men Ho urs 6.75 1,700 Primary 

Pittsburgh Detective Hours 3 f>rimary 

Pittsburgh Patrolmen Hours 

8.42 1,700 2,3 Primary 

District Magistrate Hours 

7.61 1,700 3 Primary 

Pittsburgh Po I ice Magistrate Ho urs 16.47 542 4,6,7,8 Secondary 

Detention Days 55.38 542 6,7,8 Primary 

Common Pleas Judge Days 

7.00 365 7,8,13 Primary 860.50 211 11-14 Public Defender Days Primary 118.00 211 11-14 District Attorney Prosecutor Days Secondary 100.00 211 11-14 District Attorney Indictment Hours Secondary 13.11 1,610 Grand Jury Hours 9.'10 Secondary 129.39 1,266 Behavior Clinic Days 10 Secondary 387.00 230 Pre-Sentence Investigation Days - Primary 62.82 230 County Probation Officer Years - Secondary 69.00 35 16 State Parole Officer Years Secondary 480.00 35 19 State Institution Years Secondary 4,438.00 Juvenile I ntake Officer Days 1 17,18 Primary 

Juvenile Judge Days 

76.67 230 20 Secondary 

Juvenile Prosecution Days 

480.20 211 21 Primary 

Juvenile Probation Officer Years 

76.37 115 21 Secondary 

Juvenile Detention Days 

231.88 35 20,23 Secondary 

Juvenile Institution Years 

18.95 365 20,21 Primary 9,621.40 1 22 Primary 

2: When primary data were not available for the 

analys1s of.Allegheny County, estimates from Los Angeles 

and Co~nec~lcut were o~ten used. The Los Angeles informat1on 1S reported 1n Alfred Blumstein and Richard 

Larson, "Models of a Total.Criminal Justice System," in 

S ~tems A roach and the C1t , M.D. Mesarovic and R. 

Re1sman, eds. New York: American Elsevier, 1972}. 

pp. ~09-5l. The Connecticut data are found in Jacob 

Belk1n.and Alfred Blumstein, "Methodology for the 

AnalYS1s of Total Criminal Justice Systems" Urban 

Syst:ms Ins~itute Working Paper, School of'Urban and 

f~~6~c Affa1rs, Carnegie-Mellon University, Pittsburgh, 

151 

'-
various combinations of primary and secondary sources. 

Table 1 summarizes much of this basic input data for 

the resources in .A 11 egheny County. Each resource is 

listed along with its unit cost and annual availability. 

Wherever possible, the unit costs include the costs 

of administration and backup personnel. The table 

also indicates the processing stages where the resource is utlized and the source of the data. 

Analysis of the AC-CJS 

With this basic data on CJS operations, the JUSSIM 

model can be used to generate a quantitative description of the present system. This description includes 

the distribution of system and subsystem costs, the degree of flow penetration, and the resource requirements, thus providing the planning council with an 

empirical basis for examining the operations of the 

various agencies in the CJS. 

For example, Table 2 shows the distribution of 

the total adult system costs among the major subsystems. As noted, the correction subsystem accounts for 

almost two-thirds (63.9 percent) of total adult CJS 

costs. 3 Depending on the crime type, the major cost 

Table 2. DISTRIBUTION OF TOTAL ADULT COST AMONG THE 

MAJOR SUBSYSTEMS FOR SELECTED CRIME TYPES 

Percent of Percent of Percent of 

TOlDl Adult Totul Adult Total Adult 

Cost for Cost for Cost for 

Police Court Correction ;rNol Adult Cost 

All Crime Types 14.3 21.7 63.9 $17.085,900. 

Murder and Non·negligent Manslaughter 1.3 8.1 90.5 2,231,700. 

Farcillic Rape 5.8 22.5 71.6 386,500. 

r10bbcry 4.3 12.1 83.6 2,774,800. 

Ag!lravntoo Assault 13.6 33.1 53.3 933,000. 

Burglary 12.3 22.2 65.5 3.033,600. 

Auto Theft 45.1 40.9 14.0 344,400. 

Simplo Assault 6.1 34.0 59.9 295,000. 

Stolen Property 10.6 18.4 71.0 310,000. 

Commercial Vice 16.5 14.6 68.9 318,600. 

Narcotics 10.0 19.7 70.3 2,143,300. 

Drunk DrivlI1g 3.0 70.9 26,'i 391,100. 

Disorderly Conduct 31.0 56.2 12.8 113,400. 

3. It should ~e noted that Table 2 represents the 

distribution of only those system costs directly attributable to criminal reports and arrests. For example, the 

police cost here includes only the cost of dealing directly with specific crimes (e.g., responding to calls, 

making investigations and arrests). The cost ?f maintaining patrols and providing other public ~~rvices like 

traffic control are not included. 

152 

component can b~ ~olice (e.g., auto theft), courts 

(e.g., drunk dnvlng), ?r correction, (e.g., robbery). Table 2 also shows the large system costs associated 

with robbery and burglary which account for one-third 

of the total system cost. 

The court subsystem in Allegheny County includes 

the minor judiciary~ detention, and the Court lff 

COITmon Pleas. A summary of the flows and costs of 

this court subsystem is shown in Table 3. The first 

column indicates the percentage of those arrestees 

appearing at the minor judiciary who are subsequent1y held for court for Selected crime types. In all cases 

those not he~d for court are either disposed of at a 

sunTJ1ary hear'lng (}r dismissed at the preliminary hearing: 

Column 2 of Table 3 shows the input to the court 

expressed as a percentage of the total arrests for 

each selected crime type. These percentages indicate the probability of penetrating to the Court of 

Common Pleas after arrest, and they reflect the effect 

of both the minor judiciary and the Juvenile Court in 

limiting the flow of arrested individuals into the 

Court of Common Pleas. It is clear that with the 

exceptions of murder and narcotics cases, the majority of arrested individuals never reach trial in the 

Court of Common Pleas. 

The remainder of the table presents the distribution of totai court costs among the magistrates 

detention, and the Court of Common Pleas. The co~rt 

of Common Pleas is the most expensive component of 

this subsystem. Its costs are generally eight times 

Table 3. FLOWS AND COSTS IN THE ADULT COURT 

SUBSYSTEM FOR SELECTED CRIME TYPES 

Percent of Percent (If Percent of TOWI Court CostS Cases at Armstces 

Magistrates' DispOS0d of 

Hearings in Cour< of C(lurt of 

Crime Type Court 

Held for Commo'] Totul Court Minor Common Pleas Cost Judiciary Dntcntion Picas 

All Crime T;:pC's 22.7% H~.:2% $3.712.600 8.0% 25.5% 66.5% Murder and Non·negligent 

Manslaughter 100.0 100.0 182,300 4.7 47.1 48.2 Robbery 64.5 40.9 334,400 2.6 15.4 72.0 jl\.ggravated Assault 39.8 39.9 308,600 5.7 12.9 81.4 Burglary 69.2 36.2 672,500 2.9 26.2 70.9 Larceny 31.5 14.0 279,100 4.2 29.8 66.0 Stolen Property 53.5 42.1 57,100 7.0 29.8 63.2 Weapons 55.a 48.1 45.000 8.0 26.9 65.1 Narcotics 64.2 55.7 421.BOO 6.8 12.9 80.3 Gambling 35.8: 44.9 54,100 14.6 0.0 85.4 

153 
.'1"'" _--------....... ------.-------

r 

the costs of the minor judiciary, even though the 

magistrates handle more cases than the Court. Detention costs also constitute a sizable portion 

(26 percent) of the total court cost. 

Tables 4 and 5 illustrate the chances of penetrating to the correction subsystem after entering the CJS. Table 4 presents the proportion of arrested 

adults sentenced to probation or incarceration for 

several representative crime types. The chances are 

relatively high for the serious pr'operty crillies (.54 

for robbery, .51 for burglary) and quite low for vice 

crimes (e.g., .01 for gambling). Table 5 presents 

the percentage of juveniles committed to a juvenile 

correctional institution after referral to Juvenile 

Court for several crime types. Surprisingly, the 

probability of being institutionalized appears to be 

highest for the strictly juvenile offenses of truancy and ungovernable behavior--perhaps because the court 

often suppresses more serious charges so that the 

Juvenile will not have a record of criminal charges 

against him. If this is the case, t~en the juvenile is really being institutionalized for more serious 

criminal acts, although fOT~ally charged with a minor 

juvenile offense. 

The data on the detention or bail status of 

arrestees were also analyzed. They showed that the 

probability of dismissal at the preliminary hearing 

is higher for arrestees on bail than for thOSE held 

in detention. Over all crime types, the probability 

of dismissal for arre~tees on bail is .521, while it 

is only .356 for arrestees held in detention. There 

are at least four hypotheses that could explain the 

phenomenon: 

Table 4. PROPORTION OF ARRESTED ADULTS SENTENCED TO PROBA· 

TION OR INCARCERATION FOR VARIOUS CRIME TYPES 

Number or Pel'cent of 

Adults Arrested Adults 

Sentencro to Bei nJ Sentenced 

Probation or to Probat ion or 

Crimo Typp Adult Arrests inc-dr~l:!(ation tncarCeiation 

Ml.rder and Non·nngHgent Mansloughter 86 58 67% 

Robbery 464 249 54 

A!jgravated Assoliit ~O11 204 20 

Burglary 1,103 560 51 

Larceny 1,038 189 18 

Slolen Prop()rty 373 88 24 

''''capons 383 49 13 

Narcotics 1,687 623 37 

Gambling 774 8 1 

,<I..ll Others 25,413 945 4 

154 

I 

1. Those arrestees out on bail have both the 

money and the time to secure counsel for their de- f~nse, thereby increasing the probability of dismlssal. 

2. The police magistrates ~ake a bail/detenr;on decision at the preliminary arraignment that 

places in detention those arrestees who are more lik~ly to ~av: c~mm~tted the crime and more likely 

to Jump ball lf lt lS made available to them. 

3. Arrested persons who know they are guilty 

are.more likel~.to accept detention, with the expec- tatlon of gettlng a credit on the sentence for the 

time served in detention. 

4. There is a stigma associated with detention (whether it is caused by the arrestee's indigence or his potential risk out on bail) that 

increases the probability that a magistrate will 

hold an individual for trial. 

While the model does not suggest the reason 

for the occurrence of this phenome~on, the process 

of data collection and analysis reported here fosters 

identification of this kind of phenomenon. The 

explanation must be sought through a more detailed 

analysis of individual cases. 

This type of descriptive analysis is often 

the basis for new insights into the functioning 

of the system. It also documents the extent of 

Tnble 5. INSTITUTIONALIZATION OF JUVENILES 

FOR VARIOUS CRIME TYPES 

Number of 

Total Juveniles Juveniles ComReferred to mitted to an Crime Type Juvenile Court Institution 

All Crime Types 8,263 530 

Aggravated t\ssault 262 32 

Burglary 823 61 

Larceny 930 29 

Auto Theft 789 96 

SifT' pie Assault 558 12 

Va'1dalism 607 9 

Nc,rcotics 502 29 

Disorderly Conduct 1,155 12 

'nunaway 637 38 

'fruancy 230 46 

Ungovernable 533 102 

All Others 1,237 65 

155 

-----------;-----------------------

Percent of 

Juveniles Commitled to an 

Institution 

6.4 

12.2 

7.4 

3.1 

12.2 

2.2 

1.5 

5.8 

1.0 

6.0 

20.0 

19.1 

5.3 
known prob 1 ems ; n system operati ons. In either 

case the results of the analysis frequently suggest changes that might improve the operations in question. These changes can then be "tested" with the 

JUSSIM model. By using the extensive data base and 

the analytical methods provided in JUSSIM, planners 

um anticipate and evaluate the indi.rect and sys- tem-wide effects of any change and, with this kind 

of information, can compare the merits of various 

programs and make choices among them. 

The problem of ba i 1 refonn has been se 1 ected 

to demonstrate JUSSIM's usefulness as a planning tool. In 1970, approximately 3,80CtJ persons from 

Allegheny County were detained for some period of tirn~, and another- 9,000 were released on surety bond 

after paying a premium to a bondsmal1. There are a 

great many disadvantages associated with this bail/ 

-~-- ----~---.-------------, 

[ 

ARRESTF.ES (15,548) 

,--...,.....-_-r---t-----.J 

PHELIMINARY ARRAIGNMENT 

SUHC:W:CASH BONO 

(:1M!)} . 

NOM/O.R, 

/2,616) 

DETENTION 

/3,837) 

f'f'lELIMfNARY HEARING 

HEl.D FOB COURT 

AWAI1"ING J)ISPOSfTION IN 

counl or CO~i1~.10N PLEt\S 

CONVICTro IN T!liAL iN 

COUHTO;' COMMON PLI:A$ 

SlJR£:TYI 

CASH BOND 

/4.879} 

SUHETY! 

CASH BOND 

SURETYI 

CASH BDND 

(5,403) 

DETEN, 

TION 

(8,276) 

DETEN· 

TION 

/2,507) 

NOMI DEHN 

O.R. 11'JN 

/1,7951 (1,0781 

Figure 2. ESTIMATED FLOW OF DEFENDANTS IN 

ALLEGHENY COUNTY IN 1970 

156 

The problem of bail reform has been selected 

to demonstrate JUSSIM's usefulness as a planning tool. In 1970, approximately 3,800 persons from 

Allegheny County were detained for some period of 

time, and another 9,000 were released on surety bond 

after paying a premium to a bondsman. There are a 

great ~any disadvantages associated with this bail/ ~etentlon system. For those detained in jail these 

lnclude the loss of liberty, the loss of one's in- c~me and perhaps,one's job, the disruption of family llfe, and the stlgma of guilt often associated with 

being in jail; for those released on bail, there is 

the cost of the,.bond premium. While it is often 

difficult to measure all the social costs associated 

with these disadvantages, some of the economic costs I 

can be estimated. The JUSSIM flow and cost data 

are particularly useful in this analysis. 

Figure 2 is a graphic representation of the 

estimated defendant flow from preliminary arraignment to tri ali n A 11 egheny County. There are three 

defendant statuses at each step in the process: 

(1) released on sur~ty or cash bond, (2) released 

on nominal bond or own recognizance, or (3) detained. 

At present there are four points where an arrested 

individual can be released from detention: (1) at 

the preliminary arraignment by posting bailor being released on nominal bond, (2) at the prelin):inary 

hearing through the dismissal of charges or release 

on nominal bond or bail, (3) sometime before trial 

by posting bailor on nominal bond after filing a 

special petition to the court, and (4) after trial 

through an acquittal. 

Of the 15,548 defendants at preliminary arraignment, 16.8 percent (or 2,616) are released on nominal bond or their own recognizance (O.R.). 58.5 

percent (or 9,095) are released on surety Dr cash 

bond, and 24.7 percent (or 3,837) are held in detention until their preliminary hearing. Only 

8,276 of the original 15,548 defendants are held 

for court aftl!{~ their' pr'el iminarj hear'ing. UT 

those held for court 30.3 percent (or 2,507) are 

detained at All~gheny County Jail. 10.8 percent 

(or 890) are releused on nominal bond or O.R. and 

59.0 percent (or 4,879) are released on surety or 

cash bond. This released population of 5,769 is 

ultimately augmented by another 1,429 defendants 

who are released from jail on nominal or surety 

bond at some time before their disposition in the 

Court of Common Pleas. This additional release 

of defendants changes the defendant status at the 

time of trial to 21.7 percent, 65.3 percent, and 

13.0 percent on nominal bond, surety bond, and in 

157 
detention, respectively. This means that.of the 

original 3,837 defendants detained by mag1strates 

after preliminary arraignment, 2,759 (or 71.9 yercent) are later released either through the d1Smissal of charges or on some form of bond or recognizance before trial. In addition, there are an 

estimated 291 persons detained from the ~ime.of 

preliminary arraignment right through tr1al 1n ~he 

Court of Conmon Pleas who are subsequently acqultted 

at trial. 

If the current procedures for gaining information about defendants were improved, many of the 

defendants noW released after spending some time in 

detention could be released much earlier. This 

earlier release of defendants would represent ~ 

substantial reduction in the detention popul~t10n 

and in the costs of detention. These potentlal 

savings in detention costs can be estimated from 

the flow and cost data in the JUSSIM model. The 

results are presented in Table 6. 

If all the defendants currently released from 

detention were released immediately after their preliminary arraignment, the annual detention population would be reduced by almost 80 percent and the 

savings in detention costs would be over $475,000. 

It is however doubtful that even the best bail relea~e progra;' would be able to select ~hese 3,050 

defendants from the 3,837 presently detalned. 

Nevertheless, a reasonably achievable bound on the 

detention population after preliminary arraignment 

Table 6. POTENTIAL DETENTION COST SAVING IF ALL 

CURRENTLY RELEASED DEFENDANTS WERE 

RELEASED AT PRELIMINARY ARRAIGNMENT 1------------- Average 

Number of 

NumiJer of Detention Detention 

Defendants Pays P(Jr Cost Savings 

Current (at $7.00 per Day) P,)ifH of RRlcJSC I Released Detentioner 

a. Dismissed at Preliminary Hearing 1,330 6 $ 55,860. 

b. Bailed Before Trial 1,429 30 300,090. 

c. Acquitted in Trial 291 60 122,220~ 

$478,170. , Total 3,050 I ""., .,.' ...... ,. ; ",-.",.~ . -"1._"~".''''"_~'"'''''-'''' 

158 

might be the current percentage of defendants in detention at the time of trial--namely, 13 percent 

(see Figure 2). This would decrease the present 

detention population by 1.816 defendants. As:uming 

that these defendants come proportionately from the 

three populations in Table 6, the resulting savings 

in annual detention costs would be about $284,000. 

The same flow data can also be used to estimate 

the annual private-expenditure on ban premiums. At 

present a total of approximately 6,920 of the 9,619 

defendants released on surety bond are subsequently 

dismissed either at prel iminary heaY'ing or at trial 

(e.g., indi~tment quashed, nolle prossed, or acquitted). 

Using the district attorney's bail schedule we esti~ 

mated that these persons pay an average of $102 each 

in nonrefunded bond premiums, or a total of $705,840 

annually.4 

Almost all of this private cost of release for 

dismissed defendants can be avoided by changes in 

the present bail procedure. There are essentially 

two strategies likely to affect the defendant expenditure on bond premiums. Insuring the early discovery 

of information about the defendants can increase the 

number of defendants released on nominal bond or on 

their own recognizance. The court could ftlso implement a cash bond system where the defend~~t pays an 

8 percent premium to the court, to be refunded (less 

a small service charge) if he is dismissed. If he 

is convicted, the premium goes toward the payment of 

court costs. The combined private savings in bail 

premiums and public savings in detention costs from 

changes in the bail procedures would be almost 

$1,050,000 annually. 

The implementation of these bail reform strategies 

would require additional manpower expenditures by the 

court. Defendants must be interviewed to obtain the 

information needed to decide on their release on nominal or cash bond. This information must then be 

verified. Also, there must be some follow-up for 

defendants releas~d on nominal bond to assure their 

appearance in court. This will include written notification and telephone contacts about court appearances. 

4. The average premium of $102 per defendant 

was estimated by taking 8 percent of the weighted 

average of the current bail schedule established by 

the D.A.'s office . 

159 
There are several alternative bail programs that 

could be implemented. Among them ;s a program to extend the use of nominal bond. This program would 

primarily affect indigent defendants. Alternatively, 

a court-operated cash deposit system could be imple- mented for those defendants presently eligible for 

surety bond. And finally, the program to extend the 

use of nominal bond could be combined with the courtoperated cash deposit system. In addition to the redeeming social merits of t~ese programs, each also 

has some economic merits which can be explored with 

the JUSSIM model. 

The potential savings and costs associated with 

each program can be determined from existing flow and 

cost data with the unticipated workload data for the 

programs. A typical bail-release program would require 

at most the following average workloads for a bailable 

defendant: .5 hours for interview, 1.0 hours for verification, and 1.S hours for follow-up. It can also be 

assumed that the annual cast for each new resource in 

the bail program will be $12,500 and that each resource 

is available 1,840 hours per year (230 days/year X 8 

hours/day). When these assumptions are combined with 

the present flow data, the expected annual cost of a 

new program can be calculated. 

Table 7 presents a comparison of the costs and 

savings, both public and private, associated with each 

of the three bail programs. The highest ratio of 

total savings to costs is found for the court-operated 

cash deposit system (alternative 2), although all the 

savings here are private with no public savings. This 

program would save the cost of nonrefunded bond pre- miums for many defendants currently released on surety 

bond. However, it would not aid those indigent defendants who will still be unable to raise the cash 

deposit required. The combined program (alternative 3) 

has the dual advantage of affecting both indigent 

defendants and those on surety bond while also having the hig.hest net total savings of any of the programs ($850!500 compared to $354,500 and $537,000). This 

information was made available to the planning council 

to aid in its deliberations about bail reform in the 

county. _ .. 

160 

., 

'f 

I 

Program 

Table 7. POTENTIAL SAVINGS AND PROGRAM COSTS 

OF NEW BAIL RELEASE PROGRAMS 

~ 

Defendants 

to be Potential Public 

Defendants Affected 1 ntervlewed and Private Savings 

1. PROGRAM TO Earlier Release of Currently 

AFFECT INDIGENTS Released Detentioners 2,759 $ 356,000. 

(det. costs) 

Detained through Trial 1,078 -

Extended Nominal Bond to 40% 

of Bailable Population at Arraignment 11,711 211,000.* 

- TOTAL 15,548 $ 567,000. 

2. COURT-OPERATED Dismissed at Preliminary Hearing 

CASH DEPOSIT SYS· or Trial 6,920 $ 637,000." 

TEM FOR SURETY 

DEFENDENTS Convicted 2,699 - , .f.~~ ... r: - TOTAL 9,619 $ 637,000. 

.> 

3. EXTEND NOMINAL EarlilJr Release of Currently 

BOND AND CASH Released Detentioners 2,759 $ 356,000. 

DEPOSIT SYSTEM (det. costs) 

(262) 24,000~ 

Detained through Triol 1,078 - Extended Nominal Bond to 40% 

of Bailt-ble Population at Arraignment 4,684 $ 211 ,OOO~ 

Surety Defendants Dismissed at 

Preliminary Hearing or Trial 5,129 472,OOO~ 

Convicted Surety Defendants 1,898 -

- " for premo costs 

TOTAL 15,548 $1,063,000. 

161 

, 

Expected Costs 

$212,500. 

(for 17 resources) 

$100,000. 

(for 8 resources) 

$212,500. 

(for 17 resources) 
Part IV. 

USE OF POPULATION DATA 
CHAPTER 14 

DATA USE TOOLS AND TECHNIQUES AVAILABLE 

TO CRIMINAL JUSTICE PROFESSIONALS 

Ronald E. Crellin, George L. Farnsworth, 

and Leo Schuerman 

I NTRODUCTI ON 

The full potentia1 of available data use tools and technologies, particularly as .they relate to small areas, has yet to be fully realized 

by professionals working in criminal justice planning. The purpose of 

this chapter is: 

• To describe the available computer based tools and techniques for processing, structuring, linking, analyzing and displaying 

small at'ea data (census tracts and specialized subcity administrative districts). 

• To provide a selected reference to these products so that potential 

users have a useful guide to their accessibility and use. ,: .. 

GEOGRAPHY 

Geography is an important element associated with most items of data. 

Any official involved with the distribution of services, public or private, 

must also be concerned with the distribution of the potentjal client population in relation to the supply and demand for these services. 

Having current and accurate data on a timely basis by appropriate 

geographic areas is critical to the decisionmaking process. One of the. 

problems in obtaining comprehensive data profiles of geographic areas of 

interest and cross tabulating administrative data with socio-economic data 

from the census is the incompatibility of geographic areas for which data 

is collected and reported. 

In the criminal justice field in particular, it is critical to know 

precisely at a point in time 

• t"hat happens 

• to whom 

• at what location 

• with what impact 

. 165 
and how this compares to what happened at some prior time and to predict ~hat is likely to occur at some future point in time. Only then can. 

effective decisions be made about what ~o do, what resources to provlde, 

and what combinatiDn of services to dellver. 

National planners find access to national and sta~e ~evel data 

re1atively easy. Data at the substat~ level ~ and ~artlcu,arly at the 

city and subcity levels, is becoming 'Jncreaslngly lmportant~ ho~ever, 

because of the need to d'i stri bute funds through county, reglona I, and 

10cal agencies. 

Regions for which census data are of interest may include: 

• 

• 

• 

• 

• 

• 

• 

• 

The nation 

A state 

Standard Metropolitan Statistical Area (SMSA): o~e of 243. 

geographic units in the nation, ~ncluding the ent~re_area l~ 

and around a. city that forms an lntegrated economlC and soclal 

system. 

A county (or in Lousiana a parish) 

A city 

A census tract: small areas into which cities and their adjacent 

areas have been divided for statistical purposes 

A city block group: a region within a city that includes more than 

one city block and forms a unit 

A city block: a well-defined rectangular or irregular piece of 

land bounded by streets, roads, streams, railroad tracks or other 

features 

Other units of interest may include a school district or police precinct. 

Definitions of these and other related statistical units are contained in 

the Glossary at the conclusion of this Chapter. 

Although census data is aggregated to the census tract or block level, 

local data is frequently aggregated to individu~l speci~l purpose areas 

such as health district, school district or pollce preclnct. Such geo-. 

graphic disagreements cause problems for potential data users. Theor~tl­

,.:~~JJJ'" data caul d be hand coded to any geographi c areas the user requl res. 

1156 

Practically, ~Iith the large volume of data that planners are now required 

to handle, computer processing is the best way that comparisons can be 

done economically, efficiently and accurately. 

To solve the problem of aggre9~ting data from different sources to 

common geographic areas, the computer must know which addresses are in 

which census tract, school district or police precinct. 

Just as decisionmakers need maps to do manual geographic analysis, the computer needs a computer-readable map to perform these operations automatically. 

DUAL INDEPENDENT MAPS ENCODING (DIME) FILE 

Computer-r~adable maps are already available for over 200 Standard 

Metropolitan Statistical Areas (SMSA's) throughout the country. What is 

needed are: 

, Computers 

• Computer Programs 

• Resources 

• Trained Manpower 

plus an understanding of how to apply this new technology to criminal 

justice planning. 

Computers are accessible to most public agencies, either illhouse or 

through remote terminals. Computer programs are available in the public domain. Resources can be obtained from various sources for sound 

projects. Trained Inanpower with the ne-:::essary combination of skills and 

experience is difficult to find, at the local level. 

The underpinning that allowed the development of the DIME technology 

is a computerized street map called a Geographic Base File (GB~)/DIME 

(Dual Independent Map Encoding) file. Auxiliary computer programs match 

addresses with areas such as census tracts identified in the DIME file. 

DIME files have been created for more than 200 SMSA's. A list of 

these SMSA's is found in the appendix at end of this Chapter. 

To aid potential users of these data products and technologies in 

the solution of service delivery problems in the criminal justice field, 

the following section will include a brief description of the computer 

programs for sorting, structuring, linking and displaying data from a 

variety of sources. 

167 
~~~.------------------------------------------~ 

The GBF/DIME-File can be a useful tool for local governments and. 

otller orga.niza ti on5 concerned with small a rea da ta. Th~.;. . referenc~ fl1 e, 

df:!veloped by the Census Bureau, provides a means to efflclently r~late 

l'ocal data to census data or to other local data through a.ddress lnformat'1on. 

GUF/DIME stands for Ge6graphic Base File/Dual Independent Map 

Encoding and refer; to the computerized file~ cr~a~ed by.the D~M~ ~rocess. 

This approach combines address information wlth lnformatloP ~~tf~clent 

and necessary to describe the urban street network .. By cons'ldenng each street as a series of lines and each intersectlon as a node, the 

entire region covered by the file can be viewed as a series of interfcda "t'd nodes, 1 i nes and enc'losed areas. Other features, such as streams 

or jurisdiction boundaries, may also be defined in terms of segments and 

nodoG. 

In creating a GBF/DIME,·File, each distinctive element repres~nted on 

a map is examined and, at ~oints where streets or other features.lnter- $e~tl end, or curve sharply, a node number is assigned, as seen ln 

rigure 1. 

r. ;.j. vj. ", d I ,f di r "f, \1." (t'd" w! .f'P! ',tff", ) (or nlhpr 

~t ot";jP'. ~:·;Jt'tij t. d!- ,~) tIP t~<f~ff'~/'H~'fH lntprSt~' 1, fInd, 

\. 

Fioure 1, NUMBERING THE NODES v . 

For each segment of a street (i.e., the length bEtween two nodes) tll(~ OU1E. file contains the following information: The "fromll node, the 

"to" node, the street type (e.g, .;lvenue, dr~ive, way, etc.) the address 

range on the rig~t-hand side of the street and the address range on the 

1 Mt~hand side. 

168 

~-~-~-~---~-

Mor~over, each block and census tract is uniquely numbered. These 

geographlc areas (on the 1eft- and right-hand sides of the street segment being defined) are also included for each record ill the GBF/DIMEFi 1 e. 

Figure 2 shows some of the information contained in the GBF/DIMEFile for the 100 block of Atlantic Avenue. 

For each street segment 

a DIME Record contains: 

From 

1'0 Node 

Node 123 

124 @ ~~ \99> p.~~' Street Name Atlantic '\7..1\ • • 

Street TYP,e Avenue ~i\C Left Addresses 101-199 i\...~ 

Right A.ddrcsses 100-'198 P. 

Left Block 38 

Left Tract ·9 \IJIJ @ 

Right 

Right 

Tract 

Blocll 

9 

31 

~ 

Figure 2. INFORMATION FOR 100 BLOCK OF ATLANTIC AVENUE 

The term DIME then refers to the fact that th~ basic file is 

created by coding two independent matrices: a) the nodes at the end of 

the line segments and, b) the areas enclo~edr"by the nQdes and line seg- ments. 

The computer constructs these two independent networks--one of line 

boundaries and one of areas--and matches them (Figure 3). 

UNE 

BOUNDARY 

Figure 3. MATCHED NETWORKS OF LINE BOUNDARJF.S AND AREAS 

This will ensure that the resultant network is completely represented and all the land is accounted for. . 

Once a map is digitizedi"the computer can use this information to 

plot a complete replica of;the source map. 

169 
Dime Applications 

DIME, which grew out of the research efforts of the C~nsus Bureau, 

has been found to have applications for local us~rs as wel i. State and 

local governments, and private dat~ users ~ave d1s~overed.that th~ GBF/ 

DIME-File cou1d be used for geocodlng and 1ntegrat1ng thelr own fl1es 

and records. Moreover, local data could be eff<iciently relate~ to census 

data at various geogra~hic levels and1nformation could be eas1ly 

retrieved by the computer. 

DIME has been used by local agencies in determing the ~es~ 10catio~ 

for child day-care centers, in restructuring police beats, 1n 1m~lem~nt1ng 

carpool programs, in designing school bus routes .. Nu~erou~ appl1catl0ns. 

of DIME technology have also been reported by specla11sts 1n trans~o~tat10n, 

public health, urban and regional planning, law enforc~m~nt and ~r~m'nal . justice communications, marketing, social welfare, pol,t1cal adm1nlstratlOn 

and other fields. 

Available C,omputer Programs 

Mary computer programs for use YJith DIME were developed by the) 

Bureau of the Census. However, the Program.for ~ata Researcn (PDR , of The 

Social Science Research Institute at the Un1verslty of ~outhern 

California continues program maintenance and d?cumentat10n and ma~e~. t 

programs available and pr~vides ~ssi~tance to 1n~erested users. 1S 

of these programs and the1r appl,catlons follows. 

AOMATCH ADMATtH is a computer program package designed.for uses in 

assigning geographic codes to loc~l records uSlng ~ DIME, 

ACG, or Similar geographic base f11e. The system lnclu~es 

a preprocessor, which standardizes street addresses subJect 

to variability, and o. matcher, which links geographic base 

fi 1 es to 1 oca 1 data flil es. ADMATCH is used for address 

related geocoding. T~is involves adding coordinates, c~nsus 

designations, or loca\ geographic codes to local data f,les 

containing house addrdsses. 

The ADMATCH systems. both OS and DOS were written in IBM 

System/360 Assernb'j er Language and are i t1lended for users 

of IBN System/360 computers. The ADMATCH/OS system is 

still available but has largely been s,uperceded by 

ZIPSTAN/UNIMATCH listed below. 

A COBOL ver\)i on of ADMATCH, whil e not prepared by PDR 

staff, is available but is somewhat less efficient than 

the ALCversions. 

170 

C-MAP C-MAP is a relatively simple pr'ogram written 'in Basic FORTRAN 

for producing choropleth maps. Input is via control cards 

plus a scan-line file which may be hand coded or automatically 

generated from DIME files using the DACS and SCANGEN programs. 

C-MAP produces maps up to 5 printer sheets in width and any 

desired length. WhiJe not containing a11 of the options of 

SYMAP, it is much more efficient both in terms of core 

utilization and machine time. 

CARPOl The CARPOl computer program is designed as a large-scale 

approach to automated carpooling. The program generates 

lists of potential fellow-riders from which a candidate can 

create his carpool group. 

CI\TS 

CENSlCT 

COIN 

The program has a primary search radius based on geographic 

areas and a secondary search based on non-geographic criteria 

such as common workdays, hours, ~nd driver/rider specifica- tions. While developed for use with the DIME geographic 

base file, geographic units other than census tracts - zipcodes, traffic zones, telephone exchange areas - may be used. 

CARPOl is available, in FORTRAN IV and can be run on most 

computers. 

The CATS program allows users to generate reports from the 

First Count Summary Tape. The system provides for data 

record selection, item selection, aggregation, data manipula- tion, and bar chart generation. 

The CATS program i ; written in COBOL and can be run on any 

machine with a suitable COBOL compiler. 

This program searches a Census Summary Tape and extracts 

records for a geographic-coded area as specified by the user. 

Up to five copies can be made of a new tape and the user may specify a different block size for each copy. 

The CENSlCT program is written in ANSI COBOL and will run on 

any computer having a suitable COBOL compiler. 

The COIN program will add geographic (x/y) coordinates 

to a DIME file. These coordinates must be present if the 

DIME file is to be used for calculating areas or locating centroids of user-specified areas contained in a DIME file. 

In addition, coordinates are essential for network analysis 

and almost an types of computer mapping. 

The COIN program is written in FORTRAN for an IBN Systemi360 

computer. 

171 
CRAM 

OACS 

DCODE 

DIME 

Basically, CRAM is a generalized system for determining the 

service area for a set of facility locations. In its most 

general form, service areas are constra ined by faeil ity 

capacities, and travel times. 

The system uses ~ DIME file as its network base and in 

addition requiries demand and facility capacity inputs. 

!tis anticipated that the full CRAM system will consist 

of five (5) separate FORTRAN programs and two standard 

sort passes. 

The DACSprogram is a flexible computer program for computing at'eas and centroids of user-specified blocks, census tracts 

0'1' othe'r areas defined in a DIME file. For example, centroid 

locations are requtred for input to GRIDS and some other 

computer mapping packages. Options have been programmed to g(~nerate SYMAP A-OUTLINES adjacency 1 i sti ngs, and areas in 

acres, square feet or square miles. (Also see the description 

of the SCANGEN program and the C-MAP progr'3m.) 

OACS is written in FORTRAN IV. 

The DCODE program adds special local area codes to the DIME 

file. These spec'ial local areas may ·be regions or districts 

not standard to the DIME file that are desired for geographic 

coding and mapping. The program reads a user-prepared 

dict'lonary and the input DIME file and writes a new copy of 

the file with codes included. In addition, its edit pro- cedure checks for coding errors in the input dictionary. 

The DCODE program is written in FORTRAN and will run on any 

computer with a suitable FORTRAN compiler. 

The DIME - File Creation and Editing Package was designed for 

any city or rural area who wish to create their own DIME 

file. 

The package is divided into two major sections. The first 

section deals prJmarily with the clerical procedures for the initi~l DIME file creation. The second section describes 

the computer programs used in converting and editing the 

manually encoded data to a master file for machine use. 

The computer programs provide the capability for c'('eating 

an automated DIME file, topological editing, address 

editing, and inserting corrections. In addition, digitizing 

instructions and a coordinate insertion computer program 

are included. 

172 

n 

DIME ' The topological and address editing computer programs may 

can't. also be used b.v SMSA's to edit standard census files. 

DUPEPAL 

._ FIRST 

COUNT 

REPORT - ER 

FOURUSE 

G 

The t~ree basic.p~ograms which make up the DIME - File 

Creatlon and Edltlng Package were written in FORTRAN for an 

IBM System/360 computer. 

DUPEPAL, written in FORTRAN IV, contains two programs. 

DUPENODE~ lists by census tract the node/maps which occur on 

a DIME flle and flags all detected duplicate nodes. 

.Pi\LS g'enerates adjacency 1 i sts for- an area code from' a DIME' file. 

The.FIRST COUNT REPORTER is a series of three (3) programs deslgned to produce information from the First Count Census Summary Tapes. 

1) Reporter:. 

First ,Count Reporter is a program designed to produce 

a standardl zed I~eport from the Fi rstCount Summary Tapes. 

Input may be,a Summary Tape or a CENSLOT-produced tape. Data 

may ~e _ orgam zed, by standard geographic codes or by 

speclflcally deflned areas. If special area codes are used 

the CODER program must rewrite the input tape. ' 

2) Coder 

Local area codes may be added to each record on a conv~rted t~p: ma~ing it possible to organize data to the 

user s speclflcatlons for the final report. Local areas 

must be defined in terms of Census areas. 

3) Tep (Tape Conversion Program) 

The Tape Conversion Program converts the Cen~us Bureau's 

standard F'i rst Count Tape from character to bi nary format. 

The First Count Reporter programs are written in FORTRAN. 

They.can be run on any computer having a suitable FORTRAN compller. 

The PD~ staff has developed a system of pro:,;rams for accessl~g the Fourth Count Summary tapes. These programs 

are des~gned to handle Files A, Band C, both for the po~ulatlon and,h~using counts. Each prograw is intended to p~rform a speclflc set of tasks that result in an output 

fl1e that can s~and alone or serve as input into another 

program. The alm of the pr~9ram is to reduce data handling 

173 

rw 
GRIDS 

INDEXER 

SCANGEN 

Sfr:S 

TALLY 

problems encountered by the general user in the extraction 

of data from the Census Fourth Count of population and 

housing. The basic design of the program is user oriented. 

The production is a set of programs centered around the 

FORTRAN IV language with one program written in ANSI COBOL. 

By building 3. set of limited task programs, the need for 

a large capacity computer ;s reduced. 

Included in the FOURUSE Package are the CENSLCT 4, V/,RSL 

VARSLCT 4, Driver 4, Reporter 4 and Coder 4, COMPACT 4, 

and CATS 4 programs. 

GRIDS is a genera1ized computer graphics system for users 

of almost any computer system. It will produce various 

types of printer maps using a large variety of files quickly 

and easi1y. The objective is to enable the non-programmer 

to produce ~imple maps quickly and easily with little 

preparation. At the same time, the program can provide 

complex mapping capabilities for the more sophisticated 

users. 

GRIDS is written in ANSI FORTRAN IV and will run on any 

computer system with a suitable FORTRAN compiler regardless of computer word size or operating system. 

INDEXER is a program written in FORTRAN IV which creates 

and prints statistical indices using a simple series of 

commands, requiring no knowledge of programming by the 

user. 

SCANGEN is written in standard FORTRAN IV and is designed 

to allow a user to convert a SYMAP A-CONFORMOLINE deck 

(either clerically prepared or output from the DACS program) into a scan1ines file as input to C-MAP. 

SECS is a program designed to detect errors in the 

digitizing Qf DIME files. All possible pairs of segments 

within each census tract are examined to determine 

whether intersections occur. Intersections constitute 

errors since the DIME file segments are, by definition, c~nnected only at their node point. 

The :ECS program is written in ANSI COBOL and will run on 

any computer with a standard compiler. This program has 

recently been enhanced to product a scaled map of nodes 

and coord~nate locations as an aid to error detection. 

TALLY is a general i zed computer program \'Jri tten in FORTRAN 

IV which creates geographic summary records from any administrative cr other data file. 

174 

j' 

TIDE 

UNIMATCH 

UPDIME 

TIDE sta~ds for Terminal Interactive DIME Environment 

and conslsts of series of computer programs designed to 

allow those who wish to gain an understanding of DIME 

technc;>logy "hands:on" interactive computer instruction ~ow to create, edlt and utilize DIME files for problem 1ng. 

.' ~IDE is ~ritten largely in FORTRAN IV with selected 

1nteract1ve extensions. 

in 

solvUN~MATC~ is an advanced generalized record linkage system w~lCh_w~ll perform most geocoding applications including slmul~t10n of ADMATCH and an improved address matching algorlthm. 

UNIMATCH has many matching capabilities not available in 

ADMATCH.such as the ability to handle building names, s~reet lnte~s~ctions and non-address matching. Data flle~ conta'lnwg statistical information of any type can 

be llnked, ~s long as a common information structure between the flles can be identified and isolated. 

~JNlMATCH consists of three separate pf'ograms, a compiler, 

an a~semblE~~ ~nd an ex~cutor. The UNIMATCH system also p~ov~des utlllty fun~t10ns such as field conversion, dlctlonary searches, record imputation, e~c. 

The s~stem ena?les a u~er to define his own procedure for match1~g.two f11es hav!ng one or more identifiers in common. 

In add!tlon, the updatlng of a file and field conversion 

operatlons can be performed. 

UNIMATCH is written in IBM 360 Assembly Language. 

UPDIME,is a system for correcting, extending and updating D!~E.flles. The ~ystem also has the capability for 

eJ1tlng. It,conslsts of a computer program and is supported 

by ~ set of lndepen~ent publications which are used in up- dat1ng t~e Metropoll~an Map Series. The system uses coding transactlon cards Wh1Ch reflect those geographic changes th~t are t? ?e used as data input to the UPDIME program. Whlle ~equlrlng a larger machine capacity than the Census Bu~eau s CUE program, UPDIME performs topological and other edlt~ on the results of the supplied corrections and 

proV1des faster turn around time and better error detection. 

The UPDIME ~rogram can be used to keep DIME files current a~ chang~s 1 n the urban geography occur conti nua 11y. These 

cnanges lnclude the addition of streets and boundarv line changes. ~ 

UPDIME is written in FORTRAN IV. 

175 

"~
. d d redesigned system for ZIP ZIPSTAN is an lmprove an . ed to replace the current 

STAN standardizing addresses, deSlg~th UNIMATCH. It nenerates 

ADMATCH preprocessor for use w~nformatted street address 

a standardiz~d match keY

f ;ro~ a general fi1e cop::ring and 

fields. It 15 ~l~o use u a 

reformatting utlllty program. 

ZIPSTAN is written in IBM 360 Assembly Language, 

k' on an interactive system for 

The staff of PDR is ,currentlY ~~~ :n91ude the ability to abstract, 

use with DIME files. ,ThlS syste~'lw~S i~~~grate local data in the DIME 

edit and ,update the flles, as we 

'fil e structure. 

S~~LL AREA DATA USE PRODUCTS: DOCUMENTATION AND COMPUTER 

PROGRAM PAC~AGES 

Census Use Study Report Series 

Documents * 

the activities and results of the New This report series documents 

Haven CenSUs Use Study. 

D No 70 26 An overvi ew of the 

oC i . 'General Descripti~. ~arch ~9th~ Ne~PHaven Census Use Study. development and operatlons 0 

2. 

44 A report on the mapping Compute~ Mapping. August 1~69, PP'l computer mapping of census and local data uSlng severa 

techniques. 

, " M h 1970, 23pp. A report on 

3. Data TabulationdActlvl~~e~p'eci:~Ctabulations provided to lo~alt the contents an uses 'census of New Haven, Connectlcu . agencies from the 1967 speclal 

, '970 46pp A report on the 

4. The DIME Geocodi~9 ~1~~e~~Og~~~~i~ ba~e fil~, including a de- d 

development of t e. d the edit system, uses of the file an scription of the flle an . methods for creating a DIME flle. 

1 A cies April 1970, 92pp. A de5. Data Interests of ~oca f ~~~veys'undertaken to explore the needs scription of a serles 0 ~ _ 

of local agencies for small-area data. 

Se tember 1969, 41 pp. A r~p~r:_~~,.~ 6. Fami'1 Health Survey t P ment data from tne specl a I t:ell:::'u:::, 

sample survey taken 0 aug. on various elements of family of New Haven with informatlon 

health. 

. bl f om the U.S. Census *The pubifcations descr;b~d are obtalna e r 

Bureau in Washington, D.C. 20L33 

176 

-

7. Health Information System. October 1969, 67pp. This report 

documents the development of a maternal and child health care 

information system utilizing census and local data. 

8. Data Uses in Health Planning. June 1970, 48pp. This report 

outlines the uses of data in health planning based on the 

general research conducted at the Census Use Study. 

9. Data Uses in Urban Planning. February 1970, 28pp.' A description 

of the general findings of the Census Use StlJdy as they apply to 

the field of urban planning. 

10. Data Uses in School Administration. April 1970, 30pp. A' 

report 'describing the uses of data in school administration based 

upon activities conducted at the-Census Use Study with local 

school administrators. 

11. Area Travel Survey. September 1970, 43pp. A description of 

a sample survey conducted to augment the New ~aven special census 

data with basic data for use in transportation pl~nning. 

12. Health Information Svstem II. March 1971. 306pp. A suoDlementary report on the development and implementation of a 

computer-based health information system including materials, 

documentation, methodology and analysis. 

14. Geocoding with ADMATCH - A Los Angeles Experience. 

23pp. A report describing the use of ADMATCH with 

local geographic base files such as ACG, DIME, and 

indexes. 

January 1971, 

a variety of 

street tract 

General Publications Series 

These publications document the projects ana activies of the Census Use 

Study in a number of fields of small-area data applications. 

A Geographic Base File for Urban Data Systems. May 1969, 24pp. 

(Reprinted with permission of System Development Corporation). 

A description of the Dual Independent Map Encoding approach 

to the creation of a geographic base file. 

Unified Statistical Evaluation Study - Report No.2. (Preliminary). 

October 1972, 35pp. This report presents graphic illustrations 

of selected socioeconomic characteristics of Marion County, 

Indiana, taken from the Census Fourth Count Summary tape. 

Handbook for Manpower Planners - Part I. September 1973. 

102pp. A publication designed to assist manpower planners in 

the use of data from the 1970 Decennial Census. 

177 
. ' 

Handbook for Manpower Planners.- Part II. Novem~er 1974. 

iihis publication presents publlshed and.non:pub11shed so~rces 

of manpo~er data and suggests data appllcatlons to the fleld 

of human resource planning. 

The First International DIME Colloquium: Conference Proceedings. 

May 1973, 115pp. This report presents the c~nference p,roceed- ings of the First International DIME Co11oqulum, held In 

Washington, D.C., August 27-29, 1972. 

DIME Worksho s: An interim Re ort. May 1973, 126 pp. A 

description of the DIME Dual Independent Map.Encoding) Workshops designed to assist local personnel 1n the use of 

the Census Bureau's GBF/DIME files and related computer 

techniques for small-area oata analysis. 

Da ta Uses in the Pri va te Sector: Proceedi ngs of the ExeclJ..ti ve 

Seminar. March 1973, 120pp. Included in this publication 

on the proceedings of the first executive seminar are reports 

dealing with the application of the DIME and sma11:area data 

technology to such areas as site selection, market1ng, and trade 

area analysis . 

The Uses of GBF/DIME. September 1974, 170pp. A compendium of 

GBF/DIME applications to such fields as edu;at~on, health, 

human resources, public safety and transpor~at10n, as documented by u:::.ers throughout ~he country. 

Data - Information - Decision: The Indianapolis Experience. 

October 1974. This illustrated publication documents the 

use of census data by decisionmakers in Indianapolis. 

Technology Made Easy Series 

This series of publications is designed to ~resent compl~x, technical 

material and concepts in a form that can be readlly understooa by the 

local user. 

1. ADMATCH Adventures. A non-technical, illustrated explanation 

of the ADMATCH computer program, which assigns geographic codes to data records containing address information. 

2. CARPOl: An Overview. This simplified and illustrated version 

o7Pthe CARPOl computer program provides the non-technical 

administrator or carpooler with an introduction to the program ~nd the technology behind it. 

3. pay Care Center. An i11us~rated des~riptio~ ~f a syste~ designed to provide infor:matlOn to ass1st admln1strators 1n 

allocating resources for social services . 

4. DIME Comi and Stories. A cartoon explanation of the usefulness 

of DIME and the basis of Dual Independe~t Map Encoding. 

178 

.. ' .' 

Stud 

. Thi~ series deals with essential concepts and components of a modern 

lnformatlOn system. SCRIS~ originally established as a jOint effort of 

the Bureau of the ~ensus a~d the County of los Angeles, h,as attempted to 

transfer the exper1enc0 ga1ned in New Haven to a larger urban area. 

1. Computer Graphics. A joint study of SCRIS and the los Angeles 

City Plann~ng Department, January 1970, 22pp. I\n example of 

the effe~tlv~ use of a g~ographic base file for referencing data, the appllcat10n of ADMAT~H for organizing data, and the 

use of the computer as a device for displaying information. 

2. ACG/DIM~ U8dating System: A First look. Marchi 1970, 26pp. 

A descr1ptlon of the in1tial thinking on a fundamental work it~m in the SCRIS program, the development of update and 

malntenance procedures for the geographic base file. 

4. ACG/DIME Updating System: An Interim Report. October 1970 

55pp. An amplification of report Nu: 2 describing a propos~d system and plans for updating the los Angeles ACG/DIME file 

including mapping and clerical procedures as well as computer processing of update transactions. 

8. ACG/DIME Updating System: The Long Beach Experience. October 

1971, 68pp. The report contains extensive descriptions of 

procedures covering the mapping, clerical coding, recordkeeping, 

and com~uter pro~essing act~al1y used for a portion of the long 

Beach flle. Estlmates of tlme, space, and cost requirements are also presented. 

10. Southern California Regional Information Study Research Notes: 

1970-1971. December 1971, 88pp. This report presents a compendium of Research Notes describing programs and projects of 

SCRIS .. It contains both descriptions and full operating in- struct10n~ for th~ use of a large number of programs including: (1) Coordlnate Ed1t Program; (2) DIME-ADMATCH Record Conversion 

Program·,DARC; (3) ACG-ADMATCH Record Conversi on Program-AARC; (4) DIME Dictionary Coding Program-DCODE; (5) Coordinate 

Insertion Program-COIN: (6) Shortest Path Program; (7) DIME 

Area Cenb'oid System-DACS; (8) Census Summary Tape Selector 

CENSLCT:. (9) First County Reporter; (10) Columnar Aggregation 

and Tabulation System-CATS; 911) Report on Test of Hand-Digitizing' (12) CRIDS Catalogued Procedure. ' 

179 
Social and Heaith Indicators Sys~em Reports 

The purpose of the Social and Health Indicators System, originally 

sponsored by the Office of Economic Opportunity (OEG, and now supported by the Department of Health, Education and Welfare, Health Resources 

Administration, is to provide a mechanism for' monitoring the health status 

and social and economic well-being of populations receiving (or eligible 

to receive) health and social services in a given area. 

The reports, in general, detail each of the three stages of the 

program: 

Stage I: The initial phase of the program determines whether 

relevant and accurate data is readily available in 

adequate detail, timeliness, and appropriate (small- W 

area) geographic level for the selected sites. ~ ! 

, 

Stage II: Data is reconstructed on an annual basis for the 

1965-1970 period and yields an historical data 

base to set've as the foundation for a social and 

health indicators system. 

Stage III: The final report phase of the program involves 

refinement, supplement and extension of the 

1965-1970 data base and the selection of key social and health indicators. \ 

The following reports have been issued: 

1. Atlanta: Part I, April 1974, 61pp. 

2. Atlanta: Part II, October 1973, 212pp. 

3. Los Angeles, June 1973, 320 pp. 4. Mound Bayou, Mississippi: Part I, June 1972, 65pp. 

5. Mound Bayou, Mississippi: Part II, June 1972, 282pp. 

6. Phoenix: Part I, October 1973, 59pp. 

computer Program Manuals 

1. DOS AD~TCH: An Address Matching System. A computer program 

package designed for use in assigning geographic codes to local 

records using a DIME or similar geographic base file. It includes 

a users' manual and computer programs (written in IBM 860 

Assembly language). 

2. .9_S ADMATCH: An Address Matching System. A new version of the DOS 

programs specifically redeSigned for more rapid processing of 

large files on medium-size and large computers. It includes a 

users' manual and computer programs (written in IBM 360 Assembly 

1 anguage). 

180 

,,~' 
---- < 

Social and Health Indicators System Reports 

hose of the Social and Health Indicators System, originally 

sponS~::dP~;Pthe Office of Economic.Opportunity (OEO~e:~~hn~:S~~~~~;ted 

~~m~~~s~~~~~:~n~so~oH~~~~~deE~~~:~~~~i~~df~~~~~~~tori~g.t~e(~~a!ngm;US ar.d social and economic well-belng of p?pulat~ons recelVln ~ 

to receive) health and social services 1n a glven area. 

The reports, in genera, 1 deta1'1 each of tile three stages of the 

program: 

Stage I: 

Stage II: 

Stage I II: 

The initial phase of the program d~ter'min~s whet~er 

relevant and accurate data is readl1y aV~11able 1n 

adequate detail, timeliness, and appropr1~te (smallarea) geographic level for the selected sltes. 

Data is reconstructed on an annu~l ba~is for the 

1965-1970 period and yields an h1stOrlcal.data 

base to serve as the foundation for a soclal and 

health indicators system. 

The final report phase of the pro~ram involves 

refinement, supplement and extenSl?n of the 

1965-1970 data base and the selectlon of \ey social and health indicators. 

The following reports have been issued: 

1. 

2. 

1. 

2. 

3. 

4. 

5. 

6. 

Atlanta: Part I, April 1974, 61pp; 

Atlanta: Part II, October 1973, 212pp. 

los Angeles, June 1973, 320 pp. 

Mound Bayou, Mississippi: Part I, June 1972, 65pp. 

Mound Bayo~, MiSSiSSippi: Part II, June 1972, 282pp. 

Phoenix: Part I, October 1973, 59pp. 

Computer Program Manuals 

DOS ADMft.TCH: An Address Matchi ng System. A c~mputer prog~am 1 

package designed for use in assigning ge~9r~Ph1cf~~des i~ i~~~udes records using a DIME or similar geograph1~ base. '\~M 360 

a users· manual and computer programs (wrltten 1r. 

Assembly language). 

OS ADMATCH: An Address Matching System. A n~w version.of the DOS 

programs specifically redesigned for more rap1d proc~ss~na of 

1 f'l on medium-size and large co~puter~. It 1nc u es a u:~;~· ~a~~a 1 and computer programs (wr'l tten , n IBM 360 Assembly 1 anguage). 

180 

3. GRIDS: A ~9mputer Mapping System. A Computer program package for 

use on both smal1- and large-scale computers. Within a grid 

pattern structure it produces density, shading and value maps. 

It includes a users· manual and computer programs (written in' 

4.0I~E: 

Ba;sic Fortran 

A Geographic 

Iv) '; 

Base File System. A computer program package 

for ~reating a DIME Geographic base file. It includes cl~rical inst~uctions, a computer manual and programs (written in Basic Fortran IV). 

5. CARPOl. A~omputer program package for large-scale carpooling 

using DIME technology. It is designed to present applicants with 

lists of potenti'al carpoolers who live nearby and work 'similar 

shifts. Its documentation is accompanied by administrative sugges- tions for executive~"and technical data for staff members not 

familiar with computer technology. It includes a users· manual and 

computer program (written iiJ:;Basic Fortran IV). 

6. UNIMATCH 1: A R~9.rd Linkage System. A com'puter program prackage for matching data files. It has the capability of handling 

building names, street intersections and non-address matching. It 

includes a usersl manual and computer program (written in IBr~ Assembly language) . 

GLOSSARY 

BLOCKS: Blocks are usually well-defined rectangular pieces of land, 

bounded by streets or roads. However, they may be irregular in 

shape or part1al1y bounded by railroad tracks, streams, or other 

features. Blacks may not cross other boundaries such as city 

limits. Statistics for blocks are tabulated for all cities of 

50,000 or more and for the urbanized areas of these cHies. In 

addition, the Bureau also collected and tabulated data for 

blocks on a contractual basis in over 900 areas outside urbanized 

areas. A list of these contract block areas is available upon request from the Census Bureau. 

BLOCK GROUPS: A Block group is a combination of contiguous city blocks 

and generally has a population of about 1,000. They are subdivisions of census tracts and are defined without regard to 

the boundaries of political or administrative areas, such as 

cities or minor civil divisions. When a block group straddles 

one or more of these boundaries, data f~r those parts in 

different areas are tabulated separately. For the purpose of 

providing small-area census data, block groups are the equivalent of enumeration districts in the built-up portions of the 145 largest SMSA·.::;. 

181 
CENSUS COUNTY PIVISIONS (CC.Q2l.: These are statistical subdivisions of counties in 21 States where minor civil divisions 

were not suitable for presenting census data. In these States 

the MDC's are either too small, have lost nearly all meaning 

locally" or have frequent boundary changes. Over 7,000 CCD's have 

been established a,S relatively permanent statistical areas 

by the Bureau of the Census in cooperation with State and 

local groups. Tha States containing CCO's are: Alabama, 

Arizona, California, Colorado, Delaware, Florida, Georgia, 

Hawaii. Idaho, Kentucky, Montana, New Mexico, Noy'th Dakota, 

Oklahoma" Oregon, South Carolina, Tennessee, Texas, Utah, 

Washington and Wyoming. 

CENSUS TRACTS: Tracts are statistical subdivisions of SMSA's and a 

limited number of areas outside of SMSA's. Tract boundaries 

are determined by a local committee and approved by the Bureau 

of the Census; they conform to county 1 ines .• ' Tracts are generally 

designed to be r~latively homogeneous with respect to population characteristi~s, economic status and living conditions. 

The average tract has about 4,000 to 5,OOQ r~sidents. For the 

1970 census all SMSA's that were recogniz:ed at the time of 

the census were completely tracted. Over.100 counties, cities, 

or parts of counties- outs i de SMSA' s are a,l so tracted. ~ c" 

CENTRAL BUSINESS DlSTRICT'{CBD); Tili s Census Bureau, term refers to an 

-, area of very high land valuation; charactehzed by a high concentration of retail businesses, offices, theaters. hotels~ and 

service businesses and high traffic flow. CSO boundaries are 

def'ined in terms of existing census tract Jines. CBDls cure 

1ocate'd in cities with 100,000 or more poi?\ilation. 

COMPLETE COliNT OR lOG-PERCENT DATA; This census terminology refel"s to 

items "of information collected for everyj,ndividual and housing 

un; t at the time 'of the census. ',: " 

COUNTIES: Counties are t~e primary political andadministrative 

divisions Df the~States. The only major:exceptions are in 

Louisiana where the divisions are called: parishes, and in 

Alaska wln.ere, 20 census divisions have beenc)'eated as county 

equivalents. There are a number of cities ~'ihich are independent of any county organization and,:,: because they constitute primary dhdsions within their states, are accorded the 

same treatment as the county units in the preparati on of . census tabulatioris. NOTE: The District otColumbia and the 

independent cities within the States of Mai~yland, Missouf'i, 

Nevada and Virgi,nia are all identified as cbunty equivalents. 

COUNTS: First-SixtlnJ.: The terms used by the Census Bureau to identify 

sets. of 1970 Census data summari es on <computer tape for certa ill 

idnds of geographi c areas. Numberi ng refers to the order in 

which the data sets were released. 

182 

DUAL INDEPENDENT ~J~P ENCODING (DIME): T " \' rep~esentlng,map features nume~~c~~F/DIME flTe lS,a system fa.l:'\ 

It 1 S es~ent1 ally a fil e of .l.,,JY for process 1 ng by computer. 

segment 1 s a portfO'n of tsegmen (.,',~:ecords where a typi ca 1 ,,' , , '. a s reet def;Jned b 't " :,' or C1Vllboundaries,' Other se n .: Y In ~l'sectlng streets 

fea tures, ra il road tracks . $ le~to/ ,may be def~ ned for na tura.'1 

The basic feature of a DIMEJ~~~~d~ctl0n boundanes a~d the lfk'e. ~nd block (area bounded by s ' ls)t.~at e~ch node (lntersecfion) , Dua 1 Indepelldentl! refer~s to ei~:n~!c ? s un 1 que ly i denti fi ed. 

111 the network is described 01 ~"t~hat each boundary segment an~ the b'l ocks to the ri ht a 'Y s~ec 1 fYln$ the nodes at the ends 

unlquely numbered, the c~m ut~~ left:Wl~h each node a~d block 

nehlOrks and match them t P, , can then,construct two In<;/ependent 

represented and an land ~c~~~ured t~e eXlstin~ ne~work is completely ~dded to the DIME file b a ,tL~ or. Spa~lal lnformation is 

lntersection node. ~ 551gn1ng geographlc coordinates to each 

ENUMERATION DISTR leTS (ED's)' T'~ - _ - B _. ne.:.€ ar'" sma11 area d ,., w'eau, which have an aver', .... l' S enned by the Census 

newly defined with pach ' age POpu,atlon of about 800. ED's are 

for the, contra 1 of ,;ensu~e~~~~a~~~n~se as adm'~ ~i ~trative units tabu1a~1Ons are prepared for ED') . (In adoltlOn, some census 

of a c 1 ty, township or oth s . t They neVE~r cross the bounda)'Y' 

\.1 h' h ,er area \ excep""cit bl k) I 1 C census data dl"e tabul tlted. l... Y DC S for 

GEOGRAPHIC BASE FILEjGBF): A descri ti- ':", . ' 

an area (stt'eets, intersectigns,?P ~~ the $:ographlC attributes of 

computer-readable for;m. An add·~ ~,-ogra~l'n\... c?des, addresses) in 

are examples. le~s codlng gUlde and a DIME file 

MI~OR CIVIL DIVISIONS (MeD IS)' Th ' 

: administrative SUbdi~isio~~e ~re th~'prlmary political and , of MCD is the to~nship but ~h cOUntles~ The most common type 

magisterial districts, 'gores, :~~.at'e also towns, preCincts, 

PLACES: ~here a re two types of places r '" ln~orp?rated and unincorporaiedeco~nlzea In the census tabulations-_ 

Unlts lncorporated as cities t· nCO~ptOrated places are political 

the fol1owi'<ll'~ exceptions. T~wnoW~S'/lnageS, or boroUghs, with 

Wisconsin and the boroughs in Afa~~a ew England, Ne~ York~ and 

rather they are considered to be MCD Jare ~ot r~cognlzed as places; places are subdiVisions of h " s:.' iOS~ ~ncorporated country divisions) Some it e millor C1Vl1 d1v1sions (or census 

MeD,'s or cross MCD'and coun~ycolr~orated places, however, constitute , lnes. , 

Un,ihcorporated places are densel1 ' , wlthout legally defined corpor t~ ~~t~ ed popu1atlon centers 

residential nucleus, and bound a ~ lm~ts. Each has a definite 

Census to include, insofar as pa~~~l~bal t e dlr~wtnhbY tille, Burea,u of the 

area. e, a u e densely settled 

183 
REGIONS AND DIVISIONS: Census regions and divisions are l~fge geographic 

areas which have been used for many decades for the purpose of 

providing summary figures at intermediate levels between those 

for the United States and those for individual States. The 

nine divisions d~e groupings of contiguous States, except for 

Alaska and Hawaii. The four regions are composed "bf groups of 

divisions. ' 

ST~J:4DARD INDUSTRIAL CL,~SSIFICATION (SIC) CODE: Classified establ ishments 

by the type o;-industrial activity in which they ar~ engaged. The 

categories arE' set up to provide a simple and \<lol,~kable arrangement of sepa'.'a.ting and class'ifying gv'olJPs for statistical analysis. 

Comparability to census classifications is discussed in 1970 

Census of Popu'1ation, Classified Index of Industries and 

Occupations. -

STANDARD METROPOLITAN STATISTICAL AREAS (SMSA's): As reported in the 1970 

Census, Standard Metropolitan Statistical Areas consist of a 

county or group of contiguous counties (except in the New 

England States) containing at least one city of 50,000 inhabitants 

or more, or "twin cities" with a combined population of at least 

50,000. In addi'~ionto the county(s), containing the central 

city(s), contiguous counties are included in an SMSA if they 

are metrcpolitan in character an~ are socially and economically 

integrated with the central city. In the New England States, 

SMSA!s consist of groups of towns and cities inste~d of counties 

because these geog~aphic areas are administratively more important 

than counties. The title of an SMSA identifies the central city 

or cities. The SMSA's are clefined by the Office of Management 

and Budget. The 1970 census data were tabulated for a total of 

247 SMSA's. NOTE: An additional 21 SMSA's were created in 1971 

and '1972 as a result of post rensal revision of criteria; the 

total number of SMSA's is presently 268. 

SUMMARY TAPES: This term refers to the 1970 Census computer tapes which 

contain more data than are available in the published reports. 

However, no information specified to an individual person or 

housing unit is mad0 available. The data about individuals 

and hOllsing units are aggregated by areas; e.g .• block groups or 

enumeration districts, census tracts, etc. 

SUPPRESSION: The Census Bureau term which refers to the insertion of 

a code to replace an actual value to prevent disclosure of 

information concerning an individual or firm. . 

URBANIZED AREAS: An urbaniZ€!d area consists of a central.city (or "twin 

cities") of 50,000 inhabitants or more plus the,surrounding 

densely settled incorporated and unincorporated areas which meet 

specific criteria of population size or density. Urbanized 

areas differ f~'om SMSA lsi n that rtlra 1 port; ons of the coun ti es com~osing an SMSA arr excluded, as are those places which ar~ 

urban h~ nature but separated by rural territory from the densely 

popul ated area sunounding the central city. The urbanized al'ea. 

populat'rop is sometimes divided into those in the central city 

and thOSi in the remainder of the area or the urban fringe. 

184 

~~------------------ APPENDIX ----.--,--~---. 

List of SMSA's With GBF/DIME Files 

Alabama 

Birmingham 

Birmingham Regional Planning Commission 

Florence 

Muscle Shoals Council of 

Local Governments 

Gadsden 

City Planning Department 

Huntsvi11 e 

Huntsville City Planning Commission 

Mobile 

South Alabama Regional 

Planning Commission 

Montgomery 

Bureau of Urban Planning 

Tuscaloosa 

Planning Department 

Alaska 

And~dY'age 

Greater Anchorage Area Borough Planning Department 

Arizona 

Phoenix 

Maricopa County Planning and 

Zoning Commission-Department 

Tucson 

Tuc~on Area Transportation 

Planning Agency 

1.85 

Arkansas 

Fayetteville-Springdale 

Fort Smith, Ark.-Okla. 

Arkhoma Regional Planning Commission 

Little Rock-North Little Rock 

Planning Department 

Pine Bluff 

Southeast Arkansas Regional Planning Commission 

Caiifornia 

Anaheim-Santa Ana-Garden Grove 

Orange County For'ecast and 

Ana lys is Centel' 

Bakersfield 

Kern County Planning Department 

Fresno 

Fresno County Planning Department 

Los Angeles-Long Beach 

los Angeles County Regional Planning Commission 

~~odesto 

Stanislaus County Planning Department 

Oxnard-Simi Valley-Ventura 

Ventura County Planning Department 

Riverside County 

Riverside County Planning Department 
San Bernardino County San Bernardino Planning 

Department 

Sacramento 

Sacramento Regional Area 

Pl anni ng Commi s5:19n 

Salinas-Seaside-Monterey 

County of Monterey Planning 

Department 

San Diego Comprehensive Planning 

Organiza ti on 

San Francisco-Oakland 

Association of Bay Area 

Governments 

San Jose 

Santa Clara County Center 

Santa Barbara County - Cities 

Area Planning Commission 

Santa Cruz 

Santa Rosa 

Sonoma County Plann'jng Department 

Stockton 

San Joaquin County Planning 

Department 

Valejo-Fairfield-Napa 

(See San Francisco) 

Colorado 

Colorado Springs Pikes Peak Area Council of 

Govel"nments 

Denver-Boulder 

Denver Regional Council of 

Governments 

Pueblo 

Pueblo Regional Planning Commission 

Connecticut 

Bridgeport 

The Greater Bridgeport Regional 

Planning Agency 

Bristol 

(See New Britain) 

Danbury 

Housatonic Valley Council of 

Elected Officials 

186 

, 

Hartford 

Meriden 

City Planning Commission 

New Britain . Central Conn~cticut Regional 

Planning Agency 

New Haven-West Haven 

Regional Planning Agency of 

South Central Connecticut 

New London-Nor~ich, Conn. - R.I. 

Southeastern Cdnnecticut Regional 

Planning Agency 

Norwalk 

Southwestern Regional Planning Agency 

Stamford 

Planning Board 

Waterbury 

Centr~l Naugatuck Valley 

Reglonal Planning Agency 

Delaware 

W~lmington, Del.-N.J.-Md. Wllming~on Metropolitan Area 

Plannlng Coordinating Council 

District of Columbia 

Washingt?n, D.C.-Md.-Va. 

Metropolltan Washington Council 

of Governments 

FloriE,9.. 

Daytona Beach 

Volusia Council of Governments 

Fort lauderdale-Hollywood 

Browatd County Area Planning Board 

Fort Myers 

Gainesville ~ 

North Central Florida R~gional 

Planning Council 

Jacksonvi 11 e 

Jacksonville Area Planning Board 

Lakeland-Winter Haven 

Imperial Polk County Board of 

Commissioners 

Melbourne-Titusville-Cocoa 

Miami 

Metropol itan Dade Courity 

Community Improvement Program 

187 

Orlando 

East Central Florida Regional P'lanning Council 

Pensacola 

Escambi~-Santa Rosa Regional Planning Council 

Sarasota 

Ta 11 ahasse'e 

Tallahassee-Leon County Planning Department 

Tampa-St. P~tersburg I 

Tampa Bay Regional Planning Council 

West Palm Beach .. Boca Raton .. Area Planning Board of Pal~Beach County 

Georgia : ~ " 

Albany 

Albany ~lanning Commission 

Atl anta 

Atlanta Regi6'nal Commissiol'i 

Augusta, Ga.-S.C. 

Augusta-Richmond County Planning Commission 

Columbus, Ga.-Ala. 

Department of Community Development 

Maco 

Middle Georgia Arell Planning Commission 

Savannah 

Chatham Courity-Savannah 

Metropolitan Pla./'H1ing Commission 
;1 .' 

Hawaii 

Honolulu 

State Department of Transportation 

Idaho 

Boi se City Ada Council of Governments 

Illinois 

Bloomington-Normal 

McLean County Regional Planning Commission 

Champa i gn-Urbana-Rantouf Champaign County Regional 

Planning Commission 

Chicago 

Northeastern Illinois 

Planning Commission 

Decatur 

Department of Community 

Development 

Peoria 

Ti'l-County Regional Planning 

Commission 

Rockford 

City-County ?lanning Commission 

Rockford-Winnebago County 

Springfield 

Spi'ingfiel d-Sanagal110n Courty Regiona1 Planning C:ommission 

Indiana 

Anderson 

City Planning Department 

Evansv'i11e, Ind.-Ky. Southwestern Indiana & Kentucky 

Fort Wayne Three Ri vers Coordi nati ng COl~~lCil 

Gary-Hamrnond-East Chicago 

(See Chicago) 

Indianapolis 

Census Use Study 

Lafayette-West L~fayette. 

Tippecanoe Area Plan Commission 

Muncie 

Delaware-Muncie Metropolitan 

Planning Commission 

South Bend Ar~aPlan Commi$sion of St. Joseph 

County 

Terre Haute 

Area Planning Department for 

Vigo County 

Iowa 

Cedar Rapids Linn County Regional Planning 

Agency 

Davenport-Rock Is1at1d-t'~ol i ne~ 

Iowa-In. 

Bi-State Metropolitan Planning 

Commission 

Des Moines 

Central Iowa Regional Association 

of Local Governments 

Dubuque 

Dubuque County Metropolitan Area 

Planning Commission 

Sioux City, Iowa-Neb. 

Siouxland Interstate Metropolitan 

Planning Council 

Regional Council of Governments 

188 

Waterloo-Cedar Falls 

Iowa Northland Regional Council 

of Governments 

Kansas 

Topeka 

Topeka-Shawnee Metropolitan Planning Commission 

Wi chita 

vii chi ta-Sedgwi ck County 

Metropolitan Area Planning Department 

Kentucky 

lexington 

CitY-County Planning Commission 

Louisville, Ky.-Ind. 

falls of the Ohio Metropolitan Council of Governments 

Owensboro, Ky. 

Owensboro Metropolitan Planning Commission 

Louisiana 

A1exandria 

Rapides Area Planning Commission 

Baton Rouge 

Capital Region Planning Commission 

Lafayette 

Lafayette Planning Commission 

Lake Charles 

Calcasiue Regional Planning Commission 

Monroe 

Ouachita Council of Governments 

189 

New' Orl.eans 

Regional Planning Commission 

Shreveport 

Caddo-Bossier Council of 

Lecal Gov~rnments 

Maine 

Lewi ston-Auburn; 

Androscoggin Valley Regional Plc1nning Commission 

Portland 

Greater Portland Council of 

Governments 

Maryland 

Ba ltimore 

Regional Planning Councl) 

Massachusetts 

80S ton 

Brockton 

Fall River, Mass.-R.I. 

Fall River Planning Board 

Fitchburg-Leominster 

Planning Board, City Hall 

Lawrence-Haverhill Mass.-N.H. 

Division of Planning & Development 

Lowell, Mass.-N.H. 

Lowell City Development Authority 

New Bedford 

City Planning Department 

Pittsfield. 

Pittsfield City Planning Board 
Springfield-Chicopee-Holyoke, 

Mass.-Conn. t 

Springfield Planning Departmen 

Worcester t ~Jorcester City Pl ann'j rig Departmen 

Michigan 

Ann Arbor 

(See Detroit) 

Battle Creek 

Bay City . Bay Regional Planning Commissl0n 

Detroit 

Southeast Michigan Council of 

Governments 

Flint . Genesee County r~et~opOll tan 

Planning CommlSSlon 

Grand Rapids Kent Ottawa Regional Planning 

Commission 

Jackson 

JacKson Metropolitan A)'c~ , ~~gional Planning CommlSSlon 

Kalamazoo-Portage . Kalamazoo Metro~ol~tan County Planning CommlSSlon 

Lansing-East Lansing , Tri-County Regional Plannlng 

Commission 

Muskegon-Muskegon He~ghts 

t4est ~~ichiga'l Shorellne, ._ Regional Plam1ing CommlSS101l 

Saginaw 

Office of the County Planner 

~li nnesota ------

Duluth-Superior, Minn.-Wis. 

Head of the Lakes Council of 

Governments 

Minneapolis-St. Paul, Minne.:W~s. 

Metropolitan Council of the IWln 

Ci ti es l~r'ea 

Rochester h t 

Planning Dir'ector~ City of' Hoc E:.~s ';f:~!,' 

St. Cloud '. 

St. Cloud Metro~ol~tan Area 

Planning CommlSSlon 

Mississ:!.B£i 

---

Biloxi-Gulfport . Gulf Regional ~'annlng Commiss',ion 

Jackson 

Jackson City Planning Board 

~1issour-i ---'--

Columbia . County Administratlon Bu'Ulding 

Kansas City, Mo.-Kan. 

Mid-;\merican Regional Council 

St, Joseph . '0" 

Metropolitan Planning CommlSSl , 

St. Louis, Mo.-Ill. . '_ East-\~est Gateway Coordlnatlrlg 

Council 

Springfie1d < 

Zoning and Plannlng Comm1ss'i<.m 

Montana 

Billings . Billings-Yellowstone, City-County 

Planning Board 

" \ 

I' 

'\1 

.~ 

Great Falls 

Great Fall S City-County Planni'ng Board 

Nebrask':l 

-------

L-i nco 'In 

L inca'ln C'ity"Lancaster' 

County Pl ann; ng DE!partment 

Omaha:1 Neb, - I o lila 

Omaha-Council Bluffs Metropol- itan Area Planning Agency 

Nevada 

----

las Ih=gas 

Clark County Regional P1anning Council 

F(eno 

The Regional Planning COlTImission 

of Reno~ Sparks i~ Washoe County 

r'lanches ter 

Southern New HAmpshire Planning Commission 

Nashua 

Nashua Regional Planning Comm-l ss i on 

,New_~_et~ 

PI t'! anti c City 

Atlantic County Planning Department 

\Jersey Ci ty 

Long Btanch··Asblill"y Park 

Nonmouth COllnty Planning Board 

'~ewark 

Morris County 

Morris County Planning Boa~'d 

New Brunswick-Perth Amboy-Sayreville ~rjiddlesex County Planning Board 

Paterson-C1ifton-Passaic 

(See New York~ N.Y.) 

Trenton 

(See Philadelphia. Pa.) 

Vineland-Millville-Bridgeton 

Cv.mber7and County Planning Board 

New Me){ ',I co 

Albuquerque 

Middle Rio Grande Council of 

Governments of New Mexico 

New York 

A'l bany,.Schenectady_ Troy 

Capital District Regional Planning Commission 

Binghamton s NY-Pa. 

Southern Tier East Regional Plann'jng Board 

Buffalo 

Erie and Niagara Counties Regional Plann'ing Board 

Elmita 

Chemung COI.nn:y Plannifl9 Boa.rd 

N3.ssau-Suffolk 

Nassau-Suffolk Reg10ndl Planning Bo~rd and Nassau-Suffolk Regional Marine Resources Council 

New York-NY-N.1J. 

Tri-State Regional Planning COImni s S i on 
Poughkeepsie 

Dutchess County Planning 

Boal'd 

Rochester 

Genesee Finger Lakes 

Regional Planning Board 

SYk'acuse 

Syracuse-Onondaga Plannirg Age~ilcy 

Utica-Rome 

Herkimer-~neida Counties Cc~prehensive Planning Program 

North La: r:G 'Li.!JA. 

Ashev i 1:1 e 

I'ietrc,po /I l' l' tan Pl:>mi)i~g L< Board 

Bur 1 i ngto(f'l 

Charlotte-Gastonia 

Charlotte-Mecklenturg Planning 

Comm; SSid)n 

Fayettev'j 1\ 1 e 

Cumberland County JDint 

Planning Boal~d 

Greensboro-~igh foint. 

GuiHor'd :CDunty Flall]n1ng 

Depattment 

Winston-Salem 

Ci ty-Cmmty ?'l!anni ng Bnar'd 

Ra 1 eicih-Durham Trian~le J Council of 

Governments 

Wilmington-New ~anover 

Planning Depart~ient 

192 

North Dakota 

Fargo-Moorhead~Minn. . . Moorhead Planning CommlSS10n 

Ohio 

Akron 

(See Cleveland) 

Canton 

Stark County Area Transportation 

Study 

Cincinnati, Ohio-Ky,-Ind .. Ohio-Kentu~ky-Indlana Reglonal Planning Authority 

Cleveland ; 

Northeast Ohio Areawlde Coordinnating Agency 

Columbus 

Mid-Ohio Regional Planning 

Commission 

Dayton 

Miami Valley Regional Planning 

Commission 

Hamilton-Middletown 

(See Cincinnati) 

Lima . Lima-Allen County Reglonal 

Planning Commission 

Lorain-Elyria 

(See Cleveland) 

Mansfield 

Richland County Regional 

Planning Commission 

Springfield 

Clark County-Springfield Regional 

Planning Commission 

Steubenville-Weirton, Ohio-W.Va. Brooke-Hancock-Jeff~rson 

Metropolitan P"lan,llng Commission 

Toledo, Ohio-Mich. 

ToledO-Lucas County Planning Commissions 

Warren 

Trumbull County Planning Commission 

Youngstown 

County of ~4ahoning Planning Commission 

Oklahoma 

Lawton 

Lawton Metropolitan Area 

Planning Commission 

Oklahoma City 

Association of Central Oklahoma 

Governments 

Tulsa 

Tulsa Metropolitan Area 

Planning Commission 

Oreyon 

Eugene-Springfield 

Lane Council of Governments 

Portland, Orea.-Wash. 

Columbia R~gion Associaton 

of Governments 

Salem 

r·1i d-Wi 11 amette Va 11 ey 

Pennsylvania 

Allentown-Bethlehem-Easton, Pa-NY 

Joint Planning Commission 

Lehigh-Northampton Counties 

193 

Altoona 

Blair County Planning Commission 

Erie 

Erie County Metropolitan Planning Commission 

Harrisburg 

Tri-County Regional Planning Commission 

Johnstown 

Cambria County Planning Commission 

Lancaster 

Lancaster County Pl ann; n9 

Commission 

Scranton 

Lackawanna Coun't,y Regional, Planning Commission 

Wilkes-Barre-Hazleton 

Luzerne County Planning Cmrrniss'!(nl 

Philadelphia~ Pa.-N.J. 

Delawa.re ~faney Regional P'Ia.nning Commission 

Pittsburgh 

Southwestern Pennsylvania RAgional Pl ann; ng Comrni ss i on 

Reading 

Berks County Planning Commission 

Wi 11, i ams port 

lycoming County Planning Commission 

York 

York County Planning Conlnrission 

Puerto Rico 

Caguas 

(See San Juan) 

-
Mayaguez 

(See San Juan) 

Ponce 

(See San Juan) 

San <Juan 

Puerto Rico Planning Board 

Rhode Island 

Providence·Warwick-Pawtucket, 

R. 1. -[11ass. 

Statewide Planning Program 

South Carolina 

Charleston 

B€rkeley-Charleston-Dorchest~r 

Regional Planning Council 

Columbia 

Central Midlands Regional 

Planning Council 

Greenville-Spartanburg 

Greenville County Health Dept. 

South Oilkvta 

Sioux Falls 

City Planning Department 

Tennessee 

Chattanooga, Tenn.-Ga. 

Chattanooga-Hamilton County Regional Planning Commission 

Kingsport-Bristol, Tenn.-Va. 

Knoxville 

Knoxville/Knox County 

Metropolitan Planning Commission 

194 

Memphis, Tenn.-Ark.-Miss. 

Memphis and Shelby County Planning 

Commission 

Nashville-Davidson 

Metropolitan Planning Commission 

Texas 

Abilene 

West Central Texas Council of 

Governments 

Amarillo 

Panhandle Reg~onal Planning 

Commission 

Austin 

Austin City Planning Department 

Beaumont-Port Arthur-Orange South East Texas Regional Planning 

Commission 

Brownsville-Harlingen-San Benito 

Planning and Zoning Commission 

Bryan-College Station 

Director of City Planning 

Corpus Christi 

Coastal Bend Council of Governments 

Da11as-Fort Worth 

North Central Texas Council of 

Governments 

El Paso 

West Texas Council of Governments 

Galveston-Texas City 

(See Houston) 

Houston 

Houston-Galveston Area Council 

Killeen-Temple 

Laredo 

City Planning and Zoning Commission 

Lubbock 

Planning and Zoning Board 

McAllen-Pharr-Edinburg 

Planning and Zoning Commission 

Midland 

Plan~lng Department 

Odessa 

Odessa Planning Department 

San Angelo 

Concho Valley Council of 

Governemnts 

San Antonio 

Alamo Area Council of 

Governments 

Sherman-Denison 

Texoma Regional Planning Commission 

Texarkana, Texas-Texarkana, Ark. 

Ark-Tex Council of Governments 

Tyler 

City Planning Department 

Waco 

Waco City Planning Department 

Wi chit2: fa 11 s 

Nortex Regional Planning Commission 

El Paso 

Department of Planning and 

Research 

195 

Utah 

Provo-Orem 

Utah County Council of Governments 

Ogden 

Weber Area Council of Governments 

Sa 1t La ke City Salt Lake County 

Virginia 

Lynchburg 

Central Virginia District Planning Commission 

Newport News-Ham~ton 

Peninsula Planning District 

Commission 

Norfolk-Virginia Beach-Portsmouth 

Va.-N.C. Southeaster~ Virginia Planning District Commission 

Petersburg-Co?onial Heights-Hopewell 

Crater Planning District Commission 

Richmond 

Richmond Regional Planning Commission 

Roanoke 

Fifth Planning District Commission 

Washington 

Richland-Kennewick 

Benton-Franklin Governmental 

Cunference 

Seattle-Everett 

Puget Sound Governmental Conferonce 

Spokane 

Spokane Regional Planning Conference City Planning Department 
i 

i , 

Tacoma 

(See Seattle) 

Yakima 

Yakima County Planning 

Commission 

West Virginia 

Charleston 

Kanawha County Planning 

Commission 

Huntington-Ashland, W.Va.-Ky,-

Ohio 

KYOVA Interstate Planning 

ComnJission 

Parkersburg-Marietta, W.Va.-

Ohio 

Wood County Planning Commission 

Wheeling, W.Va.-Ohio 

Bel-O-Mar Interstate Planning 

Commi S s, i on 

Weirtolfl 

Brooke-Hancock-Jefferson Metropolitan Planning Conmission 

Wisconsin 

Appleton-Oshko~h . East Central Wlsconsln Regional 

Planning Commission 

Green Bay Green Bay-Brown County Planning 

Commission 

Kenosha 

(See Mil waukee) 

La Crosse 

Mississippi River Regional 

Planning COIli;mission 

Madison 

Da~e County Regional Planning 

Commission 

Milwaukee 

Southeastern Wisconsin Regional 

Planning Commission 

Racine 

(See Milwaukee) 

196 

CHAPTER 15 

SMALL AR.EA POPULATION ESTH1ATION 

Leo Schuerman 

This discussion on smal1 area populatiol'1>.'estimation 

techn; ques . was adapted from a chapter cf,~orrmunity 

Health Indlcator System: A Model fcr Pro. :ram Plannin 

and Evaluation - los An e]es Metro olit~)q;'Area to be 

published. This report Was written by Leo Schuerman 

of the University cf Southern California while a staff 

member of the Census Use Study, Bureau of the Census. 

INTRODUCTION 

This Chapter was written for a person with a prior ~ackgrcund in 

advanced statistical estimation techniques. For those wh~ have not had 

su~h a b~ckgrDund, the sec~ion will still be useful in providing an 

onentatlcn to these techmques and tools. In most instances ccnsulting assistance may be required tc implement them. 

The population estimation techniques discuss'ed in th'i;ssection were 

developed specifically to enable the estimation of denominators for rates. 

The denominator in a rate is the "population-at-risk." For eXnmplE'" if 

the event of interest is the occurrence of arrests of male juveniles ages 

1 0-17 t the numerator waul d be the reportednunlbe~'·'of mal e juvenil e arrests 

and the denominator would be the total number of males,ages 10-17. As 

such, the denominator represents the upper limit of thw phenomenon. 

A number 'If sources of information were investigated for possible 

use as denomin2,tor values. These included Federal and local censuses and 

special surveys. None was discovered that could serve as a source for 

denominator values. The national census of population and housing occurs 

only at ten year intervals .. Therefore, using decennial census information 

for denominators becomes increasingly less reliable during the decade between censuses. Special censuses conducted by either states or the Census 

Bureau occur only infrequently and at a considerable expense. Furthermore, 

spedal censuses rarely tover large areas of metropol itan regions. They· 

ili.re ustllally collected for a city when that city feels that its official 

population figure is lower than the true value and hence an inequity exists 

in the allocation of funds. Since the primary focus of special censuses 

is the updating of total population counts, such censuses usually do not 

contain the types of information needed for more specific subpopulations 

of interest. Therefore, a need eXists to deve10p methods of arriving at 

yearly estimates for various population components. 

ESTIMATION REQUIREMENTS 

Given the need for denominators~'and the inability of existing data 

sources to fulfill the requirements for this information, various types 

of estimation procedures Were investigated. Thjs process was guided by 

the general requisites that~ny estimation procedure should comply with 

the following parameters. . 

197 
1. The estimation methodology should consist of a set of generalized 

procedural steps. 

2. The procedure shl)uld utilize existing a.dministrative data sets as 

input. 

3. The procedure should not be "datd bound," that is, tied to specific 

types or formats of administrative data. 

4. The procedure should yield estimates that ~r~ rich in population 

composition detail. (e.g., age., sex, and ethmclty). 

5. The procedure should have the flexibility to estimate areal units 

sma. n sr than county Ol~ ci ty tota 1 s. 

6. Estimates should be obtainable on at least a yearly basis. 

7. The benchmark year information should be geJthered from ~ocally 

derived files or from obtainable Federal Decennial Census f~]es. 

Among demographers and urban data.specia!ist~~ there is a.long t~a~i- tion of interest in methods of populatlon estlmatlon. From thiS tradltion ~omes the notion that to be truly useful, an estimation procedure s~.)ou~d 

be chosen on the co-bas is of the needs for. the estimates ~nd the eXl stHlg data environment of the area for which est1mates are requlred. In g~neral, 

the needs are that estimates be annual, timely, of fine ~re~ resolutlon. 

and rich in population composition detail. Further, estlma~es are speclfical1y required at the level of cens~s.tract~ rather th~n some larger areal 

units.,; This requires the use of admlnlstratlVe data WhlCh can be aggregate~~.&b thi s 1 evel . 

';'t"')~Sing the available administrative data files for Lo~ ~ngeles County, 

seven administrative files were discovered that cou~d effl~len~lY be pro- cessed and employed as input to a system of popul~tlon e~tlmat10n. These 

files were already being collected and pr?cessed 1n machlne readable.form 

by va ri ous agenc i es in the county. A ~ 1 fl1 es ~ere geocodab 1 e, that 1 S, . 

it was possible to locate each event ln spac~ 1n terms of a census tractlng 

system. These files pertained to the followlng events: 

1- Births 

2. Deaths 

3. Fetal Deaths 

4. Reportable Disease 

5. Mental Health Clinic Records 

6. Adult Probation Cases 

7. Juvenile Probation Cases 

198 

. Given t~e specific requirements for estimates and the eXisting d"ta 

env:ronment 1n los Angeles County, a method of poupulation estimation was 

declded upon that ~ombined aspects of two standard estimating methodologies. These were the Ratlo Correlation Method (also called the Multiple Regression method) and the Bogue-Duncan Composite method. Both methods employ data 

that is indirect in nature. Indirect data is information that is designed 

to measure something other than the population parameters fo~ which estimates are needed. Indirect data serve as surrogates for the population 

parameters of interest. The distinction between direct and indirect data 

arises from the use of the files. The same data set may serve both functions for different applications. For example, the Juvenile Probation file 

may be considered direct information lIihen use.d to investigate the characteristics of juvenile probationers and indirect when used to estimate the 

number of juveniles in the population ages 10 to 17. All data sources for 

the estimation procedure chosen here,. may be. considered indirect 'e;.:cept 

for the use of the 1970 Federal CenslJs of Population and Housing whicb was 

used for the bench mark dependent variables. 

BOGUE-DUNCAN COMPOSITE METHOD 1 

The Bogue-Duncan Composite method1involves creating separate estimates 

for various subgroups 'of the population ar,d then summing these components 

to arrive at tota1s. Th~method uses data sources specific to certain 

subgroups of the population as predicted by logice.l or demographically sound data relationships. Events such as the records of births are used 

as symptoms of population compos.Hion. Throughout this discussion, such 

events when they are recorded and tabula,ted'.are referred to as both 

symptoms (their logical status) and independent variables (their statistical 

ut i1 ity). Spec; f1 ca 1ly, the Bogue-Duncan method makes separate estimates 

of the popul ation over 45 year's of age from recorded deaths for both mal es 

and females ov~t' 45 years, and the sex and c.ge specific death rates for 

persons over 45. The population ages 15 to 44 is estimated by inspection of bi rth records. The estimat'ion procedure fi rst estimates the number 

of females from the number of births and .birth rates, and then estimates 

the number of males from the estimated number of females and the malefemale ratios. Children aged five to fourteen ar:e,estimate,d{from school 

enrollment data and school enrollment rates. Children r~Ss;\'lthan five years 

of age are inferred from ratios of chilaren under five and the female popu- lation. After these separate component estimates are completed, estimates 

for age, sex and total population may be constructed by simple addition. 

Whi 1 e it is not usually done, it woul d bepos~ i b 1 e to introduce a cl ass ification variable, such as ethnicity, if the information abc-Jt births, 

deaths,and school enrollment is available by ethhic categories. The 

reason that this method is not recommended for small areas is that the 

rates ,of births, deaths and school enrollment vary widely over a popula- tion of census tracts in a culturally and socially diverse metropolitan 

area. 

Thus, the utility of the Bogue-Duncan Composite method is the notion 

of creating separate estimates for various types of population subgroups, each b,'\sed on the; nformation source i'/hich should be most powerful for 

the est~mation of that group. The pl"ocedure for summing the estimate.s 

created for subpopulations to totals I"emains a viable way of increasing 

the validity of total estimates. 

199 /" > • 
.. ~),. .' 

RATIO CORRELATION METHOD 

While the notion of subpopulation specific estimates was borrowed 

from the Boque-Duncan scheme, the actual estimation method comes from 

another sta~dard technique, known as the Ratio Correlation or Multiple 

Regression method. This set of procedures also utilizes symptomatic 

information as proxy measures for population parameters 1t/hich are not 

themselves directly measurable. In its simplest form, a ratio correlation estimate involves two points in time and three kriown values with 

which to estimate one unknown value. An example might be drawn for 

Hyputhetical City, involving teiephones in service as symptoms for the 

estimation of the total population. Let us assume that in 1960, the 

total population is known from the Federal Census of Population to be 

200U persons. If in this year, telephone company records show 500 

telephones in service in the city, there is a r~tio of one telephone for 

each four persons. This ratio can alternately be expressed as .25 

telephones per capita. Suppose that in 1965 telephone company records 

show that there are 600 telephones in service. In an attempt to estimate 

the number of persons at that time, an analyst might apply the ratio from 

the 1960 finding to the current number of telephones and conclude that 

the 1965 population of Hypothetical City could be estimated to be 2400 

persons. The validity of such a conclusion depends 'on the assumption 

that the telephone per capita ratio remains constant or nearly constant 

during the five years. 

A more cotmlon application of this method involves the charting of 

change variables over at least two points of. time. This type of ratio 

correlation method invo1ves two bench mark years for which both direct 

and. 'indirect information can be evaluated. This application often employs 

the investigation of rate change between the symptom and population parameter over a period of time (per'naps behlleen Decennial Censuses) and then 

uses the rate of change in the relationship to further refine the estimates. 

Other similar uses involve the use of proration of growth, wh2re an estimate 

for a large spatial unit such as a state is allocated irito estimates ~or that 

state's counties on the basis of the inferred change in the county's share 

of the total population. 

In a still lilore practical application of the ratio correlation 

technique, an estimate does not depend on any single symptom type but 

rather a number of different symptoms combined in some fashion. Methods 

of combining multiple symptoms include: the use of a linear least squares 

equation (Multiple Regress;on)~ the use of symptom transformation and 

averaging, and the use of weights based on a priori assumptions. The 

most common of these alternatives is the use of Multiple Regression. Using 

many well chosen symptoms is believed to have the effect of partially canceling out the effects of an unforseen drastic change in the relationship 

between any single symptom and the dependent variable. An example would 

be a drastic reduction in the birth rate. 

The primary shortcoming of this technique is that for many types of 

demographic phenomenon, especially those related to highly volatile social 

conditions, the temporal interval on which such change is measured (ten 

200 

years) is simply too long. Many phenomena change in ways that could not 

be a~curatel~ reflected in an analysis of rate of chah'ge during the period 

of ~lme cons1~ere~. To assume that the rate of change may be extended in 

a llnear fashlon lS ~ot soun~ for many investigations. A good example would be the change 1n the b1rth rate during the period of time between 

the 1960 and 1970 censuses. Obviously the use of a linear orojection of 

the observed 1960,to 19~0 change for the years subsequent to the i970 

census could be hlghly lnappropriate .. 

RECOMMENDED ESTIMATION METHOD 

The method of ~stimation recommended combines aspects of both the Bogue-Ou~can Composlte.method and the statistical formulation of the Ratio 

Correlat10n or Regressl~n method. Borrowed from t.he composite method is 

the use of separate e~t1mates /or subpopul ations, surrming for total s, 

and,the concern for rlchnes~s)n population composition. From Ratio Correl,at1On me~hod~ com~s the reg'Y'ession formulation for combining various 

symptomatlc f11es 1n an attempt to arrive at optimum predictive equations. 

One ad~itional i~gredientwas chosen for inclusion in the estimation ~odel. It 1S the notlon of controlling for type of urban area. Contro1'l1ng h~sWi~esPtea~ uti1ity in statistical investigations dealing with 

factors whlc~lmp1n~e o~ some other relationship. In the case of small ~rea populat~on e:t1mat1on, an attempt was made to control foY' differences 1~ the r~latl0ns~lps between symptoms and population parameters across a 

h1ghly d1fferentlated metropolitan region. This applicatiQn for estimation 

has been suggested by Chu, Goldberg, Morrison,2 and others. 

NECESSARY ASSUMPTIONS 

3 '>":~';::;~".: ~':"'" ~\'\ \, '; 

Shyrock and Siegal have noted that th€ extent to which'iYldirect data 

may be useful in models of population estimation is directly related to ,the 

effect that factors other than population composition have on the va1ues: 

of the symptoma tic data. ' . >,: 

An examp'le which illustrated this point is the constd(;ration of the relati?nshf~ between the occurrence of a juvenile male ~~~the juvenile pY'obatlon f'¥le, and the number of males 10 to 17. The assumptionwllich underlies the use of a regi~ession framework for annual estimation is that 

over time, the relationship between the symptomatic vaV"iable and the dependent variable remains constant. This assump'Uon is necessary 'in light of the fact that direct data is not available for periods of time between 

censuses. Obviously, the assumption becomes more tenuous with an increased 

interval between the estimated year and the benchmc,i"k on which the estimation equations have been constructed .. 

A second assumption is that the relationships between symptomatic 

and dependent variables hold over space. This assumption suggests that 

the proportion of males agesJO to 17 which show up on a juvenile probati011 

file in a developing suburb i~ the same as the proportion found in a 

central-city ghetto area. This assumption obviously igno.res the heterogeneity of metropolitan areas, Anyone familiar with Amer-lcan cities would 

201 
expect that differelltiation does exist and different relationsh-lps among 

symptoms and population.parameters could be expec~ed: This suggests the 

possibility ofcontroll1ng for relevant charactenstlcs of urban subareas. 

This was accomplished with a ne~Jly constructed variable, liThe Urban 

Maturation Index". . 

DEVELOPMENT OF ESTIMATION EQUATIONS 

The development of equations for the estimation of population parameters begins with assumed relationships. For example, it is assumed that 

the number of male juveniles which show up on the juvenile probation files 

would be positivJ2;lyrelated to the number of males aged 10 to 17. Similarly, 

we wbuld expect the .number of women ages 15 to 44 to be related to the . number of reported births. Such intuitive notions about the data relatlonships guide the creation of estimation equations. 

This paper di scusses changes between the years 1968 and 1971, wher'e 

it was necessary to createuniqlJe estimates of population for use as denominators, i.e. populations at risk, for these four years. Since census 

data Ls available for the year 1970 from the Decennial Census of Population and Housing, 1970 served as the benchmark for developing the estimation 

equations. TheY'efore, estimation for the years 1968 and 1969 invo1ved 

reverse estimatiun. This was done for a number of reasons. First, the 

years in question were temporally closer to 1970 than to 1960, the date of 

the prior census. This helps minimize the changes OVf:r time 011 the factors 

which \'Jould alter the relationships between population parameters,~ the dependent val"iables, and the symptomatic variables. Secondly, in order to 

have a benchmark from which to build equations, it is necessary that the 

same data be avai1able for the benchmark and the years to be estimated. 

In 1960, much of the locally collected data was not prepared in machine 

reada;)·le form. This limitation precluded using a forward and reverse 

techniqut for the estimation of the years between censu~es or for the 

systematic altc:ra,tian of weights based on past trends for the estimation 

of years subsequent to 1970. 

Definition of Areas 

Prior to the deve10pme~t of estimation equations for Los Angeles 

County, tvJO spatial problems needed resolut·ion. The first was the definition of areal subunits for which estimates are to be reported. The second 

problem '..vas to define the universe of areal coverage for th(~se estimates. 

For ELlbareas, the logical choice of 1970 census tracts couid not be employed since there had been a great many changes in tract boundaries between the 1960 and 1970 ~p.nsuses. This problem arose as a result of the 

fact that during the yeari 1966 to 1970, the local area data files pre- pared by the various agencies still employed the 1960 census tracting 

system, In fact, until 1973, some of the agencies in Los Angeles County 

were still using the 1960 census tract grid for geocoding their records fo~ individuals. This problem, coupled with the fact that the criterion 

variables for the benchmark year 1970 were to be selected from the 1970 

census, required the use of a special set of comparable areas developed fl~om both the 1960 and 1970 census tracting schemes. 

202 

t,; 

,. 

i: 

~" 

The use of this setaJ comparable areas en'abled the merginq of data 

which contained either of the twogeocoding schemes. The re'su1t was that 

data items for areas wi'th constant boundaries could be matched. The comparability grid for all of los Angeles County consisted of 1157 areas 

called "correspondence tracts u • This compares to 1254 census tracts for 

1960 and 1584 census tracts for the 1970 censuses, 

It should be noted that for some areas of the metropolitan region, the correspondence tracts were the same as either the 1960 or the 1970 

systems, since boundarie's had not changed between the two censuses" For 

other area~. where changes in the tractil1g systems had occurred, it was 

necessary to aggregate on both of the systems unti1 cOlTlTlon boundaries 

were achieved. 

. The second sp~tia1 question involved the total area of coverage for 

thls study, the unlVcrse of territory, for which estimations would be 

created. The determination of this area involved the elimination of 

correspondence tracts on the basis of three criteria taken from the 1970 ce~sus. These criteri,a were: total population, total number of housing unlts, and the percent\~fthe total population which resided 1n group 

quarters. Correspondence tracts were e1imi nated if there were' either 

less than 100 persons total population, fewer than 10 individual housing 

units, or .greater than 50 percent of the population in group quartets. 

In addition to these criteria, the tNcts which encompassed the channel 

ishnds and tracts which consisted of vessels in port were' eliminated. 

The result of this e1 imination process provided 1142 correspondence 

tracts as the universe; of territoY·y. 

Development of Data Ft~ 

Once the areal definition 'vias completed, the next step::was to develop the necessary data files for building the estimating equattons at the':". 

benchmark year, 1970. Files used as symptoms included data which con- . tained individual case records pertaining to births, deaths, reportable diseases, adult and juvenile probation records, and reported mental health 

cases. 

The agencies responsible for the creation and m!llntenance of these 

files had included within each individual record, the census tract of 

residence for the individual to which the information pertained. This 

eliminated the step of adding the census tract code:; either by hand or 

with the use of the Census Bureau's unimatch computer system. 

Each of these files also contained information about the individual 

to which each record pertained, including age, sex, ,'ace and other relevant characteristics depending upon the agency~s interest in the individual. 

It should be noted that the file on births contained information for both 

parents, as well as for the child. All other files pertainQd to only one 

individual. 

One general problem with the in.formation as collected by the var·ious 

agencies should be noted. The Spanish ethnicity classification schemes 

used by the agencies were not all consistent with each other. For this 

reason, it was deemed advisable that each of the files should be processed for spanish surname codes. With one exception, this was done with the 

203 
Spanish surname syntactic analysis prog~am. ~he ex~eptio~ was t~e ~e~tal I 

health file, which contained only the f~rs~ flve le~te~s 1~ the lndlvldual s 

name due to considerations of confidentlallty. For thlS fll~, th~ ~ode~ 

supplied by the Department of Mental Health were used: The,ldent:f1cat:on 

of persons with Soanish surname was of concern for th1S proJect,sl~c~, 1n 

Los Angeles County, individuals of Spanish heritage make up a slgnlflcant proportion of the population. 

At this point in the preparation of the data files, all n~mes, a~dre~ses, 

and other information not relevant for the purposes of,populatlon est1mat1on 

\'/ere eliminated from each of the six local area data f1les. The next step in file preparation was the aggregation of the individual c~se r2cords to 

correspondence tract areas so that information comp~ra~l~ w1th ~he 1970 

census could be achieved. The information for all 1nd1v1duals 1n each,of 

the correspondence tracts was summed to yield area totals only. The flles 

were then in the form of census tract summaries. F~r ex~mple, for tract 

2032, there were 55 births in the year 1968. At t~ls,po1nt, ~he chara~teristics of individuals were lost and only area statlst1cs r~ma1ned. ThlS 

assured th~t all information obtained from the local agencles would remain strictly confidential. 

After' aggregation, separate master fi ~ es for each of the year's 1968 

to 1971 were prepared. In addition to vanables for each year from the 

symptomatic data files, dependent variables for t.he year 1970 were concatenated to the symptom file for that year to serv~ aS,the benchma~k 

for the development of estimation equations. At thlS t~me, the vanable, 

"Urban Maturation Index", was added to each of the yearly local area data 

files. 

At this point, one file (1970) ha~ bee~ created upon which to ~evelop 

estimation equations and three other f11es l1968~ 1969, 1971) to w~lch 

these equations would be applied to generate estlmates for the varlOUS 

population parameters. 

Checks For SpuriOUS Re1ationships 

The next procedure atter.;pts to e1 imi nate t~e poss; bi1 i ty of us 'j ng sourious variance for the estimation of popula~lon parame~ers. ,Two J1j~'t;hDd~ were employed. First, each of the varlables was Hwestlg~ted 

with regard to minimum, maximum and. mean values. Those symptomatlc var~ables which had a mean number of persons per tract le~s than ten were 

e'l;miPC(ted so that estimates would not rely on symptom~ Wh1Ch were rarely foun~ within certain areas ~f the county. Secondly, 1n a~ attempt to 

control for for errors which arose from invalid ~r unre~la~le data 

prfJparation procedures, each variable was investlgated wlthln each tract 

ov:er all four years. 

The object of interest here was the occurrence of unusual changes in 

the values of variables between years. The method used com~ared the 

change between values of individual var~ables for a trac: wlth the changes observed in the v~lues of all other vanablesfor that bact. ,For example, if the reported number of deaths to men in a tract changed radlcally between the years 1968 and 1969 but the number of reported deaths to women 

was relatively stable, as were most other symptomatic variables, the tract 

204 

-~ "\~\ 

Wo,s nagged for further invest igatiol1. If in fact, the reported number of 

deaths to men was the ordy variable to exhibit dramatic ~ncrease, it could 

be assumed that there was some irregularity in the coding or data collection 

procedures and that variable II/auld be elim'jnated from the list of candidate variables for inclusion in the process of developing estimation equa- tions. This was done only when the irregularities showed up over a number 

of tracts. The decision was based on whether or not the variable was 

suspect in greater than 20 percent of the tracts in the universe of 

territory. Few variables were actually eliminated for this second reason. 

Those which were eliminated came principally from the mental health file. 

Selecti~n Of Final Symptomatic Variables 

From the variables which remained, the most probable symptomatic variables for each estimate were selected on the basis of a theoretical 

relationship between that variable and the dependent variable to be estimated. In the desire to estimate the number of wome~ aged 15 to 44, it 

seemed that the number of reported births in a tract could be assumed to 

be related to this parameter. S1milarily, the number of reported deaths 

to persons over the age of 64 was assumed to be negatively related to the 

number of children under the age of ten. Some of these a priori assumptions Were rather loose since the a.im here was to develop a pool of candidate va)~iables ~"hi:Ch was manageable from the stancipo'il'lt IOf the statistical 

procedures to be used. A multiple stepwise regression was used to select 

the most powerfui symptomatic vari a.bl es from this i"ather large pooi of 

candidate lla.riab1es. During this process, il!!rl attempt was made to select 

sets of variab'les composed of symptomatic variables fro!p all of the six 

source files (births, deaths~ disease, mental health, ~nd juvenile ~nd 

adult probation). This was done so Hialt tr'lQI single estimate would be 

heavily dependent on any sing1e source data file. For example, in this 

way, it ~'l/as anticipated that the estimates wOUlld be relatively independent of any single change in the re1ationship between a symptom and the popula- tion parameter. The aim here was to min'im'iz,e the possibility that a rea1 

change in a proportion, such as a change in the birth rate, could alone 

Significantly bias the es.t]mates. 

The symptom selection process was aided by the inspect]o)!') of tables 

of simple product !oomei1t correlations (Pearson's R). It is important to 

note that vat'iables which were felt to have important relationships on 

the basis of socioiogical assumptions were not e1'iminated on the basis 

of minimal correl.ations. This was done for the follovJing reasons. First, 

it is possible that when a third variable is controlled, as in a multiple regression scheme, variables with little apparent first level correlations 

will add to the overall ability to predict the dependent variables. Secondly) the population of tracts are widely divergent in characteristics as the 

result of patterns of urban differentiation and specialization. For this reason~ one variable. could be very important for inclusion in the cand;-

date list for certain types of areas, while its importance might be minimal for the whole county.. This was found to be the case for a number of 

variables and \·,111 be discussed later in the description of controlling for levels of urban maturation. 
',:I: 

The reduced set of candidate variables was then processed using the 

SPSS Statistical Package with the stepwise multiple regression program. On the basis of an iterative process, the procedure selects those variables which, in linear combination, do the best job of predicting the 

dependent variables. The choice of variables includes consideration of 

the optimum explained variance and the significance of the slopes. One 

stepwise multiple regression was run for each of 28 estimates using as 

dependent variables the known or criterion variables from the 1970 census 

and the symptomatic 'Jariables from the local agencies for the year 1970. 

From the variables selected by the statistical procedure, a subset of 

fewer than ten which were most powerful were noted. These variables 

were then used to form the estimation equations for the total county. 

CONTROLLING FOR DIFFERENTIATION WITHIN METROPOLITAN ARlrAS 

There are volumes of literature based on description and classification of the differentiation wi thin urban metropo1 itan a:'eas. In the 

thirties, Park and! Burgess4and their students describ2d the differentiation within the Chi<;:ago metropolit~ln area using a framework which became' 

popular within the field of Human Ecology, known as the concentric zone 

theory. Others have shown that, while the concentric zone pattern of 

urban differentiation may have fit expanding Chicago quite well ,the 

same pattern was not applicable to all cities. However, the search 'for 

a purely spatial !.hrban typology did senst~ize researchers to the fact 

that activities and cultural differentiation w'ithin various subareas of 

cities result in different patterns of behavior. 

Coming from economic studies "is the notion of stages of deVSlopment in the use of land within metropolitan ar'eas. Hoover and Verll0l1 postu- lated an I.wban land usec.yde which proceeo.sfrom stages of settlement and 

building up, to intensh1e residential UJJse~ to use patterns other than 

residential .;.- principly commerka'i and industrial, and fiMHy to stages of urban renewaL"This perspective on urban development sugges.ts that 

urban morphology should be viewed 'in terms of the land use stage of 

sections of a metropolitan area. 

From this perspective, a numbel" of stl.lldies have employed the not'ion 

of Ifdate built Up,lf (B. Duncan et al, Van Arsdol and SchuermaVlJ~ 1971).6 

This concept has been used as a tool for the investigation of changes in 

neighborhood cohorts with respect to population aggregation and expansion, and the aggregation and expansion of ethnic populations. The use of Ifdate 

built Upll attempts to get at the processes of development of metropolitan 

subareas. For this estimation project, a variable similar to "date built 

up" was constructed. 

For the purposes of population estimation, such a notion may control 

for differentiation within metropolitan. regions. Given the assumption 

that the relationship between various symptomatic or indirect data and 

the population component values is to be controlled within a situation 

of urban differentiation, distinguishing subareas on the basis of status 

within an urban typology of stages of growth would seem appropriate. 

Toward this end, a new index of urban subarea position within a cyclical 

developmental process has been devised. Tfie ori,ginal variable, "date 

··206 

'.we? 1 

, 

~~~~ii~rU~~~ ~!~~1t~no~h~td~!:s~tt~~i~~i~SC~~~U!c;~~C\~~~i~~~~u~es~~~~e ~ott~owever~b~eflect more advanced stages of urban development. There 

1S e POSSl l11ty that a subarea may exhibit persistence in terms of the 

type of land us~ .. ~ compound index was constructed which takes into 

account ~he posslbll~ty th~t an area mayor may not change from a IIbuilt 

U

UPbstaMtet to ~ne of lntenslve residentia1 use, This measure is called the 

r_an a uratlon Index. 

Urban Maturation Index 

Specifically, the.Urba~ Maturation Index includes the notion of the 

Use of ~and for both slngle and multiple dwelling units. As such, the Urban Ma~ur~tlon Jn~e~ refle~ts both the first stage of settlement-subdivision ~h tt e transltlon t~ lntensive residential land use, Figure 1 demonstrates 

a a cross-tab~lat1on of these two measures of urban maturity was em- pl~yed. The varlable on the Y axis is the original variable "date built ~P , ~~ u~ed by Dun~an, The X axi s contcdl11s an analogous measure cons tructed 

drom . e ate at WhlCh a census tract achieved a multiple dwelling unit 

enslty of ~t.least two units per acre. When these two variables are 

cross-classlfled,.a matrix of nine cells results. The cens were then 

ranked on the basls of the concept of Ii llrbanness." These f'anks are d'isplayed as the uppermost number within each cell (See figure 1). -. 

. ,~t should ~e remembered that although there are nine discrete classif!Catl?ns.to thls.newly developed scheme~ they are descriptions of posi- tlon wlthln w~at ls.essentially a continuous process of urban development 

In the same llght, It must be stressed that although these positions iii a~ urban ~ypology a~e ranked.for convenience during statistical procedures these are essentlally nomlnal classes rather than ordinal ones. ~ 

. Area o~e is the classification which represents the most urban conditlOn for th1S scheme. This value is ascribed to. areas that met the c it . Of.two.mul~iple dwelling units prior to 1940~ but have not yet met th~ erla c~lter~a wlthrega~d to single dwelling units. This is, at first, misleadin 

SHlce lt seems to llnply that the criteria for single dwelling units will g 

be m~t at some date subsequent to the 1970 census. This is not the case 

for lf a ce~sus tr~ct met the criteria for muH'iple dwelling units befor~ 1940, but (hd not ln ~940 meet the criteria for sing1e dwelling units, one 

must assl1m~ th~t the lnte~sity of residential use for that area e)(ist~~ in 

a stage ~·'tnch.1S ~eyond, 1n a deVelopmental sense, that of an area which 

meets both crl ten a. An area that in 1940 met both crite~'i ill must be as~u~ed to br; in an ea~lier stage of development since the land is not ~tll1zed to lts potentlal for high density residential activities. If 

1t we~e, thr; pre~ale~ce of multiple dwelling units would cause the single dwell~ng umt cnterlon not to be met. Construction of an index in this 

way !ltS t~e Hoover and Vernon notion of stages of development in the land 

use "ycle 1n a way that accounts for the cyclical nature of the pattern ?f land use ch~nge .. A further extension of this notion would be one that 

lncorporated hlstorlcal measures on commerical and industrial land usage. 

207 

'M 
r, 

Date of Maturation 

Multiple Dwelling Units 

Pre Pre Pre 

1940 1960 1970 

- - - - - - - - - -'J 

• 

2 4 7 

Pre. 65 86 60 18.4 

1940 • (5.7) (7.5) (5.2) 

- - --. - -- - .... 

Date of I 3 5 8 

Maturation Pre. 41 139 410 51. 7 

1960 I (3.6) (12.2) (35.9) 

Single - - - - -. Dwell i ng 1 6 9 

• Units Post. 58 39 244 29.9 

1970 • (5.1) (3.4) (21 .4) 

- - - - - - - - - - - - 1142 

14.4 23.1 62.5 100.0 J 

Figure 1. URBAN INOEX MATRIX 

Inspection of the ranks of the urbanization index in Figures 1 and 2 

demonstrate that the measure is logically consistent with the notion of 

stages of development. Area nine is the least urban of the possible values. In this type of area, neither the criterion for multiple or 

single dwelling units is met. 

The use of this newly c0nstructed variable for development of a 

regression type estimation model proceeds from the need to adjust the 

prediction of the dependent variable. The basis of adjustment is the 

differences in the relationships among areas which are differentiated in 

terms of the land use cycle. The notion of land use is itself not critical 

for the estimation of population component values, since it is not land 

use, but rather population composition that is of interest. The notion of 

land use instead implies a range of demonstrated differences in the characteristics of the classified populations. There are important social 

conditions which correlate with the developmental and historical characteristics of neighborhoods. These co~relates imply different levels of fertility, morbidity', incidence of mental health disorders, mortality, age 

structure, and crime. That the relationship between the proportion of 

occurrence of such events should vary systematically by urban maturation 

level is sufficient reason to control for differentiated areas when attempting to estimate population characteY'istics from the occurrence of these 

events. 

208 

Statistical Analysis Steps 

First, ~ s~a~istical test was performed to discover whether or not 

there were slgn1f1cant ~ifferences in the slopes of the regression hyperpl~ne~ ~mong t.~e mne urban maturation areas. If there \lJas shown to 

be no slgn1f1cant ~lfferences among slopes, this new variable could be add~d to the equat~ons as additional information for the correction of est1~at~ ~alues ~slng a multiple eta procedure. If there was found to 

be.slgn1f1cant d1fferences among slopes, then a multiplicative relationSh1P rather ~han.an addit~ve one could be assumed. In this second case, 

separate.est1mat1ng equat10ns would have to be developed for use in each 

of the n1ne ~rban matur~tion areas. The test used for this investigation 

was the mult1ple analys1s of covariance . 

. The result of this test was that for all dependent variables, interact10n was s~ow~ ~o occu~ across all nine urban maturation areas (i.e., 

there w~re slgmf1cant d1fferences among slopes). In one sense this 

resul t 1S a t~st~ment to the utility of the urban maturation ar~a scheme 

for the descnptlOn ~f suba~eas within a metl"opol itan region. It demonstrated that.th~ van~ble .. U~ba~ Maturatio71 Index", is indeed, useful fo~ the stat1st1cal d1fferent1at1on of areas on the basis of the relationSh1PS among population parameters. 

1. M~ltipl~ dwell~ng unit density achieved before 

dwell1ng un1t dens1ty not yet achieved in 1970 . ;; 

2. Multiple dwelling unit density achieved bef;re 

dwelling unit density achieved before 1940. 

3. Multiple dwelling unit density achieved before 

dwelling u~it density achieved before 1960. 

4. Multiple dwelling unit density achieved before 

dwelling unit density achieved before 1940. 

5. Multiple dwelling unit density achieved before 

dwelling unit density achieved before 1960. 

6. Multiple dwelling unit density achieved before 

dwelling unit density not yet achieved in 1970. 

7. Multiple dwelling unit density not yet achieved 

dwelling unit density achieved before 1940. 

B. Multiple dwelling unit density not yet achieved 

dwelling unit density achieved before 1970. 

9. ~~ltipl~ dwe11~ng unit density not yet achieved 

dwell1ng umt dens1ty not yet achi'~ved in 1970. 

1940, single 

1940, single 

1940 ~. single 

1960', single 

1960, s i ngl e 

1960, single 

in 1970, single 

in 1970, single 

in 1970, single 

'----____________________ '____________ ---..J 

Figure 2. URBAN MATURATION INDEX AREA DEFINITIONS 

209 

- -_._-- ----------------------.... 

----------------------
While the power of the Urban Maturation Index was suggested by the 

above finding, the unfortunate practical result of the multiple analysis of covariance test was that separate estimation equations had to be constructed for each of the nine. areas. Therefore, the original pool of 

candidate variables was again searched for symptomatic variables sp~cific to each of the urban maturat.ion areas, since using symptoms based on 

relationships developed for the entire county had been demonstrated to be 

less powerful for certain of the Urban Maturation Index areas. 

Again, each of the potential variables for use in the list of 

symptoms was investigated for number of cases within tracts contained 

in each of the Urban Maturation Index areas. Variables which had too 

few cases were again eliminated from the lists of potential candidates. 

Following this selection procedure, the nine sets of candidate variables 

were then processed with multiple stepwise regression in an attempt to 

select sets of 5 to 10 variables to be used in the actual estimation 

equations for the tracts contained within each of the nine areas. There 

is another way of demonstrating that creat'~g separate estimating equa- tions for each of the areas significantly increased the ability to pre- dict or estimate accurately. Reference to Figure 3 wi1l demonstrate 

the differences between the R squared values for the estimating equations 

for the entire county and the values of the R squares for the equations 

developed for each of the nine Urban t~aturation Index areas. 

When all 252 equations were finalized, they were stored for input 

to the ESTIMATOR program. ESTIMATOR was run for each of the study years 

for which estimates were needed. The files of yearly estimates were 

then sorted and included in the master data file for the construction of 

the desired rates as described in the Introduction to this Chapter. 

NOTES 

lDonald J. Bogue and Beverly Duncan, IIA Composite Method for Estimating 

Postcensal Population of Small .Areas by Age, Sex, and Color,1! Vital 

Statistics--Special Reports 5 Selected Studies [an HEW, National Center 

for Health Statistics serial publication], Vol. 47 (August),1959. 

2Shih-Fan Chu, lIOn the Multiple Regression Method of Population Estimation," 

Nevada Po ulation Studies: Technical Re ort (University of Nevada, n.d.); 

Davi Goldberg, et al., IIEstimates of Population Change in Michigan, 1950-

1960,11 Michigan Population Studies, No.1 (Ann Arbor: University of Michigan, Program for Research in Population and Human Eco'iogy, 1960); David 

GOldberg, et al., IIA Test of the Accuracy of Ratio Correlation Population Estimates,1I Land Economics, Vol. 40, 1964, pp. 100-102; Peter A. Morrison, 

Demographic Information for Cities: A Manual for Estimatin and Project- ing Local Popu ation Characteristics, R-1373-NSF Santa Monica: R.AND 

Corporation, 1971). . 

3Henry S. Shryock, Jr. and Jacob S. Seigal and Associates, The Methods and 

Materials of Demography (Washington, D.C.: U.S. Bureau of the Census, 

1973) . 

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4Robert E. Park, Ernest W. Burgess, and Robert D. McKenzie, eds., The City 

(Chicago: University of Chicago Press, 1967). 

5Edgar M. Hoover and Raymond Vernon, Anatomy of a Metropolis (Garden City, 

N. Y . : Doub 1 eday, 1962). 

6Bever1 Duncan Georges Sabagh, and Maurice D. Van Arsdol, Jr., "Patterns 

f CityYGrowth,'~ American Journal of Sociolo9..l, Vol. 6? (J~nua~y},.1962'd o 419-421; Maurice D. Van Arsdol and Leo Schuerman, Red~strlbutlon an ~~~imi1ation of Ethnic populations: The Los Angeles Case, Demography, 

Vol. 8, 1971, pp. 459-480. 

212 

INTRODUCTION 

CHAPTER 16 

TOOLS FOR FORECASTING 

Frank Lockfeld 

When the locations or coordinates of criminal 'events are located on a 

map along with geographic identifiers the resulting display can be a useful 

tool in detecting and diagnosing crime patterns. The statistkal data 

resulting from the map encoding of events can, with proper cautioh~ be used 

in forecasting events. .. 

COMPUTER MAPPING 

The ability to identify the locations of criminal events can significantly enhance crime analysis. Knowing where different crimes occur assists 

in the diagnosis of problems, facilitates the development of more accurate 

baseline measures, provides a basis for focused programs, enables program 

evaluation, and is valuable for projecting levels of activities. 

Products developed by the Bureau of the Census have improved the 

availability and cost-effectiveness of location identi-fication through 

computer techniques. A computerized map, called a Geographic Base File 

(GBF/DIME) was developed for most metropolitan areas as part of the 1970 

decennial Census. These maps have been corrected, updated and extended in 

many Standard Statistical Metropolitan Areas (SMSA's) throughout the 

country. The GBF/OIME identifies the address ranges along each street 

segment together with geographic identifiers relating to each side of the 

segment, such as census tract and block number, corporate jurisdiction, 

zip code, etc. Additional geographic identification, such as beat or precinct number, may be added to the GBF/DIME. The GBFjlHME identifies x-y 

coordinates at each end (node) of the segment. These coordinates relate to 

latitude-longitude and to state plane coordinate systems for states where 

such systems exist. 

"Geocoding" (Criminal Justice System Standard 4.8) is the process of 

matching the specified location of an event to the GBF/DIME and attaching 

the appropriate geographic identifiers to the other information recorded 

about that event. The Census program "UNIMATCW enables events referenced 

to a street address, street intersection or place name to be matched against 

the GBF/DIME. Another Census computer program, "GRIDS," enables computer 

line printer maps to be produced from the x-y coordinate values given these 

events (see Figures 1 and 2). Other computer programs, such as SYMAP, 

developed by the Harvard University laboratory for Computer Graphics and 

Spatial Analysis, are also available for the production of maps from 

geocoded data. 

213 
TOTAL ARRESTS 

36083(;).8 

354239.8 

347639.8 

341039.8 

334439.8 

337839.8 

321239.8 

31463'9.8 

308039.8 

301439.8 

294839,8 

288239.8 

151 

281639.8 

275039.8 

268439.8 

261839.8 

255239.8 

248639.9 

242039.9 

235439.9 

228839,9 

222239.9 

215639.9 

209039.9 

202439.9 

195839.9 

189239.9 

182639.9 

".~:~ 

--- GRIDS ---

16311601631160. 

(GRID RELATED INFORMATION DISPLAY SYSTEM) --- GRIDS ---

TOTAL ARRESTS 

SANTA CLARA COUNTY CJIC SUMMARY DATA 

SCALE 1 INCH = 2 MILES 

SHADING INDICATES APPROX NUMBER OF ARRESTS 

. 1 TO 4 ARRESTS 

+ 5 TO 9 ARRESTS 

DARKER SHADING INDICATES 10-14 AND OVER 14 ARRES1S 

MINIMUM CELL VALUE(S) O.lOUOOOE oi 

MAXIMUM CELL VALUE(S) 0.410000E 02 

FREQUENCY 

1.000000 

4.099992 

505 199 143 

~~~j111~1j~jI~~~I~1ll.' • ......... " ... ' ..... 4.099992 0.099g98 14.09998 

9.099998 14.09998 16.09998 

Figure 1. EXAMPLE OF "GRIDS" SHADED MAP. 

214 

CAPER 73 CRIME 

1594999. , 312499.8!". ::.::..:..:...::.;~---->---~--<~--,---___ -.,--.o--~_ 

308499.8 : 

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/~ GRIDS --- (GRID RELATED INFORMATION DISPLAY SYSTEM) --- GRIDS 

CAPER 73 CRIME 

DATA SHOWS TOTAL CAPER EVENTS DURING FIRST HALF 1973 

IN CENSUS TRACTS 5009. 5010, 5012, 5013 AND 5016 

PREPARED BY THE CENTER FOR URBAN ANAL YS;:'COUNTY OF SANTA CLARA 

DATE PREPARED: APRIL 8,1974 . 

I\~AP SCALE - 1 INCH EQUALS 800 FEET 

MINIMUM CELL VALUE (S) 0.100000E 01 

MAXIMUM CELL VALUE IS) OAOOOOOE 02 

Figure 2. EXAMPLE OF "GRIDS" VALUE MAP. 

215 
While map displays can facilitate crime analysis, the power of geocoded 

crime data is most effectively realized when organized as a data base in an 

interactive computer system capable of selectively extracting~ summarizing, 

manipulating and displaying the data directly on a map image.' (See Figures 3A, 3~ and 4A, 48.) 

One of the main advantages of geocoded crime data is that the events 

may be aggregated to relate to geographic units for which other data is 

available. Census data and property data are important complementary data 

items for crime analysis. A property data source, for example, that distinguishes the number of comnlercial establishments within particular areas 

enhances the analysis of crimes against such activities. Census data provides detailed characteristics of population and housing for small areas (blocks, 

block groups and census tracts) that enables the association of such char~cteristics with crime data. The use of geocoded calls-for-service data 

against property and census data facilitates beat or patrol design that 

equalize workloads,2 permits organization for strategic response to anticipated demands for police services,3 and provides documentation for estimating workforce requirements associated with changing characteristics of the 

serviced area. 

FORECASTING WITH GEOCODED DATA 

Most crime-specific analysis techniques are based on cross-sectional 

data, that is, data that is referenced to a single point in time or independent of time. A major advantage of location-coded data is that a 

greater variety of analytic techniques may be employed, especially statistical 

methods of spatial analysis where each area serves as an independent observation. A classic study analyzing the spatial distribu.tion of crimes within 

a city and their relationships to significant economic, demographic a.nd 

social detenninants is c. F. Schmid HUrban Crime Areas. "4 This study used 

'offense and arrestee data during 1949-1951 together with 1950 Census tract . data to generalize "gradients!! and "isopleths" of crime and to relate crime 

to urban typologies drawn frow sociology. The ecological pattern of crimes 

were also analyzed with factor analysis of the offense, arrestee and census 

data. In a similar study, Howard5 used cl~ster analYSis scores for census 

tracts, based on 1.970 Census data, as well as linear multiple regression, 

and 1972 arrestee data to estimate relationships between underlying social 

dimensions and arrestee rates. 

Techniques such as factor analysis and regression analysiS may yield statistically meaningful associations of characteristics, but do nlOt necessarily demonstrate causality. Equations derived from regression that statistically ~Ie)tplainll the variation among the observations for a given place 

at a given time, Dr replicate observed values within certain confidence limits~ are not necessarily transferrable to other places or other times. 

This limitation is of particular concern in the contest of crime analYSis 

and the forecasting or projection of crime. 

The genesis of crime is recognized to be in the community. but no 

theoretical construct--sociological, psychological or economic--has been 

accepted as the explanation of the etiology of crime. 6,7 While regression 

is a powerful tool of analysis, its application to data other than that in 

216 

" i 

x EVENTS SELECTION = VIEW'NP 

DRAW EXPAND SHRINK NORMAL DISPLAY 50 EVENTS RETURN G 

PIP2 SCALE TO PIP2 CENTER ERASE 

I PROCESS:t\JG CONTINUES 

/

"EXTRACTING" the data base for in-pro r II f . L . Department data), and displaying the loca~i:~~ ~an s- or-servIC~. day 25 of the sample (San Jose Police 

' m,p ,ompo,"d 0' 272 ,<portio" ,"d '""Y,i, W"". 

Figure 3A. INTERACTIVE COMPUTER GRAPHIC SYSTEM (GAD . 

and Display System) RELATING A DATA BASE TO A ~A~eO-Data AnalYSIS 

217 

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CURRENTSUPERZONES 

GADS BASIC ZONE MAP OF SAN JOSE WITH DISPLAY SYMBOLS ZONES 

o 

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NORMAL SCALE 

ENLARGE 

OVERLAY WITH MAP 1 

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SSMODE NUMBER 

CURRENT STATEMENTS 

TOTAL CONSUMED Tli\'IE ALL DAYS 1974 

DISPLAY SYMBOLS FOR EACH ZONE DEPENDING ON TOTAL CONSUMED TIME 

9 TOTCTIM(91) > 9000, 8: TOTCTIM(91) > 8000, 7: TOTCT'M191I > 

6 TOTCTIM(91 ~ > 6000, 5: TOTCTIM(91) > 5000, 4: TOTCTIM 91 > 

3 TOTCTIM(91 > 3000, 2: TOTCTIM(91) > 2000, "I: TOTCTIM 91 > 

+ TOTCTIM{91 > 500, -. TOTCTIM(91) > 0, 0: TOTCTIM 91 = 

SAVESYMB(I), 

7000, 

4000, 

1000, 

0, 

figure 4A. GADS-DISPLAYING SYMBOLS FOR EACH ZONE ON THE MAP IMAGE . 

219 

--------------------------------------.. 
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, 

which the equations ~"ere estimated suffers from the assUll1ption of ceteri s 

paribus--all things remain in the same relationship. This assumption is 

particularly difficult for social phenomena such as crime. Technology, social and cultural moreSt and other attributes difficult to measure are 

recognized to be associated with crime, but are rarely explicitly incl~ded 

in such analysis. Even if such characteristics were included in regression however, values of these characteristics at future times would be required 

to use the estimating equations in a forecasting or projecting mode. Still, 

these statistical methods should be considered. If there is any doubt about 

their application in a given instance, the advice of a statistician should 

be sought. 

Two techniques are useful for projecting crime over a short.-term period (e.g., up to 2-3 years), though neither resolves the fundamental problems above. The first technique is time series analysis. Exponential smoothing is a particular method of time series analysis that gives greatest weight to 

recent information and least weight to older data. 8 Spectral analysis is a 

technique useful for events of a cyclical pattern, such as crimes with 

weekly or seasonal variations. A second approach is the analysis of first 

d,tfferences, a method used in modeling other urban behavior. Multiple regression is a primary tool for such analysis, but the variables are rep- resented as changes over a unit of time rather than cross-sectional. The 

projection period established by such analysis is the unit of time originally represented. Like other forms of projections through eq6~at;ons, estimates of 

future values of the independent variables are required. This approach can 

use either independent estimates of such changes or fynct10ns of previously observed states. An expression that uses both varia~ons might be as 

follows: 

dASSAULTS i = a + b, MALES, 17-25; + b2 dUNEMPLOYMENT,MA!.ES + b

3dCOMMCL i + t; 

where: 

dASSAULTS = char;ge in the number of assaults in area i from time t to 

ttl 

MALES, 17-25. = number of males aged 17-25 resident in area i at t+l 

1 

dUNEMPlOYMENT, MALES = change in the areawide rate of male unemployment 

from. t to t+1 

dCOMMCL.=change in the number of commercial establishments in area i 

1 from t to t+ 1 

a, b

l , b2, b3 ~ constants estimated by regression 

Ei = error term for area i 

The usual cautions and criteria for validity of multipl'e regression equations 

apply to first difference estimates. 

221 
NOTES 

lp. E. Mantey, and E. D. Carlson, "Integrated Data Bases for Municipal 

Decisi on-Makin9, II National Computer Conference, 1975 (Montval e, N.J,: 

AFIPS Press, 1975), pp. 487-493. 

2R• V. Bradshaw, "Mr.tn-Machine Interaction for Pol ice Fiel d Manpower 

Deployment ,II Urban and Regional Information Systems Association Conference Proceedings, 1974. 

3R. C. Larson, "A Hypercube Queuing Model for Facil ity Location and Redistricting in Urban Emergency Services," The Rand Corporation, R-1238-HUD, 

March 1973. 

4C. F. Schmid, IIUrban Crime Areas~" American Sociological Review. XXV, 

no. 4 and no. 5 (August and October 1960). 

5M. K. Howard, IlPredicting Arrestee Residence Rates,1I Santa Clara County 

Criminal Justice Pilot Program, April 1973. 

6M .. D. Maltz, IlMeasures of Effectiveness for Crime Reduction Programs," 

Operations Research. ~XIII, no. 3 (May-June 1975), pp. 452-474. 

7J . B. Kau and P. H. Rubin, "New Estimates of the DetenHinants of Urban 

Crime," Th{;; ,.ml~als of Regional Science. IX, no. 1 (March 1975L pp. 68-76. 

8S. I. Gass, "Models in Law Enforcement and Criminal Justice," A·Guide to 

Models in Governmental Planning and Operations, Prepared for Office of 

Research and Development, Environmental Protection Agency, Washington, 

D.C., Contract No. 68-01-0788, August 1974, pp. 231-275; see especially 

pp. 244-261. 

222 

Part V. 

DEVELOPING AN ANALYTIC UNIT 

AT THE LOCAL LEVEL 

" 

" 

fi 
INTRODUCTION 

CHAPTER 17 

CRIME ANALYSIS: ORGANIZATION OF A TE~M, TO 

IMPACT THE LOCAL CRIMINAL JUSTICE SYSTEM 

Charles D. Weller 

A professional crime analysis team is an important element in planning and implementing crime reduction activities. The organization and development of a professional crime analysis capability requires advance thought and preparation in order to systematically plan and implement crime reduction activities at the city or county level. The decision to organize this 

capability suggests that elected officials have reached agreement on the purpose and objectives of the team and that certain assumptions are shared by those who will be affected by th~ products of the team. This chapter discusses how the crime analysis team can be staffed and made to function with 

other elements of government. 

PRELIMINARY PLANNING FOR A CRIME 

ANALYSIS TEAM 

Far too often preliminary preparations for criminal justice planning 

are overlooked. The result is that local officials may not approach a local 

criminal justice planning program with a full understa~ding of its purpose, functions, and benefits. Traditionally, mayors, city managers, county commissioners, and even governors have passively supported the need for some 

criminal justice planning, originally stimulated by the need to qualify for 

grants-in-aid from the Law Enforcement Assistance Administration. Experience has shown that these government officials often fail to fully appre- ciate or completely exploit the potential of good planning until such a unit 

-has been functioning fora year or two, without their active interest and 

input. Frequently, attention is drawn tD crjminal justice planning needs as 

the result of a critical budget d:2cision in which federal funding emerges 

as the only sol ut i on to alack of 1 oca 1 y'evenue. Therefore, an important 

part of the preliminary planning is to familiarize local officials with the 

need for and benefits of an organized, ongoing crime analysis capability and 

to illustrate how it can be made to support major procedural and policy decisions in the criminal justice system. 

There is a heightened interest on the part of mayors and city managers in acquiring a criminal justice policy analysis capability. Future national 

funding opportunities may further stimulate this interest. For the purpose of this paper, attention will be focused on the development of a crime analysis capability at the city and/or county level, fully acknowledging its 

critical value at other levels of government. 

Regardless of its organizational location, there are certain critical 

considerations that should be reviewed and addressed. 

225 
1. Does the city and county jurisdiction really want the capability 

to measure the effectiveness of its local criminal justice system, recognizing the risk that hard data may emerge within two or three 

years which declares that the existing system may be ineffective 

and perhaps mismanaged? 

2. Is the local political environment and administrative philosophy 

secure enough to tolerate research, planning, and burdensome data 

collection without unnecessary interference? 

3. Do city and agency administrators have the management sophistica- tion to implement change if research and planning justify certain 

changes in criminal justice procedures, operations, or resource 

allocation? 

4. Does the local government or its agencies possess some data proc- essing skills and experience with which to technically support the 

crime analysis team? (This is not to suggest that each jurisdiction 

must own a computer, but rather should possess minimum and general data collection and processing knowledge and skills.) 

If the answer to these four questions is an unqualified "yes," then it is 

appropriate to proceed to consider locating a crime analysis unit near the 

elected decision-makers of the jurisdiction. For planning and crime analy- sis to have impact, the unit must be at a staff position with direct access 

to the Chief Executive(s). 

STAFFING AND RECRUITING A CRIME 

ANALYSIS TEAM 

The principal thrust of this chapter suggests that a finite number and 

quality of personnel skills are required for a crime analysis team, without 

respect to the minimum size of the population served. Larger jurisdictions will desire more specially skilled and experienced personnel, but there appears to be a minimum staffing level below which such a unit should not go if any real benefit were to be realized from it. 

Jurisdictions with sophisticated personnel departments or civil service 

units may wish to create joh specifications, salary ranges, and qualifica- tions unique to the function of this new unit. Others may wish to appoint the staff without tenure and have the crime analysis staff serve at the 

pleasure of the elected Chief Executive{s). A third alternative is to do 

both--certify the staff in the personnel classification system, but require 

the Director of the unit to serve at the pleasure of the Chief Executive(s). 

There are some advantages and disadvantages to all three alternatives. Most 

important, the Dir'ectcr of the unit must build and maintain the confidence 

of his superiors, because policy analysis usually results in "tough" decision-making, decisions that emerge from force fields representing every facet of community life. It is probably a foregone conclusion that the ~rime analysis unit will need six months to a yea~ to develop its credibillty, but this testing period can be shortened dramatically if the profes- sional qualifications of the staff are exemplary. This means that the recruitment, screening, and hiring of the staff must attain the highest priority and interest of the Chief Executive's office. 

226 

~f local of~icials ar~ willing to give this degree of support to the 

creatlon of a crlme analysls team, there will' be reduced pressure on the s~aff during the first six months of operation when their princ~pal activi- tl~S are devoted to the collection and analysis of baseline crime and oper- atl0nal event data. Their early activities will take them into the dark recor~s rooms of every local criminal justice agency. Such efforts must 

also lnvolve the state agencies which serve residents and offenders, etc., 

within the local jurisdiction. 

Staffing the unit with highly qualified professional persons who are 

capable.of interacting with all levels of criminal justice personnel is a 

prerequlsite to building and maintaining a successful crime analysis effort. 

How they are recruited and linked to local government can be flexib1e as 

long as the Chief Executive of the jurisdiction has easy and routine access 

to their skills and products. 

~he personnel recruited for the crime analysis team must be ~easonably 

exper~enced and familiar with operations of the criminal justice system. 

More lmportant, they should have educational and occupational backgrounds that demonstrate the possession of an analytical mind. 

There should be within the team a demonstrated capability to understand and conduct applied research as opposed to theoretical research. The 

staff should have a "real-world" perspective of the community and the criminal justice system. 

The ideal crime analysis team member has had occupational level experience in more than one criminal justice agency, complemented by graduate level education in data collection, data analysis, ang elementary research 

methods and design. Obviously, it is possible to possess all of these characteristics without having been involved in any post-secondary education, 

but the numbers of these individuals are so few that job qualifications and 

concomitant salary ranges should be structured to attract the more formally educated and professionally experienced candidates. This should not prove 

difficult, because there are more and more individuals possessing the educational and experience characteristics needed to pursue careers in this new 

profession. 

No crime analysis "staff per population" ratio guideline, has yet been 

set forth for developers of such a crime analysis unit. The degree of sophistication for planning and analysis tasks currently available within the 

operating agencies of a jurisdiction may be such that the central city or 

county team serve more as leaders, coordinators and stimulators. More typ- ical jurisdictions lacking agency-based special analysis units require team 

personnel with a full range of the minimum skills described below. It is 

probably a fact that most of the urban areas of the United States are at the 

point of building upon an existing modest capability. 

Without addressing the amount of resources required to support a crime 

analysis team, it appears from past experience that one-half of a crime analysis team, or a team without all the, proper skill~, really is not much of an 

advantage over no team at all. Quite frankly, each jurisdiction must decide 

to what extent its crime analysis unit should get involved in the broad 

functions of criminal justice policy analysis as opposed to, or in addition ~Ol federal grants administration. 

227 
If the Chief Executive's interest is simply to comply with LEAA grant participation requirements, local and state authorities can provide reasonable estimates of the number of staff necessary to attract and monitor the 

expenditures of the annual allocation of LEAA dollars to that jurisdiction. 

However, if an effective "crime analysis and planning unit" is desired that 

bUilds its foundation on crime analysis for the purpose of providing decision-makers with hard data about policy, procedures, and effectiveness 

of a criminal justice agency or system, then the unit must possess a multiplicity of experience and skills no matter how small the population be be 

served. Smaller jurisdictions should be encouraged to give priority to 

crime analysis and "pay the freight," so to speak, in the interest of moving their criminal justice agencies toward a better system of accountability for the local tax dollars expended. If this is not an acceptable expenditure for tax payers' dollars by the local unit of government, then decisionmakers should not launch half-hearted attempts under the guise of full blown 

professional criminal justice planning efforts. 

Five full-time professional persons supported by at least one clerical 

person is a minimum requirement for engaging in full-fledged criminal jus- tice planning. This obviously means that smaller cities and counties must 

determine that "increasing the productivity of their criminal justice agen- cies" is nearly the top priority of the community. The author believes it 

should be, but recognizes that other urban problems in some jurisdictions 

must take precedence over crime fighting. Be that as it may, if the 10ca1 

units of government IIcannot stand the heat, they should stay out of the 

kitchen," with respect to professional crime analysis and criminal justice planning at the local level. For a ~'1eary numbet' of years, too many juris- dictions have desired membership in the profession without fully understanding that municipal and county level policy analysis of any kind will be 

an expensive proposition if it is to be an effective tool for decisionmaking by elected officialj, 

WHAT SHOULD THE TEAM LOOK LIKE? 

The Director 

Nowhere in the staff of the team is the diversity of experience and 

skills more important than in the te;am leader. A key requirement of the Director is the ability to establish, as quickly as possible, professional credibil ity with criminal justice agency staff and administrators. Further, 

being able to talk "cop talk" is essential to the courtship that must take 

place with the Sheriff or Chief of Police. Similarly, knowing what an arraignment is, and the difference between probation and parole, is crucial to 

the unit's credibility with other elements of the system.· Within 60 to 90 

days, agency heads will put this person to the test and make the judgements about his/her knowledge and skills that will determine the unit's relationship with that agency far into the future. 

The Director should be a generalist with criminal justice operational experience, thoroughly familiar with the operations of the whole criminal 

justice system. He must have sufficient management skills so that he avoids 

being a "do it your_selfer." He must believe in what he is doing. He must 

be able to identify problems from data and conversation and set objectives and assign tasks to staff who will be held accountable for product completion. 

228 

This m~ans that the Director must be generally familiar with quantitative analysls, systems.analysis, and financial accountability. While he need 

not be an.expert ln ~ach of these fields, he will use his general knowledge 

to make hlms~lf mea~lngful1y conversant with the "tools" of the trade and 

the experts ln speclal areas that may provide special advice. 

The cr~me analys~s unit Director must be a communicator, striving con- st~n~ly t~ ln~egrate l~formation and form bridges between disassociated crln:ll~al Justlce agencl~s: He is a "justice of the peace" bringing together adml~lstrators who tradltlonally have preferred to remain isolated and pro- tectl~e of their ~wn areas in the overall system. He must be useful; demon- s~ratlng, not aSKlng for professional recognition of his competence. And flnal~y, h~ must ~e able to conceptualize and sell action, and serve as an 

expedltor 1n gett1ng proposed changes implemented. 

T~e 9irf!ctor and his staff, as a team, must show a service commitment t~ asslstlng the oper~ting agencies. For example, the unit should plan wlth, not foy', the pol1ce department, so that real services and assistance 

tasks can be ident~f~e~ and delivered. The Director continually searches for.c~llatera~ a~tlvlt1es to conduct that make the criminal justice agency admln1strator s Job easier. This is a creative task of matching staff competence with opportunities to ass'ist .. Clinically, crime analysis teams 

should be considered a "helping profession." 

.I~ ~he Director possesse~ th~se qualities, he will stand the early cred1blllty tests, and the unlt wl1l have access to agency files records 

procedures, and even confidential problems which really are the ~ornersto~es of a l~cal crime.ana1ys1s unit. Without hard data, the unit is nothing. Ove~ tlme, ~he D1rector s elected supervisors should irust him as much as thel~ cam~algn managers; yet a sensitive line should separate the crime analysls umt from elected officials and their subordinate agency administrators. The Chief Executive must set forth the ground rules with agency heads 

to p~event ~hem from making end"'i"uns around the crime analysis unit. The ~ea~ s. sp~c1a1 statu~ needs to be openly protected. In effect, all the 

Jurlsdlctlons agencles must administratively recognize and respect the crime 

analysis unit for its expertise. 

The skills and attributes of the remaining four members of the team do 

not nece~sarily sug~est any particular organizational hierarch.Y. That can 

be organlzed accordlng to the local decision-makers' assessment of the unit's ~unctions and t~e abilities ~f its personnel. With only five or six persons lnvo1v~d, the hlerarchy should be flat, all reporting to the Director. ~ihen 

t~e umt reaches ten to twenty staff members (as in larger ju'risdictions) w1th more specialized personnel, an additional management person may be 

justified. 

. The Data Expert 

One member of the team must be trained and educated to bring sophisti- cated quantitative skills to the unit. He or she should possess system analysis skills, statistical analysis skills, data processing skills, and the 

ability to report on the results of data analysis activities. If the data 

expert also knows the criminal justice system well, he or she is the ideal 

candidate. If a choice must be made between the person with technical 

229 
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skills and no criminal justice e~perience, as opposed to the person with 

tne desired experience and weak technical skiHs, tne Director should 

cnoose the tecnnical expert. Manipulating criminal justice datil over time 

will eventually cause ttle techn.ic~,1 expert to achieve vicarious occupatianal 

experience in the criminal just~ce system. 

The.Police Specialist 

There are same functional speciality skills required on the team. One 

staff member must be exten~;vely famil iar with the field of 1 aw ~mforcernent. 

Today many col1ege-educat~d patrol officers are leaving the security of the 

police to enter criminal justice planning, so an excellent manpower pool is 

available. However, merely having some patrol experience and a college degree does not qualify lOne far entry to the unit. Among patrol officers with 

these qualifications, the Directot' should seek candidates who demonstrate a 

propensity far analytica"/ problem salving, individuals wha appear to enjoy 

working with data, and those wh~ ",rite with some command of the EngUsh language. These individuals are likely to possess skills which can be 

broadened to accomplish many tasks not integral ta police wark but nevertheless impartant to a police agency. 

Caurts/Corrections Staff 

The basic skills required for crime a~alysis at the lacal level do not 

indicate the need fot" a full-time attorney or even a full-time correctional 

specialist. What is needed is an individual who knows and understands the 

judicial process and dispositional alternatives far convicted criminal offenders. It is readily possible to find staff who have education and/ar experience in adjudication and correctional programs who 1 similar to the 

patrol afficer, possess analytical interests. One staff person can adequately struggle with local courts and corrections probl ems as his pri nci pa 1 

functional specialty, if a basic team unit is an that can be afforded. As 

the fourth person on the team, he daes double-function duty, but it is expected that the team will have a well-rounded technical expert to help with 

adjudication and correction problems, and identification and analysis duties. 

The Budget Analyst 

Finally, the fifth person is a budget analyst with an accounting background, education, and/or experience. And, if the financial analyst has 

worked within one or more criminal justice agencies, he is the ideal candidate. The unit does not need a IIbudget butche,r." Rather, the skills of an 

analytical accountant and financial manager are desired to assist in Hbudget building." Here is the one person on the team who should possess technical 

expertise desired by every operating agency. Furthermore, the availability of this team member to provide technical assistance to the operating agen- cies should be a publicly known fact. He must strive ta change attitudes 

about accountabil ity, work unit measurement, budgeting, forecasting needs, 

assessing past-expenditure experience, and most of all, Qe must sincerely believe and portray his image as a member of the tatal team, not just an accountant for the program oriented staff. 

230 

THE CRIME ANALYSIS TEAM 

Ideally, al~ members of the crime analysis team must be able to write 

and speak effect1Yely. They should be accustomed to meeting firm deadlines :e~m members, as well as the nir~ctor, should be able to recognize opportun~ 1~1es to be useful to the operatlng agencies and seizp. upon these opportuni- tle~ ta act as a catalyst for effecting other changes already deemed deslrable. 

A special a~titude for marrying the practical and academic warlds will 

help the team bUl1d personal relationships with agency personnel that are 

based.upon competence, rather than autharity. Finally, the crime analYSis 

team ~tself must tol:ra~e isolation, both personal and academic. The unit func~1ons best when 1 t 1 S only one-half a fri end to everyone. That makes 

the Job lonely, but one, nevertheless, that offers each member of the team 

a g~eat deal af per~onal ~ride, for each undoubtedly will contribute to the achleve~ent of.the lmpasslble--successfully changing the criminal justice system 1n Amerlca. 

231 
~'~-------------------~" 

CRIME·ORIENTED PLANNING 

AND PROGRAM DEVELOPMENT 

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INTRODUCTION 

CHAPTER 18 

CRIME~ORIENTEOPLANNING AND PROGRAM DEVELOPMENT 

I< • All en Payne 

In January, 1972, the Law Enforcement Assistance Administration launched the High Impact Anti ... Crime Program, a major demonstration which pY'ovided 

up to $20 million to eight large American cities to reduce the incidence 

of stranger-to-stranger crime and burglary by 5 percent in blo years and 

20 percent in five years. Underlying this program was the conceRt of "crime~oriented planning": that trime (and particularly, specific classes 

of crime) was the starting point ~or criminal justice program development, 

not the symptoms of crime. such as overworked or understaffed police, 

courts, or corrections agencies. 

DATA -- THE BASIS FOR PROGRAM DEVELOPMENT 

The basis for planning and program development in the crime",oriented 

planning model is the collection a,nd analysis of data: cr"ime data, which 

tells us how, when, where, and with what frequency individual crimes such 

as rObbery, rape, or burglary are committed; offender/data, such as age, 

sex, race, and personal characteristics of those who commit crimes; and 

victim data which indicates the characteristics and responses of those who 

have been victimized. 

The analysis of these dat~need not necessarily involve extensive compute!" operations and programs. Basic information about a crime incident 

frequently is available from police reports. Narrative incident reports 

are the source of additional information. However, as LEAA discovered in 

the Impact Program, data availabi'lity, quality, and utility can present significant initial difficulties~ even in larger cities. On the other hand, 

transforming existing data into ~seful, retrievable data is not an insurmountable task. 

Atlanta used a small early Impact grant to redesign its police field 

reporting system. Denver, in a modified manual replication of the CAPER 

System functioning in Santa Clara County, California, added to the offense 

and arrest information available through the Police Department by hiring part-time undergraduate coders to cull additional desired information from 

police reports and case files. Eventually student coders were doubled in 

number and assigned similar tasks in every criminal justice agency in 

Denver, thus providing for an expanded base both of crime and systems response data. 

Although data sources such as Uniform Crime Reports, Offender Based 

Transaction Statistics (OBTS) and victimization surveys are discussed in 

detail elsewhere in this manual, a further word about data is in order. 

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Data needs for crime-oriented planning an~ hierarchical, as is th~ planning model itself. That is, the state planner addresses statewide burglary rates 

and geographic concentrations of burglary. The city planner focuses on 

burglary occurrence by frequency, type (corrrnercial 'Is. residential), time, 

and location (census tract or police distr'ict). The project level planner 

concentrates more on methods of operation, short term trends in frequency, and specific time and place of occurrence. 

PROBLEM IDENTIFICATION 

Analysis of data, therefore, gives us a profile of individudl crimes 

by characteristics of the crime event. setting or environment, offender, 

and victim. It also tells us in quantitative terms how the criminal justice 

system responds to the comnission of certain offenses. From this analysis 

comes the identification of problems: crime problems (e.g., the 15 percent rise in the statewide robbery rate or the 20 percent rise in citywide residential ,burglaries); offender roblems' (e.g., juveniles comprising 30 

percent of .. burg1ary arrestees ; victim-related problems (e.g., preponderance of elderly robbery victims within a certain census tract); systems problems related to the response to specific crimes (e.g., city court burglary cases 

are backlogged with a 240 d~y Jverage from arrest to trial). Problems' then 

must be prioritized, which leads to the next step, the establishment of 

goals. ' 

CRIME-ORIENTED GOAL * SETTING 

Once a specific crime problem has been identified, there are two basic 

responses: reduce the cause of the problem and improve control of the problem. Cause is reduced either by attacking underlying social, economic, or 

other basic conditions that promote the commission of crime. Obviously, 

much program activity in this regard is outside the authority and responsi- bility of the criminal justice system. Non~theless, social. demographic, and other economic data related to crime analys:s can point to obvious areas 

of interagency or intergovernmental cooperation of benefit to the community in areas beyond that simply of crime control. On another level, "cause" 

can be reduced by intervening in criminal careers through programs involving identified offender groups or through what frequently are called "prevention" 

programs, such as youth service bureaus or school attendance projects in 

high juvenile crime areas. 

Control is improved either by reducing opportunity for the commission 

of criminal acts or by increasing the risks of offending. Examples of the 

* The term "goal" is sometimes used interchangee.:biy with the term "objective." In either case, the attainment of a goal or an objective must be judged on 

a measurable interim or final achievement. 

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former include target-hardening activities such as physical security improNement or property identification. Examples of the latter include concentrated police patrol and the speedy adjudication of target crime cases. 

Crime-oriented planning, therefore, leads to the establishment of comprehensive program goals that involve the whole system of criminal justice. It is not simply a police suppression model. 

Goals should be hierarchical and quantitative. There may be initial 

resistance by operating dgency personnel to the establishment of qu~ntitative goals. However, quantitative goals provide for clarity of purpose and 

the measurement of program/project results. They are an integral component of crime-oriented planning. 

In hierarchical terms, at the state level, a major program goal might be the reduction of burglary by 5 percent within one year in th~ five 

largest metropolitan areas. At the city level, this goal might translate 

to a reduction of residential burglaries by 10 percent and a reduction of 

commercial burglaries by 5 percent. 

PROJECT DEVELOPMENT 

The selection and development of-projects continues this process of 

quantification. Tactical alternatives to meet state or city goals must be 

weighed and chosen. What type or types of program activities respond most 

directly and effectively to identified problems and established goals? The 

utility of data collection and problem analysis is ~verely hindered if the 

full examination of alternatives is shortchanged, or if data iS,collected 

and mo'!ded solely to justify the selection of a predetermined project activity. 

The process of alternative selection takes place through a screen of real-wc',~~ constraints that can be political, legal, economic, soc,ial, 

moral _, " ~thical in nature. Highly competent planners and a sophisticated rat'jona'Y upervisory board might agree that a particular alternative offers 

the best possibility for reducing the target crime, yet they may not select 

that alternative for implementation because it is economically unfeasible, 

patently illegal, or otherwise contrary to prevailing community standards. 

Constraints are natural and they must be understood by planners; the difficulty arises in not letting the possibility of constraints that may be im- pos~d unduly hinder the planner in the full development and examination of a)~er;~te tactics and programs. 

The selection from among alternatives yields a plan of related program activities that can span the criminal justice system and include some activities even outside the system. Selected activities can include programs related to the prevention of the target crime; the detection, deterrence, and 

apprehension of offenders; the diversion of offenders from the criminal jus- tice system; the adjudication of offenders; and the rehabilitation of offenders, through both institutional and non-institutional programs. Data 

_collection and analysis also may point out the need for legislative change, additional data and information systems, or additional research, all of 

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which frequently are overlooked in the search for alternative courses of 

action. 

Based upon choices from among alternatives, we can imagine the following project activities and goals develo~ed in accordance with th~ reduce 

cause - improve control approach to a clty-level burglary reductlon goal: 

1. A plnoperty identification project to enroll X persons and mark Y amount 

of property within the census tracts with highest burglary rates. , 

2. A concentrated crime patrol aimed at providing X amount of patr'ol and 

Y response time during peak hours in the high burglary census tracts, 

increasing by Z the amount of burglary arrests. 

3. A reduction in court backlog for burglary cases by X, and an increase 

in burglary convictions by Y, through the addition of judicial and 

prosecutorial personnel. 

4. Offender-based programs for burglary offenders such as drug diversion 

(where offender drug abuse is substantial) or juvenile diver~ion 

projects (\I,here juveniles are found to constitute a substantlal percentage of burglary offenders). 

Several comments can be made about these project examples. First, 

many of the particular activities undertaken as a response to crime-oriented 

problem analysis are not necessarily different or unique from those that 

are undertaken where reliance on data is less extensive. The difference 

is that these activities are underta.ken in response to specifically identified and bounded problems and their goals and operation, locati?n, and 

scope of services or products are carefully related to the magnltude and 

nature of the prob'!em. Second, products or resu I ts (effectiveness objec- tives) and levels of service (efficiency objectives) are clearly specified, 

providing for clarity of purpose and the evaluation of results and 

performance. 

PERFORMANCE MEASUREMENT 

In criminal justice, evaluation is a concept whose time has come. It 

remains an integral link in the crime-oriented planning chain of data-based 

problem analysis and goal formulation. Good evaluation depends tlpon a clear 

statement of project purpose, and indeed, good evaluation begins with the 

establishment of quantified, time-phased project goals, objectives, and 

milestones at the project design stage. 

A direct link to specific crime reduction goals cannot be established 

for every project, nor can elaborate control or experimental groups be 

utilized in every case. Nonetheless, project level self-assessment can be 

accomplished by any grantee where initial attention has been paid to 

clearly delineating the goals of the project and where sufficient attention 

has been paid to defining performance objectives and assuring for the availability, ccl1ection, and analysis of data. 

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.Linkages are ~s important t? the evaluation process as they are to the 

earller stages ?f problem analysls and goal formulation. After a project s~rategy to a~h~eve a program goal has been selected, effectiveness objec- tlves and e!flclency objectives must be established. Examples of the forme~ can l~c~u~e specific crime reduction objectives, or objectives ad- dr~ssl~g re~ldlvlS~ red~ction or decreased court processing time. Activity obJe~tlVes ope~a!1?nallZe" or explain quantitatively what levels of ser~l~es or .act~vltles will be performed or provided. For both outcome and 

actlvlty obJectlves, specific data measures are required to assess project 

progress. ~fter data elements are identified, a plan must be prepared for 

the collectlon, analysis, and interpretation of the data. 

.Several general comments about evaluation of crime-oriented programs 

?re 1~ order. "Hard" e~aluati?n is possible eve~ where objectives appear so~t. As the.e~a~uatl0~ de~1gn of the St. LOU1S Home Detentiqn Impact proJect shows, lnltlal obJectlVes such as "Keeping [assigned] youths 

trouble free and available to the court" are amenable to the establ i shment 

of specific effectiveness measures. Second, evaluation is not the end of ~he crime-oriented planning Gycie but the beginning of the ~ecycle. Pro- Ject~ such as Denver Impact's Special Crime Attack Team (SCAT), which 

contlnuously uses e~aluation data to adjust its concentration on target ar~as and target crlmes, bears out the effectiveness and utility of evaluatlon results upon project operations. 

CONCLUSIONS 

. Crime-oriented planning techniques, in summary, '~eflect first a classlcal, accepted framework for problem solving, and second a reliance on 

data and d~ta analysis for the identification of problems 'and priorities, the establlshment of goals, and the evaluation of results. Stripped bare 

of planner's jargon, this approach is an effective aid in telling us what 

our crime problems are, what responses appear warranted, what kinds of 

performance and activities we can and should expect, and whether in fact des~r:d res~lts were achieved. (See Figure 1 for a gr~phic presentation of crlme-orlented program development.) 

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ANALYSIS OF CRIME 

AND DELINQUENCY 

EVALUATE 

and UPDATE 

REDUCE CAUSE IMPROVE 

CONTROL 

ATTACK 

UNDERLYING 

CONDITIONS 

TACTICAL 

ALTERNATIVES 

APPLY INTE.fiVENTION 

TECHNIQUES 

CONSTRAINTS 

REDUCE 

OPPORTUNITY 

SELECTEO PLAN 

• Legislation 

INCREASE 

RISK 

• Planning and Evaluation 

• Research and Information 

Systems 

• Prevention 

• Detection, Deterrence, 

and Apprehension 

• Diversion 

• Adjudication 

• Institutional Rehabilitation 

• Non-Institutional 

Rehabilitation 

Source: IMPACT 

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(LEAA brochure), 1973. J 

Figure'1. CRIME·ORIENTED PROGRAM DEVELOPMENT 

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( J 

INDEX 

A 

Administrative response time, 21 

Aggravated assaUlt, definition of, 26 

Allegheny County, Pa., use of Jussim 

by, 148-161 

AnalYSis, of reliability, 125-128 

Arrest information, 6, 35 

Arres ts, 28 

Assault, of Law Enforcement Officer, 28 

Attitudes, surveys of, 19 

B 

Bail decisions analysis, 88 

Behavior, data on, 10 

Bogue-Duncan composite method, 199 

Bomb information, 29 

Breaking or entering, definition of, 26 

Burglary, definition of, 26 

C 

Case status reports, 101 

Causa 1 i ty, 115 

Causal relationship, 116 

Census data, 166-167, 197, 21-6 

Chi square statistic, 117 

Ci rcular relationship, 119-120 

Citizen satisfaction, 15 

Clearance information, 34 

Clearances, 28 

Code book, 43-44 

Commercial targets, 20 

Computerized criminal history, 30, 72 

Computer mapping, 213-·216 

Confidence interval, 58, 62 

Contingency table, 77, 117 

Control variables, 118 

Cost estimates for victimization 

survey, 22 

Court delay, 103 Cost~, distribution of adult, 152-153 

Crime 

targets of, 20-21 

deterrable, 14 

incidence prediction of, 49-62 

planning measure of, 65-70 

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Crime analysis team, 225-232 

Crime-oriented goal setting, 236-237 

Crime-oriented pianning, 235-239 

Crime rates 

calculation of, 31-33 

estimation of, 13 

Crime report preparation, 39-43 

Crime seriousness measures, 68-70 

Crime statistics, analysis of, 39-45 

Crime target \ 

commercia1,20 

househoids, 20-21 

individual. 21 

samplings of, 18-20 

Crime volume, 45 

Criminal events, incidence of, 17 

Criminal index offenses, 26 

Criminal justice planning, data base 

for, 2-4 

Criminal justice system analysis of with offender based 

transaction statistics, 83-89 

evaluation of, 15 

fragmentation of, 83 

utility of data on, 6 

Cross tablulations, 108 

Data 

D 

behavi or, 10 

census. See Census 

coding of, 43-44 

environments, 2 

geography. See Geography 

limitations of uniform crime 

reports, 30-31 

offender based transaction 

statistics, 71-79 

popul ati on, 10 

types of management and administrative, 4 

types of modus operardi data, 4 

types of offender based transaction statistics, 4 

types of uniform crime reports, 4 
types of victimization survey data, 

5 

uti 1 ity of cdme data, 5-9 

Data ~rrors, types of~ 123~125 

Data preparation for c'(ime report, 43-45 

Data purification, 123-128 

Data reliability, 123-128 

Data sources for criminal justice 

planning, 2-3 

Defendent flow, 103 

De fendents, f1 ow of, 156 

Delinquency, planning measure of, 65-70 

Deterrab1e crimes, 14 

Disposition data, 29 

Disposition information, 36 

District of Columbia, use of PROMIS by, 

91 

Dual independent maps encoding file, 

167-175 

E 

Effectiveness of programs, 16 

Errors 

random, 124 

systemati c, 124 

types of, 123-125 

Estimate, reliability of, 119 

Estimation equations, 202 

Estimation of crime rates, 13 

Evaluation, use of offender based . transaction statistics, 78-79 

Experimental data, 115 

Exponential smoothing, 221 

F 

Factor analysis, 216 

Federal Bureau of Investigation, 36 

Fe 1 ony ca l,endars, 99 

Feedback model, 138-140 

Fi rst differences analysis, 221 

Flow diagram of criminal justice 

system, 149 

Forecasting 

tools for, 213-222 

with geocoded data, 216-221 

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G 

Geocoding, 213-216 

Geocoding applications, 108 

Geograph'y, da ta, 10, 165 .. 166 

Goal setting, crime oriented, 236-237 

H 

High impact anti-crime program, 235 

High-risk subgroups, 15 

Homicide, definition of, 26 

Household targets, 20-21 

Hypothesis testing, 118-119 

I 

Impact analysis, 132-138 

Index of urban maturation, 207-208 

Infer'ences, 115 

International Association of Chiefs 

of Pol ice, 25 

Interviews with crime victims, 14 

J 

Judicial decision making, 103 

Justice system interactive model, 

7, 129-245 

Juveniles, institutionalization of, 

155 

L 

Larceny, definition of, 26 

Law enforcement agencies, performance 

measures of, 87 

Law Enforcement Assistance Administration, National Criminal Justice 

Reference Service, 3 

Law enforcement employee data, 29 

Law enforcement officers 

assaulted, 28 

killed, 28 

Linear prediction, limitations of, 

57-60 

Location coded data, 216 

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Los Angeles police department data 

base, 67 

M 

Management and administrative data, 4, 

7-9 

Mapping, computer, 213-216 

Massachusetts, crime statistics analysis 

in, 39-45 

Misdemeanor calendars, 98-99 

Modus operandi data (MO data), 4 

Modus operandi files, 6, 67-68 

Motor vehicle theft, definition of, 26 

Multiple regression, 200, 221 

Multivariate statistics, 115 

N 

National Criminal Information Center, 72 

Nonexperimental data, 115 

Non-reporting, 14 

o 

Offender based transaction statistics, 

4, 6-7, 71-79 

analysis of criminal justice 

system with, 83-89 

Offenses, 28 

Offenses known, 34 

p 

Part II crimes, 48 

Partial relcitionship, 116 

Performance measurement, 238-2uO 

Personal identifers, 83-84 

P1 anni ng crime oriented, 235-239 

description of, 129-132 

Planning team, organization of, 225 -232 

Plea bargaining, 103 

Point estimation, 118-119 

Police foundation, 22 

Police-prosecutor t~e1atiorls, 103 

Population, data on, 10 

Population estimation methods for small 

areas, 197-212 

243 

Population size, adjustments of data 

for, 56 

Prediction 

adjustments of data for extraneous 

variables, 60-63 

adjustments of data for population, 56 

algebraic method for, 52-56 

linear prediction methods for, 

49-56 

of crime incidence, 49-62 

Pdnce Georges County, Md., use of 

Jussim in, 146-147 

Problem identification, 236 

Profiles, Law Enforcement Agency, 

36-37 

Program development, crime oriented, 

2uO 

Project SEARCH, 71-72 

Prosecution performance, 102-103 

Prosecutors management information 

system, 7, 91-111 

applications of, 94-95 Pro~ecutor's Office, management of, 

91-111 

Purification of data, 123-128 

Quantitative tools 

definition of, 

kinds of, 1-2 

R 

Range of tolerance, 51 

Rape, definition of, 26 

Ratio correlation method, 200-201 

Recidivism, definition of, 88 

Recruiting, crime analysis team, 

226-228 

Recurs;vity, 119-120 

Regression analysis, 117-118, 216 

Regression coefficient, 117 Regr~ssion line, 51 

Reliability analysis, 125-128 

Reliability of data, 123-128 

Reliability of estimate, 119 

Research and planning, use of uniform 

crime reports for, 47-48 

Resource utilization, 151 
Robbery, definition of, 26 

Robustness, 120 

s 

w 

Watts model cities area, 67 

Workload reports, 99-101 

Sanpl i ng, requi rements for, Y 

18-20 Sellin-Wolfgang Index, 66-68 Youthful offenders, 34 

Spectral analysis, 221 

St.andard metropo1 itan stati sti ca 1 areas, 

DIME files used by, 185-196 

Statistical techniques, 113-121 

choice of, 120 

Statistics 

univariate, 114, 115 

multivariate, 115 

St reet addr,es s, errors in, 124 

SymptomatiC variables, 205-206 

System flow rates, 85-86 

Surveys, attitudes of population, 19 

T 

Targets of crime, 20-21 

Testing, hypothesis, 118-119 

Time series analysis, 221 

Trial, speedy, 103 

U 

Uniform crime reporting, 5-6, 25-37 

use of in planning, 40 

Uniform crime reports, use of in 

research and planning, 47-48 

Univariate statistics, 114-115 

Urban maturation index, 207-208 

v 

Val.ue loss, 28 

Variance, spurious, 204-205 Victimiz~tion survey data, 3-5 

Victimization surveys ,13-23 

Visual estimation method~ 49-52 

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