CRIME ANALYSIS IN AMERICA* 

Prepared for 

U.S. Department of Justice
Office of Community Oriented Policing Services

Center for Public Policy
University of South Alabama

March 2002

* This project was supported by cooperative agreement #99-CK-WX-K002 by the Office of 
Community Oriented Policing Services, U.S. Department of Justice. Points of view or 
opinions contained in this document are those of the authors and do not necessarily represent 
the official position or policies of the U.S. Department of Justice. 


CRIME ANALYSIS IN AMERICA

Prepared for 

U.S. Department of Justice
Office of Community Oriented Policing Services

by 

Timothy C. OShea, Ph.D.
Principal Investigator

Keith Nicholls, Ph.D.
Co-Principal Investigator

With assistance from 

Jonathan Archer
Elton Hughes, MA
Jana Tatum

Center for Public Policy
University of South Alabama

March 2002


TABLE OF CONTENTS

I. Introduction .....................................................................1 

II. Crime Analysis in America: A Broad Overview .....................................9 
Introduction ........................................................................9 
Police Departments with more than 100 
Sworn Personnel ....................................................................10 
Method .............................................................................10 
Target and Response ................................................................10 
The Questionnaire ..................................................................11 
Findings ...........................................................................13 
Personnel ..........................................................................13 
Hardware/ Software Technology ......................................................16 
Training ...........................................................................19 
Data Quality and Sources ...........................................................20 
Statistical Methods ................................................................23 
Crime Analysis Functions 
And Targets ........................................................................25 
Explanatory Factors ................................................................28 
Methods and Variables ..............................................................28 
Findings and Conclusions ...........................................................34 
Police Departments with less than 100 
Sworn Personnel ....................................................................36 
Method .............................................................................36 
The Questionnaire ..................................................................36 
Findings ...........................................................................37 


Introduction .......................................................................37 
Personnel ..........................................................................38 
Hardware/ Software Technology ......................................................39 
Training ...........................................................................42 
Data............................................................................... 43 
Functions ..........................................................................45 
Targets ............................................................................45 
Explanatory Factors ................................................................48 
Methods and Variables ..............................................................48 
Finings and Conclusions ............................................................50 
Discussion .........................................................................51 

III. Crime Analysis in America: A Closer Look ......................................55 
Introduction .......................................................................55 
Organizational Dynamics and Behavior ...............................................57 
Division of Labor ..................................................................57 
Line and Staff .....................................................................61 
Centralization .....................................................................63 
Communication ......................................................................66 
Coordination .......................................................................70 
Performance Measures ...............................................................72 
Planning ...........................................................................74 
Human Resources ....................................................................76 
Job Description ....................................................................76 
Selection ..........................................................................77 


Career Path ........................................................................78 
Training ...........................................................................79 
Compensation .......................................................................81 
Turnover ...........................................................................82 
Operations .........................................................................83 
Background .........................................................................83 
Hardware Technology ................................................................84 
Software Technology ................................................................87 
Analytical Output: Tactical ........................................................90 
Crime and Incivility Conditions ....................................................93 
General Offender: Detain ...........................................................97 
General Offender: Information Only .................................................98 
Analytical Output: Strategic .......................................................99 
Nature and Extent of Crime .........................................................99 
Planning ..........................................................................100 
Resource Allocation ...............................................................102 
Policy Research ...................................................................103 

IV. Policy Implications ...........................................................106 
Introduction ......................................................................106 
Organization ......................................................................106 
Specialized Function ..............................................................106 
Line/ Staff .......................................................................108 
Centralization ....................................................................110 
Coordination ......................................................................111 


Performance Measures ..............................................................112 
Organizational Demand .............................................................114 
Personnel .........................................................................116 
Staffing ..........................................................................116 
Job Description ...................................................................117 
Training ..........................................................................118 
Selection .........................................................................120 
Career Path .......................................................................122 
Compensation ......................................................................123 
Operations ........................................................................125 
Hardware and Software Technology ..................................................125 
Output: Tactical ..................................................................127 
Output: Strategic .................................................................130 

References ........................................................................132 

Appendix A: Questionnaires and Frequencies ........................................134 

Appendix B: Job Descriptions ......................................................204 

Appendix C: Old Crime Analysis Forms (1970s) ......................................214 

Appendix D: Tactical Crime Analysis Reports: 
Crime and Incivility Conditions ...................................................273 

Appendix E: Tactical Crime Analysis Reports: 
General Offender Notices ..........................................................350 

Appendix F: Strategic Crime Analysis Reports ......................................360 

TO OBTAIN COPIES OF APPENDICES B-F PLEASE CONTACT THE COPS 
OFFICE DIRECTLY. THEY WERE TOO LARGE TO BE MADE AVAILABLE IN A 
DOWNLOADABLE FORMAT. 


I. INTRODUCTION 

Organizations must adapt to survive. Like all 
organizations, police departments must adjust their 
administrative arrangements to accommodate shifts in social, 
economic, and political conditions. Adjustments are ordinarily 
incremental; however, there are times when change is dramatic. 
At two points in the history of American policing, according to 
Kelling and Moore (1991), extraordinary change occurred; in 
both instances a dramatic and far-reaching transformation in 
police administration followed. The first shift came in the 
early 20th century, when the professional model appeared. The 
second can be traced to the mid-1980s, when the idea of 
community policing began to attract serious attention. 

Even the casual observer of police administration is 
struck by the remarkable diffusion of community policing. 
Kingdon (1984) maintained that there are times when ideas sweep 
through a policy community like fads. A strong consensus 
develops, often between former adversaries. Elected officials 
at all levels, police executives, and academics had all 
historically held quite different views about the role of the 
police in a democracy and about how the police should be 
organized. Yet, when it came to community policing, distrust 
seemed to have slowly given way to collaboration. The idea 
held enough power to produce a striking consensus among 


policymakers who shape American policing. One area of police 
operations that has been particularly affected by the shift to 
community policing is information processing. 

Community policing does not break from professional 
policing because it demands information; obviously, both models 
rely on the acquisition and analysis of information to make 
informed decisions. Community policing can be distinguished 
from professional policing because it calls for information 
from domains that had previously been either ignored or 
neglected. New or expanded information domains are a byproduct 
of fundamental philosophical differences between professional 
and community policing. 

Reducing corruption is an important aim of professional 
policing. Reformers at the turn of the century believed that 
to do so it was necessary to separate the administration of 
police from politics. Under the professional model, the police 
distance themselves from corrupting influences, especially 
elected officials, and to a lesser degree the general public 
(just the facts, maam). The police system is for all 
practical purposes closed. Community policing takes a contrary 
position: democratic principles demand that the police actively 
formulate policy that reflects the interests of external 
stakeholders (e.g., elected officials, citizens, agencies 
related to the police mission, etc.). Aggregating these 
interests and translating them into police policy places 


greater demands on the information processing operations of the 
department. 

Under professional policing, vertical interactions between 
hierarchical levels of the organization are also strictly 
limited. Communication is primarily one direction. 
Departmental policy is formed at the top and is filtered 
downward in the form of standard operating procedures. 
Policymaking power is not shared, it is centralized at the top. 
Here too, the community policing model calls for an open 
system: all members of the organization at all levels 
participate in the policymaking process. Community policing 
operations that follow from these philosophical shifts (e.g., 
participatory management, customer and officer surveys, 
performance measures, community partnerships, strategic 
planning, etc.) require substantially more data from 
substantially more sources. 

Finally, when it comes to day-to-day field operations, the 
professional model concentrates on tactics that are intended to 
maximize the police response to crime. Police react to 911 
calls and their performance is measured by response times, 
cases cleared, and arrests. Crime control is the overriding 
goal of the police service under the professional model. 
Community policing, on the other hand, places a high value on 
proactive policing. Officers are encouraged to systematically 
scan the police data, analyze it to discover problems and their 


causes, design responses, and assess the responses. The 
ultimate goal is to go beyond crime control and deliver a broad 
range of services that improve the communitys general quality 
of life. Here, again, the demand for data is substantially 
increased. 

Police executives are acutely aware of the additional 
information demands created by community oriented policing. 
Fortunately, support has come in several forms. Federal 
authorities have actively supported community policing, 
especially when it comes to information processing. For 
example, since the Office of Community Oriented Policing 
Services (COPS) was formed in 1994, information technology 
awards to state and local law enforcement agencies have 
exceeded one billion dollars (Apt Associates, 2000). Also, 
over the past fifteen years, computer hardware and software 
technology has improved considerably. Systems currently are 
less expensive to purchase and operate, their operation 
requires less technical skill, the data are more accessible, 
and the output is more intuitive (e.g., crime maps). The 
combination of money and improved technology has vastly 
improved the information processing capacity of police 
departments. 

Police operations generally associated with information 
processing (e.g., crime counts and trends, pattern and series 
identification, manpower allocation, beat configuration, 


intelligence, crime mapping, etc.) are customarily referred to 
as crime analysis. Police crime analysis operations consist of 
three essential functions: (1) to assess the nature, extent, 
and distribution of crime in order to efficiently and 
effectively allocate resources and deploy personnel; (2) to 
identify crime-suspect correlations to assist investigations; 
and (3) to identify the conditions that facilitate crime and 
incivility so that policymakers may make informed decisions 
about prevention approaches (Reuland, 1997). 

The tasks that we now associate with crime analysis have 
been an integral part of policing since the first municipal 
police force was formed in the early 19th century. Sir Robert 
Peel, the person who many regard as the architect of modern 
municipal policing, proposed a set of essential elements of 
policing. One of Peels principles called for the police to 
keep records, both to inform the public of crime conditions and 
to direct the allocation of police resources (Gaines, Kappeler 
and Vaughn, 1999). Since Peels time the technology and the 
information demands have changed; however, the essence of 
records management remains the same: collect, collate, analyze, 
and disseminate data to facilitate informed decision making. 

Few researchers have concentrated exclusively on the crime 
analysis function. There is, however, some literature devoted 
to the subject. Several works describe the basic operations of 
police crime analysis in a how-to manual format (Reuland, 1997; 


Gottlieb, Arenson and Singh, 1994; Peterson, 1994, Austin et 
al., 1973, McCualey, 1975). A few researchers have examined 
the mechanics of crime pattern recognition, a task that some 
suggest is the heart of crime analysis (Goldsmith et al., 2000; 
OShea, 1998). Crime mapping has developed into a major tool 
for crime analysis and has been studied and reported 
extensively (Block, Dabdoub and Fregly, 1995; Harries, 1999; 
Weisburd and McEwen, 1998). Some work, albeit very little, has 
been done to critically assess the quality of analytic output 
(Spelman, 1988). A small section of the Law Enforcement 
Management and Administrative Statistics (LEMAS) report 
addresses a handful of crime analysis issues (Reaves and Hart, 
2000). National-level data are currently not available to 
describe a broad spectrum of personnel, technological, 
training, analytic, data, functions, and product utilization 
aspects of crime analysis operations. We report here the 
findings of a recent study that describes key aspects of the 
crime analysis function, and provides a baseline that may be 
used to measure trends over time. 

The remainder of the report is divided into three 
sections. The second section reports the findings of two 
national surveys. The first survey was mailed to all law 
enforcement agencies in the United States with 100 or more 
sworn personnel. Nearly 65% of the 859 agencies responded to 
the survey. There were 95 items on the survey that addressed 


six categories of crime analysis. In the second survey we drew 
a random stratified (by size and region) sample of 800 law 
enforcement agencies from a sampling frame of just over 17,000 
law enforcement agencies with less than 100 sworn personnel. 
The survey was administered via telephone. The categories of 
questions were similar to those asked in the mail survey; 
however, because of resource limitations there were fewer 
questions. We report the findings of the surveys and examine 
factors that help to explain the variance in the quality of 
crime analysis in these two populations. The third section of 
the report takes an in-depth look at crime analysis operations 
across the country in the larger departments. Data were drawn 
from telephone interviews with crime analysts and site visits 
to nine crime analysis units that demonstrated high levels of 
crime analysis. The final section of the report attempts to 
explore the policy implications that the findings suggest. 

We believe that much of what we found will come as no 
surprise to experienced law enforcement personnel. Those of us 
who have worked in this field for any length of time will find 
that the results reported here support many long-held beliefs 
about the general state of crime analysis in American law 
enforcement agencies. One may, from time to time, find the 
results surprising and counter-intuitive. It was not our 
intent to expose aspects of policing that have been concealed; 
nor was it our intent to extol the wonders of modern crime 


analysis. The overall goal of this project was to 
systematically and rigorously explore crime analysis operations 
in American law enforcement. We sincerely hope that the reader 
will walk away from this report more informed about the state 
of crime analysis in America. For law enforcement 
policymakers, we would hope that the findings that we report 
here will serve to inform decisions that affect crime analysis 
operations. 


II. CRIME ANALYSIS IN AMERICA: A BROAD OVERVIEW 

INTRODUCTION 

In exploring a complex operational phenomenon like crime 
analysis, it is useful to begin by developing a broad 
understanding of its various relevant aspects. This is the 
logical starting point. One must first acquire a sense of the 
subjects general characteristics and the patterns that those 
characteristics form. In the process we begin to confront many 
of the intuitions and anecdotal evidence that form our current 
understanding about the subject. A panoramic view also 
provides a sound foundation upon which to frame the questions 
necessary to open a deeper, more insightful inquiry. We chose 
to administer a survey to law enforcement agencies to satisfy 
this first step in the process. 

Only about 5% of all law enforcement agencies (state, 
county, municipal, and special purpose) have more than 100 
sworn personnel; yet nearly 50% of all American law enforcement 
officers are employed by those departments. In other words, 
more than 95% of all American law enforcement agencies can be 
considered small. Few would disagree that there is a 
difference between big and small departments. That would seem 
uncontroversial. To avoid lumping together targets of our 
inquiry that are different, we separate big from small 
departments and study them individually. The more 


controversial issue is the criteria that we use to make the big 
and small distinction. This is somewhat arbitrary. We follow 
the Bureau of Justice Statistics classification system that 
uses 100 sworn personnel as the break point for its LEMAS 
report. This section reports on the findings of two surveys, 
one of departments with 100 or more sworn personnel, and the 
other of departments with fewer than 100 or fewer sworn 
personnel. 

POLICE DEPARTMENTS WITH MORE THAN 100 SWORN PERSONNEL 
Method 

Target and Response 

The data used in this analysis are derived from a national 
survey of law enforcement agencies. Questionnaires were mailed 
to all municipal police, sheriffs offices, and state law 
enforcement agencies with 100 or more sworn personnel. Two 
cover letters accompanied the questionnaire and were sent to 
the agencies chief executive. One letter was drafted by the 
sponsor, COPS, and the second letter was drafted by University 
of South Alabama (USA) researchers. Both letters assured the 
participants of confidentiality and encouraged participation. 
The USA letter described the survey process, the expected 
deliverables, and the person to whom the questionnaire should 
be directed (someone knowledgeable about crime analysis 
operations). 


There are 859 law enforcement agencies that fit the target 
criteria. The first mailing, to all 859 agencies, was sent 
April 2000. One follow-up mailing was sent to nonrespondents 
July 2000. Overall, 544 (63%) completed surveys were received. 
There were no systematic differences between respondents and 
nonrespondents according to state, size of department, and type 
of department. 

The Questionnaire 

The questionnaire consisted of 95 items. Items were coded 
in two ways: First, we coded simple presence or absence of 
some feature of crime analysis (e.g., do you have a CAD system? 
yes/no). Second, items were coded for scaled responses (e.g., 
How would you rate crime analysis training? None offered, poor, 
only fair, good, excellent). In all, there were six categories 
of questions. 

The first category of questions, personnel, consisted of a 
single item that simply asked how many people in the department 
are assigned to crime analysis as their primary responsibility. 
This question was recoded to reflect the number of crime 
analysts per 100 sworn employees. 

The second category of questions, hardware/software, 
consisted of twenty-six items. Questions examined automation, 
inter-agency data sharing, and software utilization. 
Respondents were asked which reports they entered into 


automated systems, what sorts of formal data sharing 
arrangements they participated in, and what types of 
specialized software programs they used. 

The third category, training, consisted of eight items 
that measured the quality of training. Questions addressed the 
following areas of training: hardware and software, data 
processing, statistical analysis, crime analysis, geographical 
information systems, graphics, and report writing. 

The fourth questionnaire category, data, examined the 
quality, sources, and utilization of data collected by the 
department. It consisted of twenty-three items. 

The fifth category, statistics, consisted of seven items 
that examined the statistical methods that crime analysts use 
to uncover patterns and relationships in the data. Respondents 
were asked to comment on the extent to which they used the 
following statistical methods: frequencies; mean, median, mode; 
standard deviation; crosstabs; correlation; regression; and 
cluster analysis (e.g., hierarchical and K-Means). 

The final category of questions, functions/targets 
consisted of thirty items. The questions examined the types of 
operations crime analysts engage in (e.g., link analysis, 
workload distribution, etc.) and the extent to which different 
potential targets use the crime analysis product (e.g., 
command-level managers, patrol officers, etc.). 


Findings 

Personnel 

We asked departments to tell us how many people in their 
organization were assigned primarily to conduct crime analysis 
operations. Nearly 3 out of 4 (74%) departments had at least 
one member whose primary duties were crime analysis. In order 
to compare different size departments, we standardized the 
variable to the number of crime analysts per 100 sworn 
officers. If departments are excluded that have no people 
assigned primarily to the crime analysis function (139), then 
the average number of crime analysts per 100 sworn personnel is 
just under 1 (.92).1 The distribution of crime analysts per 100 
sworn officers is skewed to the right. This is due to an 
unusually high number of crime analysts per 100 sworn personnel 
in one department (10.65). If not for this one outlier, the 
distribution would be reasonably normal (skewness=1.71). 
Removing this outlier would not, however, substantially change 
the average number of crime analysts per 100 sworn personnel 
(down from .92 to .89). 

1 We do not mean to suggest that these 139 departments do not engage 
in crime analysis activities. That is simply not so. Departments 
have engaged in crime analysis long before the position of crime 
analyst appeared. It goes without saying that every member of a 
police department, in some form, at some point, engages in crime 
analysis. In this question, however, we are exploring the structural 
aspect of specialization. Our concern is only with those departments 
that have opted to formally specialize the crime analysis function.

Civilianization of police services is a controversial 
issue that has touched the crime analysis function. Many 
 


departments, over the past ten to fifteen years, have hired 
civilian crime analysts. There are two compelling reasons for 
this trend: (1) economic: civilians tend to receive lower 
salaries than sworn personnel; and (2) organizational: crime 
analysis units tend to be more stable with civilian workers, 
since sworn members are more likely to move out of the unit due 
to promotion or reassignment. As shown in table 1, of the 409 
departments that said they have personnel primarily assigned to 
crime analysis functions, nearly 8 out of 10 (78%) had at least 
one civilian crime analyst. 

Table 1: Structure 
				N 		Yes	 	No 
If you assign personnel 
exclusively to crime analysis 
are any civilians? 
				409 		78% 		12% 

				N 		Separate Unit 	Other 
How are these people whose 
primary jobs is crime analysis 
organized in your department? 
				382 		72% 		28% 

				N 	Administration 	Patrol 	Detective 	Other 
If crime analysis is a separate 
unit where on the organizational 
chart does it fall. 
				336 		44% 	8% 	27% 		21% 

Administrators have disagreed about whether crime analysis 
should be a separate function (specialization), and where in 
the organization crime analysis should be placed 
(centralization). There are sound arguments on both sides of 
these issues. Many argue that crime analysis is a legitimate 
specialization and thus should have a separate unit. Others 
argue that crime analysis is simply part of the suite of tasks 


that police officers perform and therefore it warrants no 
specialized designation. Some argue for a centralized crime 
analysis unit. Those who support this approach insist that 
decentralized crime analysis invariably thwarts the mission of 
crime analysis. When crime analysts are assigned to field unit 
commands, they are inundated with trivial requests (e.g., 
making a computer banner for the retirement party). Others 
suggest that crime analysis should be decentralized. The needs 
of different functional units differ, the argument goes, and a 
decentralized structure facilitates varied crime analysis 
activity that is responsive to individual unit needs. Table 1 
addresses the police response to these issues. 

More than seven out of 10 (72%) departments said they have 
separate crime analysis units. When a specialized unit does 
exist, in about half (44%) of the departments it falls under 
administration (centralized), and about one-quarter (27%) of 
the units are under investigations (decentralized). In only 
about 1 out of 10 (8%) departments the crime analysis unit was 
located in the patrol division (decentralized). It would 
appear that crime analysis is considered a specialized function 
and is primarily centralized under the authority of 
administration. 


Hardware/Software Technology 

The quality of crime analysis is a function of how well a 
law enforcement agency can store and access data. Law 
enforcement has enjoyed an unprecedented opportunity to advance 
its information processing capacity as a result of cheaper and 
better hardware and software technology. Aspects of hardware 
and software technology worth considering are as follows: (1) 
the extent to which formal reports are entered into automated 
information systems; (2) the types of software used to support 
crime analysis; and (3) the degree to which police departments 
utilize their automated systems to facilitate the exchange of 
information, both intra- and inter-agency. Table 2 
illustrates the range of reports that police departments 
computerize. When it comes to automated data bases, nearly 9 
out of 10 agencies that responded use automated systems to 
store primary data (i.e., initial case report, calls for 
service, and the arrest report). Nearly three-quarters of the 
departments computerize data that is arguably less critical 
(i.e., investigative, traffic, and evidence). This is not 
especially surprising; we would expect most departments to 
store these reports in computers. Other types of reports, 
however, are less likely to turn up in automated systems. 
Field interviews, pawn shop, nickname, modus operandi, 
intelligence, and vice data are entered into computers by 
nearly half of the respondents. While these data are 


unquestionably useful for analytic purposes, they are data that 
historically have not been systematically collected, much less 
stored in a computer. Automating this set of data implies an 
impressive breadth of automation. 

Table 2 : Reports that Department Computerizes 

Which of the following 
reports and information are 	N 	Yes 	No 
entered into a computerized 
information system? 

Initial Case 			529 	94% 	6% 
Arrest 				532 	90% 	10% 
Calls For Service 		528 	87% 	13% 
Investigative 			525 	75% 	25% 
Evidence 			524 	73% 	27% 
Traffic Accident 		531 	72% 	18% 
Nickname 			528 	65% 	35% 
Field Interview 		532 	65% 	35% 
Pawn Shop 			511 	54% 	46% 
Vice Case 			508 	50% 	50% 
Intelligence 			513 	42% 	58% 
MO Files 			505 	47% 	53% 
Does your department have a 
paperless information system, 
totally online? 		531 	14% 	86% 
Does your department have a 
formal records management 
system? 			542 	91% 	9% 

Table 2 also provides an indication of the overall 
sophistication of automated systems. A formal records 
management system suggests that rational thought went into the 
planning, design, and execution of the overall data processing 
operations of the department. A paperless information system, 
totally online (PISTOL) would suggest, given current 
technology, a full-scale automated system. More than 9 out of 
10 (91%) respondents said their department had a formal records 


management system; however, only about 1 out of 10 (86%) 
respondents said they have a totally paperless system. 

Table 3 summarizes software usage by the responding 
departments. Crime mapping and spreadsheet are the most 
commonly used software of the choices offered. Nearly 6 out of 
10 (65% and 60%, respectively) departments that responded use 
that type of software. Intelligence2 and operations analysis 
software is used far less, and sophisticated statistical 
software is used by only 2 out of 10 (20%) of the respondents.3 
This indicates that departments are not engaging in more 
sophisticated statistical-based methods of analysis. We will 
consider this in more detail when statistical methods and crime 
analysis functions are discussed below. 

2 A reviewer pointed out that intelligence analysis should be 
distinguished from crime analysis. We recognize that others may 
agree with this position. For purposes of this report we define 
crime analysis broadly to include intelligence analysis.
3 We recognize that using sophisticated statistical software (e.g., 
SPSS) does not necessarily permit one to conclude that sophisticated 
statistical analysis is being done by analysts. We seek to tease out 
that information through follow-up questions that will be discussed 
below. 


Table 3: Software 
Indicate which type of specialized software 
programs your department currently uses. 	N 	Yes 	No 
GIS (Mapping) 					544 	61% 	40% 
Crime Analysis 					544 	45% 	55% 
Intelligence 					544 	35% 	65% 
Operations Analysis 				544 	27% 	73% 
Does your department use a spreadsheet 
software for crime analysis (Excel, Lotus, 
Quattro Pro, etc.)? 				537 	65% 	35% 
Does your department use a statistical package 
for crime analysis (SPSS, SAS, etc.)? 		536 	20% 	80% 

Training 

The quality of crime analysis operations is closely 
connected to the quality of training. With the advances in 
hardware and software technology, police departments are 
capable of processing data more efficiently and effectively. 
More data and better technology, however, requires considerably 
more training for crime analysts than before: for data 
collection and entry, for data analysis, and for generating 
intuitively useful output. How and how well analysts transform 
data into useful strategic and tactical information is a 
function of how well they are trained. 

Table 4 depicts respondent opinions about the departments 
efforts to provide training in eight key areas. Overall, it is 


clear that the great majority of respondents feel that training 
is in the middle range (fair to good). Relatively few 
respondents rated training at the extremes, either excellent or 
poor. When it comes to training areas that appear to be 
neglected, three stand out: crime mapping, graphics, and 
statistics. 

Table 4: Training 
How would you rate 
your department's 
efforts when it comes 
to training in each 
area? 			N 	Excellent 	Good 	Only Fair 	Poor 	None Offered 
Crime Analysis 		526 	13% 		34% 	25% 		11% 	17% 
Computer Software 	530 	12% 		43% 	31% 		9% 	6% 
GIS (Mapping) 		524 	12% 		23% 	20% 		12% 	34% 
Report writing 		525 	11% 		47% 	21% 		6% 	15% 
Statistical Analysis 	529 	9% 		30% 	25% 		15% 	22% 
Computer Hardware 	528 	8% 		37% 	28% 		9% 	17% 
Processing Data 	529 	8% 		40% 	31% 		9% 	12% 
Graphics 		528 	6% 		23% 	23% 		14% 	34% 

Data Quality and Sources 

Equipment is purchased and personnel are hired and 
trained. Next, it is important to examine aspects of the data: 
how they are collected, how they are entered into computers and 
utilized; and from what sources they are derived. Table 5 
presents the results of this category of questions. 


Table 5: Data Quality, Sources and Utilization

Please indicate whether each of the following is a Major problem, Minor problem, 
or Not a problem when it comes to generating quality data. 
N
Major
Minor 
Not

Case report does not ask for enough relevant data. 
533 
12% 
44% 
44% 
Data values are not uniform (e.g., blue, blu, bl) 
535 
21%  
41%  
39%
Data entry is slow and not accessible in a timely manner 
533 
26% 
39% 
35% 
People who key the data in are careless 
532 
10% 
60% 
30% 
Officers that take the initial reports are careless 
531 
20% 
66% 
14% 

Please indicate the extent to which data from each of these sources are utilized 
for crime analysis. 
N 
Not Utilized 
Utilized Some 
Highly Utilized 

Corrections data for: 
Parole 
525 
57% 
33% 
11% 
Probation 
523 
60% 
34% 
7% 
Inmate release 
526 
61% 
30% 
9% 
Crime data from: 
Neighboring police 
529 
37% 
51% 
12% 
Federal law enforcement (FBI, DEA, ATF, etc.) 
523 
45% 
50% 
6% 
State police 
523 
48% 
45% 
8% 
Non-law enforcement 
Local city sources (building dept., school, etc.) 
528 
36% 
56% 
18% 
Local county sources (highways, tax assessor, etc.) 
525 
46% 
48% 
6% 
State sources (welfare, highways, etc.) 
525 
60% 
37% 
3% 
Federal sources (Social Sec., Veterans, etc.) 
525 
67% 
32% 
1% 
Miscellaneous sources 
State Dept. of Motor Vehicle 
527 
36% 
40% 
24% 
Courts (dispositions, pending cases, etc.) 
528 
39% 
51% 
10% 

Which of these three terms best describes your department's utilization of each 
of the following types of data: 
Count-We basically keep track of the number of occurrences 
Analyze-In addition to counting, we look for trends and relationships in the 
data 
Neither-We dont' utilize that type of data 
N
Count
Analyze
Neither
Crime totals 
530 
33% 
66% 
2% 
Arrest totals 
529 
59% 
38% 
2% 
Calls for service 
527 
40% 
56% 
5% 
Clearance rates 
527 
59% 
33% 
8% 
Traffic accidents 
572 
40% 
50% 
10% 
Citizen complaints 
516 
46% 
40% 
14% 

When it comes to data collection and entry, the findings 
reveal that in both areas about half of the responding 
departments find some problem. Overall, the biggest problem 
areas were as follows: officers who are careless in taking 
reports, data values that are not uniform (making data queries 
difficult) and slow turnaround time between data collection and 
data entry. At least two out of 10 (20%, 21%, and 26%, 
respectively) departments said these are major problem areas. 

If the community policing model demands information from 
more domains, then we should see police departments collecting 
data from outside sources. Table 5 shows that about half of 
the responding departments do use data from outside sources to 


some degree. Few highly utilize any of the sources identified. 
The other side of this coin, however, suggests that nearly half 
of the respondent departments do not utilize outside data. The 
question that these items raise is whether the data are not 
utilized because they have little strategic or tactical 
utility, whether the data are not utilized because departments 
lack the technological capacity, or whether the data are not 
utilized because departments lack the analytic skills to 
exploit the data sources. The data do not permit us to address 
that question. 

An examination of what police do with the data they 
collect raises a fundamental issue that surrounds crime 
analysis. Spelman (1988) and others criticize law enforcement 
information processing operations, arguing that too much 
attention is devoted to counting crime, rather than analyzing 
crime. Goldstein (1990) insists that the police must move past 
simply counting crime; rather, they must pursue the root causes 
of crime and incivility. This calls for a more thoughtful, 
systematic, and rigorous analysis of the data. Table 5 
summarizes the results of questions that examine this important 
area. 

Most departments utilize critical data (i.e., crime, 
arrests, calls for service, and clearance rates) to some 
degree. Only about 1 out of 20 departments completely ignore 
these data. The number of departments that ignore traffic and 


citizen complaints increases slightly (10% and 14%, 
respectively). The vast majority of departments do utilize the 
data they collect somewhat. It is more interesting, however, 
to ask how the data are utilized (i.e., do they count it or 
analyze it?). Crimes and calls for service are analyzed by 
about 6 out of 10 departments responding (66% and 56%, 
respectively). When it comes to arrests and clearance rates, 
less than 4 out of 10 (38% and 33%, respectively) departments 
analyze. A sizeable number of departments, even in critical 
areas like crime and calls for service, continue only to count 
data. 

Statistical Methods 

When it comes to the use of statistics, crime analysts 
have a range of methods at their disposal, from simple methods 
(e.g., frequencies) to relatively complex ones (e.g., 
regression and cluster analysis). Asking how often a 
departments crime analyst uses various statistical methods is 
straightforward. Interpreting the results is not quite as 
obvious. 

It is a mistake to conclude that departments that claim to 
use higher-level statistical methods very often are more 
advanced than departments that say they use those methods 
sometimes. Advanced crime analysis is not necessarily a 
function of how frequently one or another method is used; 


rather, it is a function of how well the product contributes to 
some objective performance standard (e.g., the number of cases 
cleared, problems solved, etc.). It is more useful to consider 
the extent to which respondent departments never use one or 
another statistical method. 

All of the methods we list should be in the crime analysts 
statistical tool bag. The frequency with which each tool is 
used may vary based on size of department, the nature and 
extent of crime, etc. Nevertheless, all of the tools are 
necessary at one time or another to perform the full range of 
crime analysis. If we are told that a carpenter works without 
a hammer, we might reasonably deduce that he/she is working at 
a suboptimal level. The same can be said of a crime analyst 
who never uses one or more of the basic statistical methods. 

Table 6 presents items that examine the use of statistical 
methods. Consider first frequencies. One would expect that 
most law enforcement agencies utilize frequencies. After all, 
as we noted above, law enforcement agencies have been 
criticized for their over-reliance on bean counting. More 
than 8 out of 10 (82%) departments use frequencies. The number 
of departments that use means, medians, and modes; correlation; 
and cluster analysis drops to about 6 out of 10 (64%, 58%, and 
60% respectively). Even fewer departments said they use 
higher-level statistical methods: standard deviation, 
crosstabs, and regression (49%, 40%, and 36% respectively). 


Thus, the findings show that better than one-half of the 
departments that responded do not use any statistical methods 
beyond the most basic ones (i.e., simple counting). 

Table 6: Statistical Methods 
Please indicate how often 
your department uses each 
of the following in crime 
analysis? 
N 
Never 
Sometimes 
Often 
Very Often 
Frequencies 
493 
18% 
28% 
30% 
24% 
Mean, Median, Mode 
486 
34% 
34% 
19% 
11% 
Cluster analysis 
484 
40% 
28% 
20% 
12% 
Correlation 
484 
42% 
37% 
17% 
4% 
Standard Deviation 
483 
51% 
32% 
12% 
5% 
Crosstabs 
480 
60% 
24% 
12% 
4% 
Regression 
482 
64% 
28% 
16% 
2% 

Crime Analysis Functions and Targets 

The types of activities that a crime analyst engages in 
can be broken into two temporally-based categories: strategic 
and tactical. Strategic crime analysis supports 
administrative, long-range planning. Tactical analysis 
supports short-term, day-to-day field operations. While this 
is not a perfect typology, it does help to organize the 
examination of crime analysis functions. It is also, by the 
way, the kind of distinction that many crime analysts use to 
describe the type of unit that their department has formed. 


Table 7 presents findings that reflect the level of activity 
that respondents devote to various crime analysis functions. 

Table 7: Crime Analysis Functions and Targets 
How often does your department undertake 
the following types of analysis? 
N Never Sometimes Often Very Often 
Target profile analysis 508 25% 44% 20% 11% 
Victim analysis 509 30% 53% 14% 4% 
Link analysis 499 34% 43% 18% 5% 
Temporal analysis 495 42% 27% 20% 12% 
Spatial analysis 492 36% 27% 20% 16% 
Financial analysis 505 38% 47% 12% 4% 
Flowcharting 506 34% 51% 13% 2% 
Program evaluation 506 35% 43% 18% 4% 
Case management 508 17% 37% 32% 15% 
Crime scene profiling 505 33% 50% 17% 4% 
Crime forecasting 508 31% 47% 16% 6% 
Crime trends 518 11% 37% 34% 18% 
Citizen surveys 509 35% 51% 10% 4% 
Victim surveys 503 48% 43% 6% 3% 
Employee surveys 505 49% 45% 5% 1% 
Environmental surveys 504 73% 24% 2% 1% 
Intelligence analysis 508 20% 50% 21% 9% 
Productivity analysis 510 34% 45% 15% 6% 
Civil litigation analysis 503 75% 22% 3% 1% 
Patrol strategy analysis 510 22% 46% 25% 7% 
Workload distribution 511 24% 46% 24% 6% 
Displacement/diffusion analysis 500 61% 31% 6% 1% 

To what degree are the results of your 
crime analysis efforts utilized by each 
of the following potential internal 
users?
N  Not Utilized  Utilized Some  Highly Utilized
Command level mgrs. 519  9%  54%  37% 
Middle mgrs. (Lieuts., up) 521 7% 59% 34% 
First-line mgrs. (Sgts.) 522 12% 61% 28% 
Detectives 521 10% 54% 34% 
Patrol Officers 518 16% 63% 21% 
Specialized unit officers 515 14% 57% 29% 
Traffic officers 508 35% 56% 9% 
Training unit 507 48% 48% 5% 

When we consider the activities that most departments 
engage in to some extent (i.e., either sometimes, often, or 
very often), they are primarily tactical, with the exception of 
a single strategic-based function. Target profile analysis 
(75%), case management (83%), patrol strategy analysis (78%), 
and workload distribution (76%) are all areas that involve 
decisions related directly to day-to-day field operations. 
Crime trend analysis (89%), while arguably having tactical 
implications, is primarily concerned with projecting the nature 
and extent of crime in the foreseeable future. This is more 


useful for long-range planning than it is for daily tactical 
operations. 

When we look at activities that somewhat fewer agencies 
engage in (6-7 out of 10), more strategic activities appear. 
Victim analysis (70%), link analysis (66%), spatial analysis 
(64%), flowcharting (66%), and crime scene profiling (67%) 
support tactical patrol and investigations operations. 
Financial analysis (62%), program evaluation (65%), crime 
forecasting (69%), citizen surveys (65%), and productivity 
analysis (66%) are all examples of strategic analysis. 

When it comes to the type of activities departments engage 
in least, they tend to be strategic. About 5 out of 10 
departments, or less, engage in victim, employee, or 
environmental surveys (53%, 51%, and 27%, respectively) and 
displacement/diffusion analysis (39%). 

Overall, the responding agencies engage in a wide variety 
of analytic activity: The top-level of activity is primarily 
tactical, the mid-level is mixed, and the lower level of 
activity is primarily strategic. 

Another way of determining the mix of strategic and 
tactical analysis is to examine by whom and to what degree the 
product is utilized. Table 7 describes the degree to which 
various internal targets use crime analysis product. In 8-9 
out of 10 police departments that responded, command level 
managers (91%), middle managers (93%), first line managers 


(88%), detectives (90%), patrol officers (84%), and specialized 
unit officers (86%) utilize crime analysis product either 
somewhat or highly. Products were used least by traffic and 
training. About 6 out of 10 departments said traffic officers 
(65%) use the product and about 5 out of 10 said training units 
(52%) used crime analysis product. When it comes to targets, 
it would appear that personnel at all levels of the hierarchy 
make use of crime analysis product, suggesting a mix of 
strategic and tactical demand. 

Explanatory Factors 

The response frequencies give us a general idea about 
various aspects of crime analysis operations in police 
departments with more than 100 sworn personnel. Even a cursory 
examination of the findings tells us what we already knew, but 
had no systematic evidence to support: Police departments, when 
it comes to crime analysis operations, are not all the same; 
they vary. This raises an obvious question: What factors 
explain the differences in crime analysis operations in police 
departments with more than 100 sworn personnel? We address 
that question in this section. 

Method and variables 

A crime analysis unit varies along several critical 
dimensions: 1) the quality of its hardware and software 


inventory; 2) the training of its personnel; 3) the quality, 
sources, and utilization of its data; 4) the degree of 
sophistication of its statistical methods; and, 5) the varied 
types of its operational methods. These dimensions of crime 
analysis conform to the major categories within which the 
survey questions were ordered. To determine if and to what 
degree the respondents varied along these dimensions of crime 
analysis, we constructed a summated index for each of the 
dimensions. Question choices were assigned numerical values. 
For example, one question asked the respondent to rate the 
training a department provided for crime mapping. Five options 
were offered, ranging from none offered to excellent. None 
offered was assigned 0 points, poor, 1 point, only fair, 2 
points, good, 3 points, and excellent was assigned 4 points. 
All question options were similarly assigned point scores. 
Points for all the questions within each dimension were summed. 
Using this method we were able to determine the relative scores 
of each respondent along each crime analysis dimension. We 
found that the departments that responded to the survey did 
vary along these dimensions. This raises the question, How can 
we explain the differences between departments? 

There are, of course, many possible factors that together 
explain the differences in the five index scores. The object 
of this section is to propose several factors that would appear 


to influence the quality of crime analysis. These explanatory 
factors (or independent variables) are as follows: 

Number of sworn personnel: One might reasonably expect 
that larger departments will have greater crime analysis 
capacity. The larger the department is, the greater the 
population, thus the greater the crime, and thus the greater 
the calls for service. Larger jurisdictions would be expected 
to have larger budgets and thus be able to afford a more 
sophisticated crime analysis operation. In all, the 
combination of need and available resources would predict that 
larger departments would have higher crime analysis scores. 
Please note that other variables mentioned here (i.e., 
population, crime, and calls for service) are highly correlated 
to number of sworn personnel. Because this is so, any one of 
the variables would predict the outcome to the same degree. As 
a result, we omit population, number of index crimes, and total 
number of calls for service as predictor variables. 

Crimes per 1000 population: The raw number of sworn 
personnel, or for that matter the raw number of crimes (highly 
correlated to number of sworn personnel), are only rough 
measures of police demand. A more accurate measure of the 
demand for police services, and thus the demand for crime 
analysis, is the number of crimes per 1000 citizens. Using 
this standardized measure, the size of a jurisdiction is 
rendered irrelevant. We are able to compare cities of any size 


and focus on the extent of crime. It is a far more accurate 
measure of crime and thus the demand for crime analysis. We 
would expect that the higher the crimes per 1000 people, the 
greater the demand for police services, thus the greater the 
demand for crime analysis. Crime analysis scores should be 
higher in cities with higher crimes per 1000 people. 

Per capita expenditures: The amount of resources a 
jurisdiction is willing to devote to the police service should 
be related to a law enforcement agencys capacity to conduct 
crime analysis operations. The total amount spent by a 
jurisdiction is less indicative of its commitment to the police 
service as is the ratio of expenditures for police service to 
its population. The greater the per capita expenditure devoted 
to police resources, the higher we would expect the dimension 
scores to be. 

Community policing: As we noted above, departments that 
adopt community policing, almost by definition, substantially 
increase the demand for information processing. The heart of 
the models political and administrative dimensions are driven 
by the need for efficient, effective data collection, 
collation, analysis, and dissemination. The Law Enforcement 
Management and Administrative Statistics (LEMAS) survey 
collects information about community policing operations in 
police departments over 100 sworn personnel. We merged that 
data with the mail survey data. Six questions in the LEMAS 


survey were used to create a community policing index in a 
fashion similar to those indices we created to measure various 
aspects of crime analysis. The degree to which a department 
engages in community policing activities should be related to 
the departments crime analysis functions. Specifically, the 
higher the index score for community policing, the higher we 
would expect the crime analysis dimension scores. 

Management demand: If police managers hold back crime 
analysts from producing the quality of output they feel they 
are capable of, then it is likely that the product will be 
substandard and thus the unit as a whole will suffer. Three 
questions on the mail survey tapped into this area. 
Respondents were asked if managers required crime analysts to 
produce a variety of superficial irrelevant products instead of 
more tactically useful ones. We created a summated index to 
measure this variable. One might expect that the greater the 
demand for quality analysis, the greater the dimension scores. 

Target appreciation: When a unit is valued, it is likely 
that the unit will flourish. Workers who feel that they are 
valued will be more productive. Also, management will be more 
likely to provide resources to a valued unit. We asked four 
questions about the degree to which command staff, middle 
managers, patrol officers, and detectives appreciated the crime 
analysis units work. The questions were used to form an index 
to measure target appreciation. We would expect that the more 


a unit was appreciated (i.e., the higher the appreciation index 
score), the higher the crime analysis dimension scores should 
be. 

Specialized crime analysts: Workers who specialize in a 
particular function should attain a higher proficiency at 
performing the task. We would expect that crime analysis 
capacity in departments that have specialized crime analyst 
positions should be greater than in those departments that have 
not specialized this function. Police departments that assign 
people to specialized crime analysis positions should have 
higher crime analysis index scores. 

Region: The quality of the crime analysis produced may be 
a function of the region (northeast, south, midwest, and west) 
of the country in which the department is located. We created 
three dummy variables (reference northeast) to measure the 
impact of this variable. 

Type of department: The type of department may have a 
bearing on the quality of crime analysis that it produces. 
Three types of law enforcement agencies responded to the mail 
survey: municipal, sheriff, and state. Two dummy variables 
(reference municipal) were created to measure the impact of 
this variable. 

Each of the five dimensions of crime analysis 
(hardware/software, training, data, analysis, and, operations) 
were regressed on the nine factors (# sworn, crimes/1000, per 


capita expenditures, community policing, management demand, 
appreciation, specialized crime analysts, region, and 
department type). 

Findings and conclusions 

Table 8 portrays the findings. They indicate the 
following: 

Table 8: OLS Standardized Coefficients (and Standard Errors) for Variables Predicting Crime Analysis Capacity: Departments 
with 100 or More Sworn Personnel 
Independent Variable -Training -Hardware/ Software -Statistical Method -Crime Analysis Functions -Data 
NSP .033 (.000) .083 (.000)* .039 (.000)*** .172 (.000)*** .010 (.000) 
C100K -.022 (.008) -.054 (.006) .030 (.009) .009 (.012) -.074 (.007) 
PCE .028 (.000) -.123 (.000)** -.022 (.000) .004 (.000) -.042 (.000) 
CP .139 (.237)** .103 (.166)* .026 (.152) .030 (.337) .201 (.056) 
MD .100 (.125)* .113 .112 (.082)* .142 (.194)** .218 (.109)*** 
TA .179 (.111)*** .80 (.078) .133 (.071)** .214 (.162)*** .259 (.094)*** 
SCA .232 (.927)*** .245 (.667)*** .331 (.602)*** .277 (1.33)*** .139 (.793)** 
Midwest (ref,ne) .026 (1.272) .129 (.917)* -.033 (.830) -.067 (1.87) .031 (1.07) 
South (ref,ne) .024 (1.18) .273 (.866)*** .064 (.788) .045 (1.72) .142 (.992)* 
West (ref,ne) -.015 (1.262) .294 (.907)*** .135 (.815)* -.002 (1.80) .115 (1.06) 
TYPE .069 (.926) .097 (.656)* .029 .062 (1.33) .042 (.796) 
R2 .17 .24 .24 .26 .23 
F 6.506*** 9.579*** 9.734*** 10.451*** 9.231*** 
N 369 348 352 339 354 
* p < .05 
** p < .01 
*** p < .001 

 Size of the department is a weak predictor of crime 
analysis capacity. It does have a significant effect on 
operations. The larger departments appear to engage in a 
wider variety of crime analysis operations than their 
smaller counterparts. 
 Crime does not matter. The amount of crime has no bearing 
on hardware/software, quality and utilization of data, 
training, statistical methods, and types of analytic 
operations engaged in. 
 The amount of resources that are devoted to police 
operations does impact the quality of hardware/software 


inventory; however, otherwise it has no effect on any 
other crime analysis dimension. 
 Whether or not a department has adopted community policing 
practices seems only minimally related to how well it 
analyzes crime. It does affect training and 
hardware/software inventories, but otherwise does not 
predict the quality of crime analysis. 
 When managers demand sophisticated levels of crime 
analysis output, the quality tends to follow along 
accordingly. This variable affected all dimensions of 
crime analysis. The higher the demand, the better the 
hardware/software, training, data, analysis, and breadth 
of methods employed. 
 The degree to which targets appreciate crime analysis 
output was also a strong predictor of the quality of crime 
analysis operations. In every category except 
hardware/software inventory, the greater the appreciation, 
the higher the levels of crime analysis.4 

4 We caution the reader that the opposite may also be true; that is, 
the higher the levels of crime analysis, the greater the 
appreciation. Our analysis cannot say which way these are related. 

 Crime analysis across all dimensions is superior in 
departments that designate a specialized crime analysis 
position. 
 The region of the country in which a department is located 
is a significant predictor of crime analysis operations in 


several dimensions. When it comes to hardware/software 
inventories, the west ranks first, followed by the south, 
followed by the midwest, with the northeast placing last. 
The west stands out ahead of the other regions (although 
not substantially so) when it comes to the use of 
sophisticated statistical methods. The south finished 
first when it comes to data issues. In all other areas 
there are no significant differences between regions. 

DEPARTMENTS WITH LESS THAN 100 SWORN PERSONNEL 
Method 

The Questionnaire 

The questionnaire consisted of 60 questions requiring 146 
responses.5 Most items were coded to determine simple presence 
or absence of some feature of crime analysis (e.g., do you have 
a CAD system? (yes/no). Several questions were coded for 
scaled responses (e.g., on a scale of 1 to 10, with 10 being 
the highest, how would you rate your crime analysis unit?). In 
all, there were six categories of questions. 

The first category of questions, personnel, simply asked 
if the department assigned anyone to crime analysis as a 
primary responsibility and the degree to which civilians were 
utilized in that capacity. 

5 We do not discuss the responses to all questions. For those 
interested in the complete survey instrument and the responses to all 
questions see Appendix A. 


The second category of items, hardware/software, examined 

automation, inter-agency data sharing, and software 
utilization. Respondents were asked if they used computers, 
what sort of data they entered into computers, what sorts of 
data sharing arrangements they participated in, whether they 
possessed a formal records management system, and general 
opinions about the quality of their hardware and software 
inventory. 

The third category, training, measured the extent of 
formal training in various crime analysis areas. 

The fourth category, data, examined how the collected data 
were utilized, either counted, analyzed, or neither. 

The fifth category of questions, functions, asked 
respondents what types of analytic operations the department 
performed (e.g., link analysis, workload distribution, surveys, 
etc.). 

The final category of questions, targets, examined the 
extent to which different potential targets used the crime 
analysis product. 

Findings 

Introduction 

Recall that big departments are distinguished from 
small departments by the number of sworn personnel. One 
hundred or more sworn personnel constitute a big department. 


When appropriate in this section, we will point out the 
differences, according to our findings, between the big and 
small departments. We also intend to point out differences 
within the small department group according to size. This 
may lead to a confused narrative when we begin to talk about 
the smaller (or bigger) of the small departments. We 
propose to avoid this potential confusion by referring to the 
departments with 100 or more sworn personnel as Type I, and the 
departments with less than 100 sworn members as Type II. This 
should make the discussion somewhat less difficult to follow. 

Personnel 

As table 9 shows, just over 2 out of 10 (23%) Type II 
departments surveyed had at least one person whose primary 
responsibility is crime analysis. Of those departments that 
have a crime analyst specialist, nearly 5 out of 10 (47%) of 
them had at least one civilian in the position. We find that 
size of the Type II departments has a significant effect on 
both the number of departments that have specialized crime 
analysts and that use civilians for the position. As Type II 
departments get bigger, the proportion of those that have 
specialized positions, as well as the number of departments 
that utilize civilians for the position, increases. For 
example, only about 1 in 10 (14%) departments with between 1-24 
sworn officers have a specialized crime analyst position, while 


in departments with between 75-99 sworn officers more than 4 
out of 10 (41%) have at least one person assigned primarily to 
crime analysis. 

Table 9: Personnel 
						Percent of personnel answering yes 
				N 	1-24 	25-49 	50-74 	75-99 	Total 
Is there anyone in your 
department whose primary job 
responsibility is crime 
analysis? 			777 	14% 	26% 	35% 	41% 	23%* 
Are any of the persons 
responsible for crime 
analysis civilian employees? 	176 	29% 	52% 	53% 	68% 	47%* 
* significant relationship (p < .05) between size and dependent 
variable 

Hardware/Software Technology 

As table 10 shows, regardless of size, nearly every Type 
II department has at least one computer. While this 
information is useful, it tells us nothing about how these 
machines are being used. Nearly all of the Type II departments 
in the sample, about 8 out of 10, reported having a formal 
records management system. This suggests that most Type II 
departments exercise a reasonable degree of rational planning 
in the design and construction of their information systems. 
The smallest departments surveyed were less likely to have a 
connection to the internet or e-mail; however, the difference 
is minimal. Even in the smallest Type II departments, more 
than eight out of 10 (81%) have internet connections and about 
6 out of 10 (62%) have e-mail. 


Table 10: Hardware/Software 
					Percent of personnel answering yes 
				N 	1-24 	25-49	50-74	75-99	Total 
Does your department have at 
least one computer? 		777 	95% 	99% 	100% 	100% 	97% 
Records Management System 
good or excellent? 		633 	80% 	84% 	80% 	76% 	80% 
Is software out of date? 	753 	37% 	37% 	35% 	29% 	36% 
Is hardware out of date? 	753 	29% 	24% 	15% 	12% 	23%* 
Does your department use a 
computer-aided dispatch 
system (CAD)? 			777 	44% 	66% 	88% 	92% 	61%* 
Does your department have at 
least one computer with 
access to the Internet? 	753 	81% 	93% 	99% 	97% 	88%* 
For how many employees does 
your department provide E-
mail accounts? 			753 	62% 	78% 	91% 	96% 	73%* 
* significant relationship (p < .05) between size and dependent 
variable 

The size of the department makes no difference when it 
comes to opinions about the quality of software. Nearly 4 out 
of 10 of the Type II departments surveyed said they felt that 
their software was out-of-date. There was less agreement 
across departments when hardware was considered. As the size 
of the Type II department increases, the hardware inventory 
would appear to improve. Only about one out of ten of the 
bigger Type II departments felt that their hardware was out-ofdate, 
while nearly three out of ten of the smaller Type II 
departments were dissatisfied with their hardware. 

We see in table 11 that like Type I departments, most of 
the Type II departments, about 9 out of 10, store primary data 


(initial case report, arrest, investigations, and calls for 
service) in computers. The depth of automated reports, like we 
found in the Type I departments, is impressive in these smaller 
departments. On average, around 6 out of 10 of the Type II 
departments surveyed said they enter secondary data (traffic, 
intelligence, field interviews, vice, evidence, and nickname) 
into computers. Larger Type II departments are more likely to 
enter these data, but the differences due to size are not 
substantial. 

Table 11: Computerized Data 
Percent of personnel answering yes 
N 	1-24 	25-49 	50-74 	75-99 	Total 
Which of the following 
reports and information are 
entered into the departments 
computer. 
Initial Case reports 692 93% 99% 97% 99% 95%* 
Arrests Reports 692 94% 96% 91% 99% 96% 
Traffic Accident Reports 692 66% 76% 78% 78% 71%* 
Intelligence Reports 692 59% 63% 65% 57% 61% 
Field Interview Reports 692 50% 60% 65% 62% 56%* 
Vice Case Reports 692 57% 69% 78% 81% 65%* 
Calls For Service 692 77% 90% 90% 95% 83%* 
Investigative Reports 692 90% 90% 91% 87% 90% 
Evidence Reports 692 76% 86% 92% 87% 82%* 
Pawn Shop File 692 20% 43% 52% 59% 35%* 
Nickname File 692 48% 61% 79% 81% 61%* 
MO Files 692 35% 56% 61% 57% 45%* 
Does your department 
participate in a formal 
regional information-sharing 
program? 
753 62% 10% 73% 73% 67%* 
Does your department use 
crime mapping software? 
753 9% 9% 23% 33% 15%* 
* significant relationship (p < .05) between size and dependent 
variable 

Table 11 shows that far fewer Type II departments use 


On average, about 2 out of 10 have mapping programs. In this 
case, significantly and substantially more of the larger Type 
II departments in the survey had mapping software: about 1 out 
of 10 of the smallest (49 or fewer members) departments 
compared with about 1 out of 3 of the larger ones (75-99 
members). Regardless of size, all the Type II departments 
surveyed that used mapping software geocoded data in 
essentially the same proportions. Most, about 9 out of 10, 
departments geocoded part I crimes. Nearly 7 out of 10 
geocoded part II, arrests, and calls for service. Only about 2 
out of 10 geocoded corrections data. Very few Type II 
departments, less than 1 out of 10, used hotspot software. 

Table 12: Crime Mapping 
Percent of personnel answering yes 
N 	1-24 	25-49 	50-74 	75-99 	Total 
Which of the following 
types of data does your 
department map? 
Part I Crimes 112 85% 75% 89% 97% 88% 
Part II Crimes 112 72% 63% 71% 83% 74% 
Arrests 112 69% 63% 54% 73% 65% 
Calls for Service 112 64% 75% 63% 80% 69% 
Corrections 112 13% 25% 17% 30% 20% 
Traffic 112 64% 75% 63% 63% 64% 
Do you use hotspot 
software such as STAC? 
112 3% 13% 6% 7% 5% 
* significant relationship (p < .05) between size and 
dependent variable 

Training 

Table 13 depicts training issues. Nearly half of the Type 
II departments surveyed provide crime analysis training to its 


members. There is little variance between Type II departments 
according to size. Nearly all of them, about 9 out of 10, 
provide report writing training. About 6 out of 10 of the 
departments surveyed deliver software training to it members; 
somewhat fewer, on average, about half, train members in 
hardware operations. Few of them train members in statistical 
methods, graphics, and geographical information systems. 

Table 13: Training 
Percent of personnel answering yes 
N 1-24 25-49 50-74 75-99 Total 
Does your 
department provide 
training in the 
following areas: 

Crime Analysis 777 47% 55% 61% 52% 51%* 
Report Writing 777 88% 93% 88% 81% 88% 
GIS Mapping 753 10% 22% 33% 36% 19%* 
Computer Hardware 753 39% 53% 65% 50% 47%* 
Computer Software 753 57% 75% 80% 73% 66%* 
Statistical 
Analysis 
753 24% 39% 45% 46% 33%* 
Graphics 753 15% 25% 32% 37% 22%* 
* significant relationship (p < .05) between size and dependent variable 

Data 

The items in this category were intended to measure the 
degree to which the Type II departments analyze the data that 
they collect. As we pointed out in the survey of Type I 
departments, some have criticized them for devoting too much 
energy to simply counting crime, rather than analyzing it. As 
table 14 shows, we found that counting does dominate crime 
analysis operations in the Type II departments, like we found 
with the Type I departments. 


Table 14: Data Utilization 
Count, Analyze, 
or not utilize 
following data 
N 
Percent of personnel answering yes
1-24 
Count 
Analyze 
Not Utilize  
Percent of personnel answering yes  
25-49 
Count 
Analyze 
Not Utilize 
Percent of personnel answering yes
50-74 
Count 
Analyze 
Not Utilize
Percent of personnel answering yes
75-99 
Count 
Analyze 
Not Utilize

Crime totals 777 62% 21% 16% 58% 36% 6% 57% 40% 3% 53% 44% 1% 
Arrest totals 777 70% 18% 11% 66% 30% 3% 70% 29% 1% 72% 24% 2% 
Clearance rates 777 59% 17% 23% 51% 35% 11% 62% 30% 7% 58% 34% 6% 
Calls for service 777 64% 22% 13% 56% 38% 6% 60% 40% 0% 48% 49% 2% 
Traffic accidents 777 59% 27% 13% 44% 47% 8% 48% 45% 6% 36% 53% 9% 
Citizen 
complaints 
777 57% 29% 14% 46% 46% 8% 41% 50% 7% 38% 56% 4% 

About 6 out of 10 of the Type II departments, regardless 
of size, count crime, arrests, clearance rates, calls for 
service, traffic accidents, and citizen complaints. When it 
comes to analyzing data, of the Type II departments surveyed, 
as the size category increased, the proportion that analyzed 
the various data increased from about 2 out of 10 in the 
departments with 1-24 members to about 4 out of 10 in the 
departments with 75-99 members. Very few of the larger 
departments (50-99 members) said that they do not utilize data 
(either count or analyze). More of the smallest Type II 
departments (1-24 members) said that they do not utilize data. 
For example, more than 2 out of 10 of these departments said 
that they do not utilize clearance rate data. Although we did 
not specifically ask respondents to describe their analytic 
operations, these data suggest that little sophisticated 
statistical analysis is performed. This finding is similar to 
what we found in the Type I departments. 


Functions 

In keeping with the survey of Type I departments, we 
distinguish crime analysis functions (e.g., target profiling, 
spatial analysis, surveys, etc.) according to whether they are 
primarily strategic or tactical. As table 15 illustrates, when 
it comes to the kind of functions that Type II departments do, 
we found the following: Type I departments engage in a wider 
range of functions. However, when we look at the mix between 
tactical and strategic functions, the Type II departments 
engage in a greater proportion of strategic functions than 
their Type I counterparts. Table 15 depicts these findings. 

Table 15: Functions 
Which of the following crime analysis 
functions does your department employ? 
Percent of personnel answering yes 
N 1-24 25-49 50-74 75-99 Total 
Citizen surveys 777 45% 58% 66% 71% 54%* 
Workload distribution 777 38% 59% 68% 68% 50%* 
Program evaluation 777 36% 56% 65% 67% 47%* 
Employee surveys 777 40% 49% 59% 57% 47%* 
Spatial 777 33% 63% 61% 67% 46%* 
Patrol Strategy 777 39% 60% 50% 53% 45%* 
Crime trends 777 33% 53% 63% 68% 45%* 
Temporal 777 31% 55% 55% 58% 42%* 
Productivity 777 35% 52% 52% 52% 42%* 
Victim surveys 777 32% 40% 46% 53% 38%* 
Intelligence 777 30% 47% 52% 44% 38%* 
Crime scene profiling 777 28% 40% 39% 42% 33%* 
Flowcharting 777 16% 28% 44% 46% 26%* 
Victim 777 20% 28% 30% 31% 24%* 
Link 777 13% 28% 22% 31% 19%* 
Crime forecasting 777 13% 23% 28% 32% 19%* 
Financial 777 13% 19% 24% 23% 17%* 
Target profile 777 8% 18% 21% 29% 14%* 
Displacement and diffusion 777 12% 17% 18% 17% 14% 
Civil litigation 777 9% 6% 14% 6% 9%* 
Environmental survey 777 6% 3% 11% 10% 7% 
* significant relationship (p < .05) between size and dependent variable 

These distinctions, however, evaporate when we break down 
Type II departments by size. The bigger of the Type II 
45 


departments are very similar to the Type I departments when it 
comes to range of functions and the mix of tactical and 
strategic functions. The significant and substantial 
differences emerge when we compare Type I departments with the 
Type II departments of 1-24 members. 

Targets 

The worldwide web is an efficient and effective way for 
police departments of any size to communicate with the public. 
As table 16 points out, when the larger (25 members or greater) 
of the Type II departments is distinguished from the smaller 
ones (less than 25 members), we find that over 6 out of 10 of 
the bigger ones have a formal web page. Only about 3 out of 10 
of the smaller Type II departments have one. But for those 
departments that do have a web page, more than 8 out of 10 
utilize it to facilitate information exchange between the 
department and the public. There is no significant difference 
based on size of Type II departments when it comes to access to 
crime maps. Of those Type II departments that produce crime 
maps, about 6 out of 10 allow the public to view them. 


Table 16: Targets 
Percent of personnel answering yes 
N 1-24 25-49 50-74 75-99 Total 
Department have an official web page 753 34% 59% 74% 72% 52%* 
Citizens allowed to view web 112 49% 63% 77% 67% 63%* 
Means for publishing communications via the web 342 82% 79% 80% 87% 82% 
Are crime analysis products provided to the 
following 
Community 777 40% 57% 58% 56% 47%* 
Prosecutors 777 49% 46% 54% 42% 49% 
Outside law enforcement 777 49% 48% 60% 60% 52%* 
Elected officials 777 57% 55% 65% 59% 58% 
* significant relationship (p < .05) between size and dependent variable 

Elected officials tend to be the dominant external target 
for crime analysis products. About 6 out of 10 of the Type II 
departments surveyed provide crime analysis output to elected 
officials. The size of the department makes no difference in 
this area. There is also no significant size difference 
between Type II departments when it comes to providing analytic 
output to prosecutors. Nearly 5 out of 10 departments surveyed 
said that prosecutors are targets of their crime analysis 
products. As the size of the Type II departments increases, 
the number of departments that said they provide output to the 
community and to outside law enforcement agencies tended to 
increase; however, the differences were not substantial. 
Overall, about 5 out of 10 Type II departments surveyed deliver 
product to these two targets. 


Explanatory Factors 

As we did with the Type I departments, we explore in this 
section factors that might help explain the variance in the 
quality of crime analysis operations in Type II departments. 

Method and variables 

We examined crime analysis in Type II departments along 
four primary dimensions: 1) the quality of hardware and 
software inventory; 2) the training of its personnel; 3) the 
quality, sources, and utilization of its data; and, 4) the 
varied types of operational methods. To determine the extent 
of variance between respondents we constructed four summated 
indices, like we did for the Type I departments (refer to the 
preceding section for a discussion of the method). We found 
that the respondents did vary along the four dimensions, 
raising the same question that we addressed in the previous 
section: How can we explain the differences between 
departments? The explanatory factors that we propose are as 
follows: 

Population: The size of the city should help to explain 
the quality of service delivery within its public 
organizations. Larger cities should be expected to have access 
to more resources, both human and fiscal. One would expect 
that the larger the city, the greater the crime analysis index 
scores. 


Crime: The amount of crime a jurisdiction suffers should 
be related to the nature and extent of operations that it 
engages in. Greater levels of crime should translate into a 
greater need for crime analysis operations. We would expect 
that as crime increases, crime analysis operations 
correspondingly increase to meet the need. 

Region: The quality of crime analysis produced may be a 
function of the region of the country (northeast, south, 
Midwest, and west) in which the department is located. We 
created three dummy variables (reference northeast) to measure 
the impact of this variable. 

Type of department: The type of department may have a 
bearing on the quality of crime analysis that it produces. Two 
types of law enforcement agencies were included in the phone 
survey: municipal and sheriff. One dummy variable (reference 
municipal) was created to measure the impact of this variable. 

Crime analyst specialization: Whether or not a department 
has designated a specialized crime analyst position should 
influence the departments crime analysis operations. Every 
law enforcement agency, regardless of whether or not they 
specialize the function, engage in crime analysis; however, 
those that designate a specialized crime analysis function will 
be likely to take a greater interest in crime analysis 
operations, and the resources dedicated to those operations, 
than a department without a specialized function. We would 


therefore expect that departments with a specialized crime 
analysis function will have higher index scores. 

Each of the four dimensions of crime analysis 
(hardware/software, training, data, and operations) was 
regressed on the four factors (population, crime, region, and 
type of department). 

Findings and conclusions 

Table 17 portrays the findings. They indicate the 
following: 

Table 17: OLS Standardized Coefficients (and Standard Errors) for Variables Predicting Crime 
Analysis Capacity: Departments with Fewer than 100 Sworn Personnel 
Independent Variable 
Hardware/ Software 
Training 
Crime Analysis Functions 
Data 
POP .259 (.059)*** .402 (.272)*** .359 (.156)*** .263 (.110)*** 
INDEX -.010 (.000) .085 (.000)** -.026 (.000) .001 (.000) 
Midwest (ref,ne) -.043 (.214) -.132 (.988)** -.008 (.568) -.152 (.401)** 
South (ref,ne) -.042 (.216) -.189 (.999)*** .027 (.575) -.121 (.406)** 
West (ref,ne) -.042 (.254) -.059 (1.175) -.025 (.676) -.043 (.477)* 
TYPE .148 (.179)*** .143 (.827)*** .184 (.476)*** .158 (.336)*** 
CAS .257 (.166)*** .202 (.765)*** .279 (.440)*** .165 (.311)*** 
R2 .17 .29 .22 .12 
F 19.438*** 38.972*** 28.465*** 14.454*** 
N 686 686 686 686 
* p < .05 
** p < .01 
*** p < .001 

 The population that the agency serves is a significant 
predictor of crime analysis operations. In all four 
dimensions we found that the larger the city, the higher 
the index scores. 
 The extent of crime only impacted hardware and software 
inventories. The higher the crime rates, the better the 
hardware and software. 
 Region was a significant predictor in both 
hardware/software inventory and the quality, source, and 


utilization of data. When it came to hardware/software, 
northeastern agencies were superior to agencies in the 
midwest, west, and the south, although the differences 
were not substantial. When it came to data quality, 
sources, and utilization, the northeast and west were 
about the same and significantly, albeit not 
substantially, better than the midwest and south; 
otherwise, region had no impact on crime analysis. 

 The type of agency was a significant predictor in all four 
dimensions. Municipal agencies consistently exhibited 
higher, although only moderately so, dimension scores than 
sheriffs departments. 
 Agencies that have crime analysis specialists have 
significantly higher scores across all four dimensions of 
crime analysis than their counterparts that do designate 
specialized crime analysts. 

Discussion 

When it comes to structure, in departments with over 100 
sworn personnel, we see a primarily specialized, centralized, 
and civilianized function. In these departments, when 
departments choose to designate a specialized crime analysis 
function, the ratio of analysts to sworn personnel is nearly 1 
crime analyst per 100 sworn officers. Hardware, software, and 
training support for crime analysis are generally good in big 


departments. In departments with less than 100 sworn 
personnel, training is provided by most departments in a wide 
range of crime anlaysis-related tasks. For the most part, data 
management is rationally planned in both big and small 
departments. The vast majority of collected data is stored in 
automated data bases across all sizes of departments. Most 
departments seek data from outside sources. In big 
departments, although crime analysis is normally centralized 
and under administrative authority, actual operations tend to 
be more tactical than administrative; nevertheless, the 
products are utilized by personnel at all levels, from the beat 
officer to command staff. 

When compared to crime analysis operations of, say, fifty 
years ago, we would expect dramatic advances in analytic 
methods and output. And in many respects this is so. There is 
no question that we store and access data more efficiently and 
effectively than we did fifty years ago. Analytic software is 
now available to greatly enhance the analysts ability to 
discover the myriad patterns and relationships in the police 
data set. Yet, with all the advances in technology, regardless 
of the size of the department, the findings here indicate that 
bean counting continues to dominate crime analysis 
operations. Granted, they are counted faster, and there are 
beans counted that were not counted fifty years ago, but the 


evidence is clear: counting is the primary method when it comes 
to working with police data. 

The reason that counting dominates analysis is unclear. 
The findings, however, point to some signs that warrant a 
closer look. Most big departments reported problems with data 
collection. Data that are inconsistently coded, or data that 
are not captured because the reports simply do not ask for the 
data, would substantially obstruct analysis. There is some 
evidence that training may be partially responsible for the 
emphasis on counting. Respondents in both big and small 
departments said overall that training in analytic-type 
software (GIS) and in basic statistics were deficient. 

It is important to look at issues like data collection, 
report configuration, and hardware and software inventories to 
assess crime analysis. But I would suggest that if we demand a 
more sophisticated utilization of the data that law enforcement 
collects, then it may be more productive to look at the demand 
side of the equation. Like it or not, policing in America 
conforms to the bureaucratic, hierarchical model. Policies are 
formed at the executive level of the organization and are 
communicated to subordinates in the form of direct orders and 
standard operating procedures. Crime analysts count crime in 
response to the demand established by executive policymakers. 
So, the interesting question is not why do crime analysts 
continue to essentially count crime, but why does the demand 


from police executives continue to emphasize crime counts? The 
findings we report here simply do not permit an answer to this 
very important question. 

We do feel obligated to point out the weaknesses of both 
the mail and phone surveys. First and most important, there is 
the problem of self-reporting bias. In most cases, one person 
completed the survey. We cannot know with certainty whether 
the respondent was entirely honest and objective in his/her 
responses. Respondents may have slanted their responses one 
way or the other based on some unknown motivation. Based on 
follow-up phone interviews with a random selection of 
respondents that we discuss below, we are reasonably confident 
that this bias was minimal. We feel a more important concern 
is that our findings measure conditions at one point in time. 
We cannot know how the crime analysis function has changed over 
time. We therefore offer these findings as a baseline from 
which changes in the crime analysis function can be measured in 
the future. 


III. CRIME ANALYSIS IN AMERICA: A CLOSER LOOK 

INTRODUCTION 

We now have some general sense of the state of crime 
analysis in America. The results of the mail and phone surveys 
helped to develop a broader understanding of various important 
aspects of crime analysis; however, in many ways, the survey 
raised as many questions as it answered. The aim of this 
section is to probe crime analysis operations in large American 
police departments a bit more deeply.

 A cursory review of the mail and phone survey results 
suggests that the quality of crime analysis operations varies 
between departments. Not all police departments are equal when 
it comes to crime analysis. We intend look more carefully at 
the departments that, by our calculations, excel in the area. 
By singling out the departments that by all indications are 
among the most advanced, we can feel more comfortable in 
drawing conclusions about the current state of the art. We may 
all have some rough idea about what a crime analysis unit 
should be. In this section we examine what it currently is, at 
its best. In the process, not only the strengths emerge, but 
the weaknesses also come into focus. 

We first had to create some method to rank mail survey 
respondents. To do so we constructed a single summated index. 
A subset of survey questions were selected and coded so that 
the higher values represented conditions favorable for crime 


analysis. In all, there were 110 questions selected for the 
index. The higher the sum is, the higher the level of crime 
analysis is. For a variety of reasons, this is a crude measure 
of a departments crime analysis competency, but for our 
purposes it does provide a rough, objective way to rank order 
the respondents. 

Our first objective, after ranking the respondents, was to 
find out more about their analytic methods. The mail survey 
appeared to indicate that analysts were counting crime more 
than they were analyzing crime. This in itself was 
interesting and informative; however, it is more useful and 
practical to determine what sort of activities the higher-level 
departments are conducting that constitutes more sophisticated 
analysis. To do this we identified the top 100 departments 
according to their rank on the summated index. Of these 
departments we looked for the ones that reported using the most 
sophisticated software and further reported engaging in higher 
levels and frequencies of the more sophisticated analytic 
operations. We then conducted telephone interviews with the 
crime analysts from these departments. In all, we interviewed 
thirty crime analysts in police departments of all sizes, 
types, and regions of the country. 

Our second objective was to delve more deeply into the 
details of the operations and management of a crime analysis 
unit. We drew a sample of nine departments from the top 


twenty-five departments on the summated index list. This 
selection was not random. Departments were selected based on a 
combination of factors: convenience, region, and reputation. 
Each of these sites was visited. In all of the departments 
that we selected, the analyst who completed the mail survey 
acted as the contact person. Members of each unit were 
interviewed, as well as other relevant members of the 
department that had an organizational connection to the crime 
analysis function. We should point out that we do not mean to 
imply that the departments selected represent the best crime 
analysis units in the country; however, we are confident that 
they are representative of the best crime analysis units in 
the country. 

In this section we summarize the findings of those phone 
interviews and site visits. 

ORGANIZATIONAL DYNAMICS AND BEHAVIOR 
Division of Labor 

Organizations increase efficiency when different types of 
work are distinguished and when the various types of work are 
assigned functional units with clearly defined jurisdictions. 
The overall goal is to militate against overlap and duplication 
of effort. We found from the mail survey that most departments 
(74%) reported having specialized crime analysts and of those 


nearly the same proportion (72%) said that they assigned their 
crime analysts to separate units. 

In every department that we visited the crime analyst 
position was formally differentiated from other specialized 
police tasks; in addition, in all the departments that we 
visited the unit was functionally differentiated from other 
units. In most departments, the specialization of the crime 
analysis function can be traced to a time when technical 
ability was necessary to extract data from the department, 
city, or county information system, and translate that stored 
data into useful strategic or tactical information. Command 
staff managers, to fulfill their obligations to report the 
nature and extent of crime in their jurisdiction, had to rely 
on someone with the technical knowledge to access and generate 
reports from data sources that demanded some measure of 
technical expertise. As police organizations moved from 
storing data on the police blotter to storing data in 
sophisticated automated systems, the need for a specialized 
function arose. 

Several of the departments we visited can trace the 
formation of a specialized crime analysis unit to the early 
1970s. These departments, like many others at that time, had 
to rely on city or county information systems to manage their 
data. Police departments have traditionally been somewhat 
territorial when it comes to their information. As such, there 


was a strong incentive to seek more control of access to data 
and the generation of reports. 

For some departments, the federal government provided a 
means for the police to secure tighter control over their data. 
The 1968 Omnibus Crime Control Act provided substantial funding 
for law enforcement agencies to develop formal planning 
operations. Many of the grants required the creation of crime 
analysis units to support planning functions. This is 
essentially what we found in three departments (i.e., 
specialized units were formed that were dedicated to taking 
control of the data and to generating reports for managers). 
As one of the crime analysts in a site we visited said: 

The chief wanted someone in the 
department to be able to get 
reports to him when he wanted 
them. Our technical people at 
the city were giving us a set of 
reports but the chief sometimes 
wanted things that it took too 
long to get from the city. 

Current hardware and software technology has substantially 
improved access to police data when compared to thirty years 
ago. Graphical user interfaces have reduced the need for 
programming skills. But there is another side to that coin. 
Software developers have been developing a wide range of 
software to support crime analysis. Consider the advances in 
crime mapping and the potential data sharing capabilities of 
the web. While overall the software and hardware may be more 


user friendly, it still takes some measure of technical 
knowledge to operate these new and advanced systems. Several 
departments that we visited have formed their crime analysis 
units within the past ten to fifteen years. The success of 
these units is directly related to the technical skills that 
the crime analysts either brought with them to the job (e.g., 
special academic credentials) or developed while they were with 
the units. Baltimore County, MD, illustrates this point. 
Through the efforts of that departments crime analysis 
supervisor, the unit has one of the most advanced mapping 
operations in the country. The skills that were required to 
build that unit were clearly outside of the skills typically 
associated with traditional police work. 

This is likely to continue into the future. Software and 
hardware developers are producing more advanced methods for 
police departments to store, share, analyze, and disseminate 
data. Taking full advantage of these advances are likely to 
require skills not commonly held by the typical police officer. 
Specialization and functional differentiation are not always a 
matter of efficiency, as much as they are a matter of 
necessity. Specializing the crime analysis function may be 
driven by the need to identify and concentrate scarce or 
previously unavailable specialized skills, not by the need to 
avoid duplication, at least for the time being. 


Line and Staff 

Notions about line and staff have changed somewhat over 
time; however, the essence of the distinction between the two 
types of functions remains the same. Line personnel perform 
tasks that directly impact the organizations primary mandate 
(e.g., write tickets, make arrests, etc.). Staff personnel, on 
the other hand, support and advise the activities of the line 
in an effort to facilitate the execution of their activities 
(e.g., law unit, planning, etc.). Crime analysis historically, 
that is prior to the early 1970s, was one of many tasks that 
line personnel performed. Beat officers and detectives 
searched for patterns and relationships in the data as a part 
of their regular routine. As the tools to store and retrieve 
the data grew more sophisticated and correspondingly more 
complex, crime analysis evolved into a specialized function. 
The crime analyst ideally used his/her technical expertise to 
more efficiently access larger data sets and discover a wider 
range of patterns and relationships. 

In a broad sense, the crime analysis personnel that we 
spoke with at the nine sites viewed their role as staff. The 
analysts that we spoke with saw a distinction between the 
skills that they possessed and the skills that sworn personnel 
possess. In their minds they performed different tasks and the 
tasks that the crime analysts performed were designed to 


facilitate the work of the sworn officer. As one crime analyst 
put it: 

Were here to support the sworn 
people. Sometimes its hard to 
get some of my people to accept 
that. Its hard not to want to 
be more involved in the 
investigations, but we have to 
know where our role stops and the 
detectives begins. We are 
supposed to make getting the 
information easier for the sworn 
people and try to put things 
together for them. Thats what 
we do. 

Although crime analysts view their overall role as staff, 
they are quick to offer the views on the preferred target of 
support. In most cases, the analysts that we spoke with 
distinguish between tactical and strategic analysis. Recall 
that tactical and strategic analysis is distinguished 
essentially by the time horizon: Tactical analysis seeks to 
support daily operations; strategic analysis seeks to support 
long-range planning. Analysts that we spoke with place a high 
value on supporting line operations; however, they somewhat 
narrowly define line operations as those associated with the 
work of beat officers and detectives. Activities that support 
the work of middle and executive level managers are viewed as 
less legitimate by the analysts that we spoke with. In a 
manner akin to the beat officer feeling that issuing traffic 
tickets is not real police work, the crime analyst appears to 
view activities that support long-range planning as not real 


crime analysis. The comments of one crime analyst point this 
out: 

I just hate the annual report. I 
know its something the chief 
wants and I do it, but it just 
seems like such a waste of time. 

How the unit views its role (i.e., staff or line) is 
influenced by its history and organizational placement. In 
most departments that we visited, the unit was situated in 
operations and had its closest links with patrol and 
investigations. One unit was situated under administration and 
this unit placed a higher value on support for administration. 
This unit has always been under the supervision of a civilian 
and that person has always reported directly to the chief. Of 
the three staff members that perform analytic tasks, two are 
assigned to administrative duties and one to tactical. Their 
output reflects this structural context. Much of their work is 
centered on activities that support long-range planning. That 
arrangement (i.e., support for strategies planning) is clearly 
the exception in the departments that we visited. 

Centralization 

Centralization is an issue that managers have had to 
address when designing the structural arrangements for a crime 
analysis unit. The substance of the issue was described in the 
previous section. Some argue that centralization is necessary 


to insure that the unit stays focused on the unit and 
organization mission; others argue that centralization inhibits 
the units flexibility to provide services to a diverse set of 
internal clienteles. 

We found both types of structural arrangements in the 
sites visited. We also found that centralization is not a 
settled issue among practicing crime analysts. One crime 
analyst in a department that had a decentralized arrangement 
said: 

Im not sure which 
[decentralization v. 
centralization] is better. Ours 
is decentralized. Its good 
because we get to know the 
officers and the commander 
better. But its not so good 
because were asked to do things 
that crime analysts probably 
shouldnt. The commander knows 
that we know how to operate the 
computer, so when anything comes 
up that can be done with the 
computer, he comes to us. Hes 
starting to do his reports [to 
the command staff] in powerpoint 
now that he knows I can do it for 
him. That takes time away from 
what I think Im supposed to do, 
but you cant say no. 

One department designed an arrangement that combined 
elements of both centralization and decentralization. Units 
were physically located in remote stations and had a loose 
working relationship within the substation chain of command; 
however, they were formally within a centralized chain of 


command with their supervisor located under investigations. 

The supervisor of this unit maintained: 

You cant have a decentralized 
unit. Analysts are being asked 
to do things that really arent 
crime analysis. But you dont 
want to loose the support of the 
station commanders. So the way 
we do it is if the station crime 
analyst is asked to do something 
that they think they shouldnt be 
doing they give me a call and I 
tell them not to do it and if the 
commander wants to know why, he 
can call me. That takes the 
crime analyst off the hook. Its 
worked for us. Weve been able 
to avoid getting into things that 
really waste our time. 

Regardless of structural arrangement, analysts in all 
sites complained that they were frequently being asked to 
perform tasks that have nothing, in their view, to do with 
crime analysis. Crime analyst familiarity with hardware and 
software technology works as much to their disadvantage as to 
their advantage. As the excerpts above point out, analysts are 
often asked to engage in activities that the analysts deem 
outside of their job description. According to one analyst: 

When they find out what the 
software can do you get, 
like, Hey, how about a 
banner: Lordy, Lordy, Sgt. 
so and so is forty. Its 
hard to say no, but Id like 
to. 

Structure can and has been used to overcome this problem. 

Yet the more important concern may be to establish, with some 


certainty, a more explicit crime analyst job description. We 
will address this issue in more detail below. 

Communication 

Formal and informal communication provides a useful 
conceptual distinction to explain organizational communication 
(Berlo, 1960). Formal communication is normally written, 
originates with some organizational authority, follows the 
chain of command, is directed at some specific individual/s, 
and is part of the process by which the organizations 
policies, mission, strategy, tactics, etc. are implemented. 
The primary advantage of formal communication is that it 
fosters accountability. The primary disadvantage of formal 
communication is that the organization can become swamped in 
documentation. Informal communication is normally verbal, may 
originate from various sources, may or may not be directed at 
some specific audience, may or may not follow normal 
communication channels, and it reflects a broader range of 
concerns than formal communication. Informal communication can 
overcome some of the disadvantages of formal communication. 
Most importantly, it tends to facilitate practical solutions to 
problems that are aggravated by the rigidity of formal 
bureaucratic arrangements. 

Effective communication is vital to the operations of a 
staff function. Crime analysis units must develop sound 


communication channels with their targets in order to carry out 
their mission. In all the sites that we visited, crime 
analysts, to varying degrees, complained of problems in this 
area. Information overload was especially evident in some of 
these departments. Some units produce a vast array of 
different types of bulletins, notices, summary reports, etc. 
Targets engage in a cost/benefit analysis when it comes to 
processing information. When the costs of processing the 
output exceed the expected benefits, the information will be 
ignored. Comments of one of the analysts interviewed are 
illustrative: 

We provide information to 
officers almost case-by-case. We 
do have some[forms] that are 
formatted, but there are just too 
many different types of requests. 
You try to get it all to the 
briefing room bulletin boards but 
it gets pretty cluttered. 

Much of the communication between crime analysts and their 
targets is informal. Most crime analysts we spoke with seemed 
to feel that this form of communication was most effective. 
All of the analysts believed that they had to sell 
themselves, and the unit, to targets. This adds to the ad hoc 
nature of the output. Part of the reason for this struggle 
seems to be connected to the civilianization of the crime 
analysis function. Nearly 8 out of 10 departments that had a 
crime analysis function (see above) had at least one civilian 


crime analyst. Seattle PDs unit was sworn and Lincoln PDs 
units first-line supervisor was a sworn member; in all the 
other departments the analysts and first-line supervisors were 
civilian. Several of the civilian analysts claimed that they 
have experienced some difficulties in gaining the trust of 
sworn members. As one civilian analyst put it: 

You have to work at selling 
yourself to the officers. You 
have to prove that what you do 
can help them. Once you do that 
theyre sold, but its not so 
easy sometimes. You have to work 
at it. Im always looking for 
ways to get them to see we can 
make their jobs easier. Most of 
the time, its up to me to make 
the first move. 

This is to be expected. The police culture literature 
(Crank, 1998) describes a belief system among police officers 
that creates resistance to anyone they consider an outsider. 
Sworn officers, particularly at the patrol level, tend to have 
a world-view that is distrustful of those outside of their 
particular functional responsibility: Patrol doesnt trust 
detectives; detectives dont trust the specialized units; sworn 
members at the street level dont trust their superiors, and so 
on. Civilians are truly considered outsiders and are held up 
to special scrutiny by all members. It is entirely 
understandable that civilians that enter this world would find 
it difficult to gain acceptance. To overcome this obstacle, 
informal communication patterns proliferate, especially between 


analysts and first-line officers; in their efforts to overcome 
target resistance, the ad hoc nature of communication is 
aggravated. 

It is important for crime analysts to build trust and 
credibility with targets. It is, likewise, important for crime 
analysts to be responsive to the information needs of a wide 
audience. To accomplish these ends, crime analysts will have 
to generate a wide range of output. But the crime analysts 
must insure that each product reaches the widest possible 
audience. The benefit of formatted reports is that it reduces 
processing time for the reader. The format allows the reader 
to quickly determine if the information is relevant to their 
needs. Unformatted, individualized output implies the 
diffusion of information and thus constrains effective 
communication between crime analysts and targets. Categorizing 
output and creating a manageable set of well-organized, 
formatted reports would improve communication between analysts 
and targets. 

We found that communication between functional units also 
tended to be informal. Most of the departments that we visited 
had some form of community policing, problem-oriented policing, 
crime prevention, or command accountability (COMSTAT) policies. 
Since each one of these policies is highly information-
dependent, one might reasonably assume that crime analysis 
would be formally linked with those functions (forms, staff 


meetings, etc.). We found that normally this was not so. 

Although some of the units interviewed did have informal links 
with these operations, formal communication mechanisms were for 
the most part absent. There were exceptions. Departments that 
had a command accountability policy formally integrated crime 
analysis output into the regular meetings. As a command staff 
officer in Lincoln, Nebraska, explained: 

Crime analysis provides all the 
information that we use to talk 
about at our meetings[command 
accountability]. Crime maps. 
Statistics. We put the maps up 
and pass around the statistics 
and talk about the problems that 
we find. Commanders tell us what 
they intend to do about them. 
The meetings are held once a 
month and commanders from all 
sections attend. The whole thing 
depends on the crime analysis 
people. 

Coordination 

Coordination is, for practical purposes, the opposite of 
division of labor. It is the process by which specialized or 
divided labor is brought together (Williams, 1980; Seidman, 
1986). It is the mechanism by which the organization seeks to 
keep its separate parts focused on the common mission. The 
more complex and specialized an organization becomes, the more 
it requires coordination. Coordination is closely akin to and 
dependent upon effective communication channels. Coordination 
is likely to meet with resistance. Those who seek to 


coordinate often advance interests that may be at odds with 
those that are the targets of coordination. 

Even though the creation of a crime analysis unit 
increases specialization, because it serves a staff function, 
it should facilitate coordination. This is what we found in 
those departments that built formal links between crime 
analysis and command accountability operations. These 
departments provided reports that facilitated links between 
various specialized units. Comments of several crime analysts 
illustrate how this worked: 

When it works, its a way to get 
different units working toward 
solving the problem. We found a 
robbery series. The way we 
described the series showed how 
other units had to get involved. 
It all began with our unit 
finding and describing the 
problem. 

Our meetings [command 
accountability] are actually 
discussions about what the crime 
analysis people put together for 
us. When we decide how different 
units can work together to solve 
a problem that crime analysis 
finds, its from what the crime 
analysis people tell us. 

Similar to what we found in communication channels, 
coordination with relevant units was loose and informal in most 
of the departments that we visited. There was little evidence 
of formal links with crime prevention, community policing, and 


problem-oriented policing when these operations were 
formalized. Crime analysts said that they work with these 
units much in the same way that they work with other sworn 
officers (i.e., on a primarily ad hoc basis). Whats more, it 
was very common, when we viewed the organizational charts, to 
find crime analysis located in functionally different sections 
of the hierarchy. This in some cases aggravated the 
difficulties in coordinating logically connected functions. 
According to one crime analyst: 

We work with the CPTED [crime 
prevention through environmental 
design] person, but there is no 
formal relationship. Theyre 
even under another division. I 
made it a point to make a 
connection. We should work more 
closely with CPTED. 

Crime Analysis Performance Measures 

According to Ammons (1995), performance measurement is 
essential to the following critical functions of an 
organization: accountability, budgeting, planning, operational 
improvement, evaluation, and, allocation of resources. It is 
necessary for a specialized unit, like crime analysis, to 
design a clearly defined set of objective, and ordinarily 
quantifiable, measures of performance. 

Performance measures should respond to a units overall 
mission, goals, and objectives, and ultimately should be driven 


by some larger organization mission or vision. We did not find 
formal unit-specific performance measures in the departments 
that we visited. Units did not have clearly and formally 
stated unit goals and objectives that could be easily 
translated into quantifiable, measurable indicators of 
performance. This should not be misconstrued to suggest some 
criticism of the work product of crime analysts; it does not. 
It simply suggests that we found that crime analysis units in 
our sample were not designing rational methods to assess the 
efficiency and effectiveness of their output. This should not 
come as any great surprise. Police departments have not been 
particularly successful at developing useful performance 
measures, nor for that matter, have they been particularly 
successful at adopting formal rational planning. One might 
reasonably expect that units within a police department would 
likewise be somewhat deficient in this area. 

It should be noted that some departments that we observed 
were making an effort to overcome this weakness. Some of the 
departments were either constructing or have constructed a unit 
mission statement and are looking at general unit goals and 
objectives. Several departments administer surveys to targets 
of their analytic output. These surveys, however, were only 
sporadically administered and were at best weak indicators of 
the demand for and the quality and usefulness of the units 
output. Analyst supervisors that we spoke with, for the most 


part, were aware of the need for evaluating their products and 
nearly all were wrestling with a solution. According to one 
analyst: 

We have a survey that we give 
officers. It is supposed to tell 
us how were doing, but it 
doesnt always get to them and it 
probably doesnt tell us enough. 
Were trying to put one together, 
but honestly its not high on the 
list of things to do. Were kind 
of busy here. 

Planning 

The Law Enforcement Assistance Administration (LEAA), a 
federal agency created by the Omnibus Crime Control and Safe 
Streets Act of 1968, awarded substantial funds to state, 
county, and municipal police departments to support rational 
planning (Hudzik and Cordner, 1985). In a phone survey 
recently conducted by the University of South Alabama Center 
for Public Policy, nearly one-third of a sample of police 
departments with 100 or more sworn personnel said that they 
engage in formal strategic planning. The planning process 
requires that an organization gather data that informs planners 
about both the external and internal environment. The crime 
analysis unit is a logical place for the collection, collation, 
analysis, and distribution of these data for planning purposes 
(e.g., nature, extent, and location of crime; crime trend 
predictions; citizen and employee surveys; etc.). We discussed 


crime analysis involvement in the formal planning process with 
the analysts that we interviewed. 

All of the departments that we visited do some form of 
strategic planning. Nearly all the analysts that we talked 
with provide reports to superiors that are in some fashion 
intended to support planning. Mostly, crime analysis units 
provide data about the nature and extent of crime in the 
departments jurisdiction. In a few departments, the analysts 
were asked to provide estimates of future trends. One 
department is analyzing data in a manner that seeks to predict 
the movement of crime over time in an effort to help planners 
estimate future manpower needs and distribution. However, 
crime analysts are not currently assisting planners in 
sophisticated analysis that one might associate with the needs 
of strategic planners (e.g., forecasting, stakeholder 
assessments, etc.). This is due in large part to the current 
emphasis on tactical analysis. Some of the departments that we 
visited designated members of the unit for administrative 
duties, which is a form of analysis that might be expected to 
support strategic planning. But the dominant view among 
analysts and police executives calls for an emphasis on 
tactical analysis. This form of analysis may be compatible 
with providing nature and extent of crime data to planners, but 
it is not the type of analysis that we would expect to find 


that would support the more sophisticated needs of strategic 
planning teams. 

HUMAN RESOURCES 
Job Description 

All but two of the departments that we visited had formal 
job descriptions (see appendix B). Job descriptions were 
normally found in the job announcements that were distributed 
to the public. One department had a job description in its 
unit manual and another department had its job description in a 
standard operating procedure. We also expanded our search by 
viewing job descriptions of other police departments that 
posted job announcements on their web sites. Of the job 
descriptions that we reviewed, they generally called for the 
following set of skill categories: hardware and software 
operation; data management and analysis; operations planning; 
and verbal and written communication. As a rule, the minimum 
education requirement qualification was an undergraduate 
degree, although several allowed experience to substitute for 
education. Judging from what we were able to determine, job 
descriptions for hiring purposes seem relatively similar 
between departments. 


Selection 

The selection process depends upon whether the analyst is 
a civilian or a sworn member of the department. For a sworn 
member, selection to a crime analysis unit is normally a 
temporary assignment and represents an opportunity for the 
officer (at whatever rank level) to broaden his/her police 
experience. When the unit is staff to command staff, this 
position can provide an opportunity for a sworn member to be 
noticed by superiors. As one sworn crime analyst said: 

Its a good assignment. It gives 
you some good experience in 
another unit and since we do a 
lot for the chief, it gives you 
the chance to show what you can 
do. 

For civilian crime analysts, the selection method varied 
across departments that we visited. As a rule, applicants were 
asked to complete a formal application form; the pool was 
reduced to some manageable number based on the application; 
oral interviews were conducted to a short-list of candidates; 
and a final decision was reached. In some cases, the 
department administered a written examination that covered 
basic analytic skills. The primary selection method appeared 
to be the oral interview. This provided the opportunity for 
interviewers to determine a candidates qualifications for the 
position. All departments conducted some form of background 
investigation; a few departments included a polygraph 


examination. While there were variations in the methods used 
to select personnel, it can be said that all the departments we 
visited did have some formalized selection process in place. 

When we asked about problems associated with the selection 
process, analysts most often mentioned was the long application 
processing time. One analysts comments illustrate this 
problem: 

It just takes too long to process 
an applicant. We have lost a 
couple good people because when 
we finally get around to offering 
them a job theyve found 
something else. 

Career Path 

Like the selection process, the career path for a crime 
analyst depends upon whether the position is civilian or sworn. 
For a sworn member, assignment to a crime analysis unit, like 
any assignment, should not affect advancement in the normal 
military-like rank structure. For a civilian, rank structure 
ordinarily is confined to whatever rank structure exists within 
the relatively small unit. In general, career path for a 
civilian analyst, both vertical and lateral, is extremely 
limited. The vertical path for civilians, in the departments 
that we visited, was limited to two and sometimes three tiers. 
Every civilian-staffed unit that we observed ultimately 
reported to a sworn police manager; this was the ceiling into 


which a civilian was confined. Except in departments where the 
civilian crime analyst position shared a job classification 
with other municipal departments, lateral movement (i.e., out 
of the crime analysis unit and into another specialization of 
the department) was not a career option. For example, in 
several California departments the crime analyst position was 
filled through the citys personnel department; when openings 
occurred in other city departments for this position, crime 
analysts were eligible to apply. 

Training 

The mail survey (see above) indicated that training was 
not a problem overall. Respondents reported that training was 
adequate. Analysts we interviewed during the site visits 
echoed this opinion. None of the departments offered a formal 
entry-level training for crime analysts. The selection process 
was normally intended to insure that new hires had the required 
basic skills. When it came to familiarizing new hires with the 
specific operations of the unit, training for all departments 
was on-the-job. On-the-job training is especially important 
for civilian hires. They must become familiar with the sort of 
analysis that has practical value. They must become familiar 
with the police culture if they expect to gain the confidence 
and trust of the sworn members. As one analyst put it: 

It was hard at first. Hell, 
its still hard. I knew how 


to operate the computer and 
software, I even knew 
something about the criminal 
justice system. But I 
didnt know anything, 
really, about what it was 
like to work for a police 
department or what a crime 
analyst did. 

Some departments took advantage of advanced training 
offered by the municipal or county personnel department. One 
analyst said: 

The department doesnt provide 
training at the academy for crime 
analysis. But city personnel has 
courses that we can use for our 
people, like word processing, 
spreadsheet and data base. That 
helps, since our budget isnt big 
enough for specialized training 
for crime analysts. 

Every department budgeted something for training. Most 
said that the amount was adequate, but they could use more, 
especially for advanced training in crime mapping. Some 
departments also took advantage of grant funding to provide in-
service training. Some departments viewed attendance at the 
International Association of Crime Analysts (IACA) as a good 
training vehicle. Many of the analysts that we interviewed 
were members of IACA and felt that the annual conference 
provided useful skill-building workshops and panels that 
improved their performance. 


Compensation 

This is another aspect of the crime analysis function in 
which the distinction between civilian and sworn employee is 
relevant. Historically, civilianization of police operations 
(e.g., station desk personnel, lock-up keepers, dispatchers, 
etc.) has been used as a means to cut costs: civilians can be 
hired in some positions for wages that are less (sometimes 
substantially so) than for sworn members. This is clearly the 
case when it comes to crime analysts. At the departments that 
we visited, civilian crime analysts earned substantially less 
than their sworn counterparts. For example, according to one 
civilian analyst who supervises a staff of eight: 

Im doing the same work as a 
lieutenant but Im making about 
$35,000 less[per year]. A 
starting analyst starts out for 
about $10,000[per year] less than 
a new police officer. 

This sort of salary disparity, to a greater or lesser 
degree, was similar at all the sites we visited. And it should 
be added that these disparities do not go unnoticed by the 
analysts that we interviewed. Other benefits that civilian 
employees enjoy (e.g., health insurance) would appear to be 
similar to their sworn officer counterparts, that is, except 
for pension benefits. Civilian members did not enjoy the same 
pension benefits, although most were offered some form of 
retirement security (e.g., deferred compensation, 401K, etc.). 


When it comes to a variety of compensation issues, sworn and 
civilian members are not treated alike. 

Turnover 

Turnover (i.e., the percentage of workers that separate 
from an organization over a year) is an important factor for 
managers to consider when assessing the general condition of an 
organization or unit. Some measure of turnover is to be 
expected and whats more is desirable. Fresh ideas prevent an 
organization or unit from becoming inflexible and unresponsive 
to its external environment. Turnover, however, can, at some 
point, become dysfunctional. Longer-tenured employees 
facilitate a process by which knowledge gained from experience 
is passed on to newcomers. Frequent turnover creates 
discontinuity and some measure of unpredictability in relations 
with other organizations or units, thereby adversely affecting 
coordination. High turnover, most importantly, implies job 
dissatisfaction, which is, in turn, related to productivity. 
All of the sites that we visited reported, to varying degrees, 
some problem with analyst turnover. It would appear that the 
trend toward civilianization and the manner in which civilians 
are integrated into policing may help to explain the condition. 

Crime analysis units are clearly a function that police 
managers have targeted for civilianization. Those who support 
civilians as crime analysts argue the following: Typically, 


sworn officers do not have the hardware and software training 
and background to perform the tasks associated with current 
crime analysis practices; civilians can be paid less than sworn 
members; turnover when analysts are sworn members is more 
frequent because of promotion and transfer; and, the number of 
sworn officers that perform primary patrol and investigative 
functions can be increased when crime analysis is performed by 
civilians. All of these arguments for civilianization may be 
true; however, from the findings that we report here, there 
appears to be a cost associated with civilianization. 

Currently, in combination, pay scales and career 
opportunities for civilian crime analysts compare unfavorably 
with sworn members. In the departments that we visited where 
the crime analysts were civilian, we were told that this 
disparity accounts for high turnover. As one analyst put it: 

The pay here is lower than it 
should be. We get much less than 
commissioned officers. When a 
better job comes along, crime 
analysts leave. We lose someone 
every couple years. 

OPERATIONS 
Background 

Over its existence, LEAA provided substantial support to 
professionalize all sectors of the criminal justice system. 
LEAA was especially interested in facilitating rational 


planning. In the early 1970s formal planning agencies were 
created in all fifty states (Hudzik and Cordner, 1985) to 
assist LEAA in administering its fiscal responsibilities, as 
well as to assist county and municipal criminal justice 
agencies in their planning efforts. 

Grants were awarded to numerous police agencies to create 
planning units; frequently LEAA required the funded agency to 
create a formal crime analysis function. Evaluation reports 
submitted by the grant recipients tell us a good deal about the 
structure, management, and operations of these initial efforts 
to formalize crime analysis (e.g., Austin et al.). In reading 
these reports, one is struck by how much, and how little, crime 
analysis has changed over the past three decades. In this 
section, we use these reports as a frame of reference to 
develop some sense of the evolution of crime analysis 
operations. 

Hardware Technology 

We found that nearly every department (of all sizes) has 
automated their information systems. More than 9 out of 10 
departments that were surveyed have at least one computer 
(mainframe, minicomputer, or PC/Mac); nearly 9 out of 10 
departments who have computers also have a formal records 
management system. This represents a truly remarkable 
improvement over the way data were managed thirty years ago. 


Since automated data storage was limited to a mainframe 
platform in the 1970s, only the larger departments could 
justify the expense. And of those departments, most were 
allotted space on the city or county mainframe. Accessing the 
data was virtually impossible for police personnel. Reports 
were limited to standard crime counts and some crude time 
comparisons that were provided by city or county data systems 
personnel according to a regular schedule (e.g., monthly crime 
totals). Ad hoc queries were nearly impossible, since the 
analyst would have to be familiar with the data base management 
systems arcane programming language. Even when someone in the 
department could write the code to query the system, getting 
time on the mainframe was often difficult. In short, the data 
were nearly impossible to access for timely tactical or 
strategic purposes. 

Beginning in the 1970s, frequently with the help of LEAA, 
some departments were not only automating their data, but were 
integrating it with crime data from other law enforcement 
agencies in their region, and were providing access to a broad 
audience of police personnel. For example, in Eugene, Oregon, 
the police department entered into an agreement with six law 
enforcement agencies in the region to share each others crime 
data through a single mainframe computer. San Diego police 
entered into a similar arrangement with fifteen other regional 
law enforcement agencies. In both cases, data systems 


personnel designed user-friendly terminal screens that provided 
most sworn members the wherewithal to execute basic queries of 
the data. These were ground-breaking agencies who were 
building the foundation for integrated justice information 
systems (IJIS). Nearly thirty years later, we are still 
pushing for data sharing between law enforcement agencies, 
frequently without success. 

There is no question that hardware inventories have 
improved dramatically over the past thirty years. Every 
department that we visited could point to a reasonably 
sophisticated selection of hardware: computers (mainframe, 
minicomputer, and PC/Mac), printers, CAD systems, digital 
imaging, etc. All of the departments visited were pleased with 
advances in intra-departmental hardware integration (e.g., 
local area networks) and the opportunity that it provided to 
facilitate communication. Most analysts also said that they 
were almost constantly in the process of assessing and 
improving the overall system. According to one analyst: 

We seem to get the money from 
somewhere. Grants have helped a 
lot. Ive been here for seven 
years and I cant remember a time 
when we werent doing something 
with the system. We just changed 
the mainframe, we just got the 
plotter. When you ask us about 
our hardware, it depends on what 
day you ask. 

Analysts that we interviewed prided themselves on being 
familiar with the state of technology. When there were 


complaints about hardware, it usually centered on the analysts 
desire to be more current. The more pressing problem, one that 
had less to do with quantity or quality of the hardware 
inventory, is typical of information technology weaknesses in 
many public organizations. There seems, in many cases, to be a 
lack of rational planning. Systems are built incrementally, 
without a clear and comprehensive overall plan. The comments 
of one analyst highlight this point; 

Walk around the units and youll 
see plenty of equipment, a lot of 
it right out of the box. But we 
just dont use all of it in a way 
that gets the most out of it. 
Its kind of like a patchwork. 

Software Technology 

Planning problems show up even more clearly when one 
considers the software inventories. As we noted above, 
hardware systems overall have improved dramatically. Every 
department that we visited had reasonably sophisticated 
automated systems. The mail survey and the phone survey 
revealed that a wide range of data are stored in computers. 
Site visits supported the mail and phone survey findings. 
However, a closer look at these state-of-the-art departments 
revealed some rather significant weaknesses. Two such problems 
stand out in particular: 1) There are still departments that 
rely on a primary case report that do not document the basic 
characteristics of the incident (e.g., type of weapon) through 


forced-choice check boxes; instead, much of the incident detail 
is left to be described in the narrative section of the case 
report. 2) There are still departments that do not enter all 
relevant data into the computer system. We found that some of 
the departments that we visited have had to design off-theshelf 
data bases (e.g., Access) to key in important case report 
data and then use that data base for analysis purposes. In 
these departments, crime analysts may spend several hours of 
their work day keying data into the homemade system.6 This 
represents a serious deficiency. One of these analysts 
complained: 

I spend a couple hours a day 
putting data into the computer. 
On a Monday, after a busy 
weekend, its even worse. I 
dont think Im being used as 
well as I should when Im 
spending so much time entering 
data. 

6 It should be noted that there is an upside to this. Even though 
the data entry by analysts is time consuming and unfortunate data 
entry tends to be more consistent since it is entered by few instead
of many. 

Another problem that every department that we visited 
complained of, to varying degrees, concerned various data 
conversion routines. Several analysts said that the 
conversions from, say, the computer aided dispatch to the SQL 
server, were awkward and in many cases the data were not 
entirely accurate. The root cause of software deficiencies, 
according to some analysts, is similar to the root cause of 
hardware deficiencies: software applications are designed and 


implemented incrementally and there is an apparent weakness in 
overall software planning and integration. Again, this is not 
a problem specific to policing, but is common to most public 
organizations. The problems that we found in information 
technology planning, both with respect to hardware and 
software, are discussed at length by Dunworth et al. (Abt 
Associates, 2000). 

When it comes to the types of software that are most 
commonly used by crime analysts in the site visit departments, 
first and foremost were data base management systems, ranging 
from sophisticated department-wide ones to smaller off-theshelf 
supplemental and specialized data bases. As we noted 
above, some of these supplements are designed to overcome 
shortcomings in the department-wide data management systems. 
Spreadsheet software is also used extensively to supplement 
larger data base management systems. Easily, crime mapping 
software is the most dominant analytic tool, used extensively 
by all the departments that we visited. Word processing 
programs and publishing programs are also used widely to aid in 
the preparation of the myriad of notices, bulletins, alerts, 
etc. that crime analyst units provide. One department in 
particular has developed special skills in developing graphics 
to support presentations in court. Like the mail survey 
indicated, we found very little use of statistical software. 


When units do engage in some basic forms of statistical 
analysis, they usually rely on spreadsheet programs. SPSS and 
SAS were rarely found to be used. Some of the units we visited 
were beginning to familiarize themselves with publishing 
various reports to the web. Some departments were also using, 
to a limited extent, manpower allocation software; however, 
these were clearly exceptions. It is safe to say that the most 
sophisticated crime analysis units tend to rely almost 
exclusively on the basic suite of office software, with primary 
emphasis on geographical information systems. 

Analytic Output: Tactical 

Thirty years ago, many departments had no automated 
information systems. In those departments, and even in the 
departments that did have some form of computerized records 
management, officers discovered patterns by crude and highly 
inefficient means (OShea, 1998). Scanning hardcopy case 
reports, manual pin maps, and word-of-mouth were the basic 
means of discovering patterns and relationships in the data. 
The primary role of crime analysis units that were formed in 
the early 1970s was to search for patterns, but more 
importantly their role was to disseminate a range of formatted 
reports to relevant targets, both for tactical and for 
strategic purposes. The idea, in short, was to provide 


statistical output to a broad audience, one that might be 
likely to make use of the analyzed information, but would not 
be likely to generate it themselves. The kinds of reports that 
crime analysts produced were for the most part crime counts and 
a wide range of memo-like bulletins to give officers 
information about identified patterns, wanted subjects, officer 
safety issues, etc. 

When we looked at the hardcopy output of the crime 
analysis units that we visited, we were struck by how similar 
the substance of those products were to the sorts of output 
that crime analysis units generated nearly thirty years ago. 
Due in large part to advances in software technology, the style 
of the products may be different; however, the substance is 
fundamentally the same. Appendix C illustrates examples of 
some of the older forms of reports. 

The ad hoc nature of the relationship between crime 
analysts and their targets is reflected in the type of tactical 
output that these units generate. Every department that we 
visited made some effort to organize the tactical analysis that 
they produce. None, however, had successfully developed a 
comprehensive system of categorizing their products. Analysts 
had a difficult time in articulating a typology of reports that 
they create. We found literally hundreds of different types of 
reports that are produced by the nine sites that we visited. 
When output is posted in places where detectives and beat 


officers/deputies can view them (e.g., the briefing room 
bulletin board), it is common to find several formatted reports 
(e.g., pattern alert and wanted person) and a vast array of 
apparently unrelated documents. Targets would clearly find it 
difficult to assimilate this endless sweep of information. The 
problem is further aggravated when output from neighboring 
departments is added to the mix. The comments of one analyst 
illustrate this problem: 

We have a lot of different types 
of reports. We dont have but a 
few standard reports that we put 
in the numbers for according to 
the time its made up. You can 
see the board is organized by 
patrol assignments, but each 
board has a lot of different 
information. An officer has to 
spend some time to get it all. 
They seem to look at the wanted 
and pattern ones more than some 
of the others. 

Lack of apparent standardization of report formats 
notwithstanding, we do feel that the tactical report samples 
that we received, both through site visits and through follow-
up phone interviews, do in fact form a rather clear set of 
categories. Although they are not entirely inclusive and 
exclusive, the categories we suggest provide a rough sense of 
the types of tactical reports that crime analysis units are 
producing. It might be useful to describe tactical output from 
the sites we visited and from the departments that we spoke 


with by telephone using these categories as an organizing 
framework. 

Crime and Incivility Conditions 

Crime series and crime patterns are the most important 
output that crime analysis units produce. The style and layout 
of these reports vary greatly between departments; however, 
they all provide a narrative description of the series or 
pattern and relevant incident information (date, time, 
location, suspect, proceeds taken, vehicle used by offender, 
etc) about the linked incidents. In nearly every department 
that we spoke with, this type of report also contains a digital 
map to illustrate the geographical aspects of the pattern or 
series. 

The mail survey and interviews with analysts indicate that 
the methods used to discover series and patterns are similar to 
those used in the past. Many series and patterns are 
discovered through word of mouth. Beat officers/deputies and 
detectives take reports and investigate incidents and over time 
begin to develop a hunch that a series is emerging. Informal 
conversations with other sworn members either confirm or reject 
the officers hunch. Using some form of a matrix, or table, is 
another popular method of discriminating series or patterns 
from the data. This systematic method has been used by police 
departments for years; the first published account of the 


method can be traced to Yamada and Spice (1979). A table is 
created with general incident characteristics (date, time, 
point of entry, point of exit, weapon used, proceeds taken, 
etc.) forming the columns and individual incidents (the report 
number) forming the rows. Examining the boxes provides a means 
of identifying incidents that are linked according to patterns 
that checks form in the boxes (see appendix D). Building these 
matrices manually is time-consuming. With the querying 
capabilities of automated data bases, matrices can now be 
easily constructed. This was a popular method of series and 
pattern recognition used by the departments that we visited. 

Mapping crimes is another popular method of series and 
pattern recognition. Manual pin maps have been a mainstay of 
police crime analysis for some time. Mapping has benefited 
from technological advances much in the same way that matrix 
construction has. Judging from our site visits and phone 
interviews of large departments, mapping is the primary method 
for identifying series and patterns. Discriminating a series 
or pattern from a map is considerably easier than doing so from 
a matrix. When incidents cluster on a map, they are easily 
grouped (see appendix D). The weakness of mapping, and the 
reason that it is so much simpler than a matrix to discriminate 
series and patterns, is that it groups incidents by a single 
dimension (i.e., location). 


Hot spots or general problems are another form of report 
that falls under this category. This type of report differs 
from the pattern or series: a pattern or series involves 
multiple criminal events that share a few or many 
characteristics (MO); hot spots involve multiple incidents that 
share location as the dominant unifying characteristics; a 
problem involves an ongoing event or series of events that may 
include matters of incivility. The problem report is the 
product of a systematic analysis of the root causes of a 
problem; it also implies a specific tactical response. For 
example, the analyst may discover in reviewing calls for 
service that numerous calls have been received complaining of 
teens drinking in a local park or possibly disturbances around 
a local bar at closing time. Or an analyst, in cooperation 
with the crime prevention unit, may discover that a theft from 
vehicle pattern around a housing complex is related to lighting 
and traffic patterns. 

When it comes to hot spot identification, most departments 
rely on mapping software. Although hot spot software is 
available to aid in this process, it is rarely used by the 
departments that we visited, nor is it used by the vast 
majority of departments in which we conducted follow-up phone 
interviews. In the mail survey of big departments, 60% said 
that they use mapping software, but only 24% of those said that 
they use hot spot software. We discovered that the respondents 


on the mail survey who said that they were using hot spot 
software ordinarily meant that they were identifying hot spots 
by visually discriminating dot clusters, not by utilizing 
software that automatically identifies (through complex 
algorithms) the groups of incidents that comprise the hot spot. 

Although problems may be discovered by a crime analyst, in 
the departments that we spoke with the dominant form of 
identifying a problem, especially in those departments that 
have formalized problem-oriented policing, is through the beat 
officer. The crime analysis unit often acts as the medium for 
disseminating the information. One analyst, from a department 
that has adopted a problem-oriented policing model described 
this type of report: 

Weve designed a report that 
helps our POP program. When an 
officer works on a problem and 
comes up with a solution, we make 
up a report to let others know 
what the officer did. It gets 
the word out to other officers 
who might have something like 
it[the problem] going on in their 
area. 

Activity Reports provide summary information to targets. 
The 24-hr activity report is used by most departments. It 
informs officers about criminal activity over a 24-hr period in 
the precinct/district/substation, usually by some smaller 
subdivision within the precinct/district/substation (e.g., beat 
or sector). The report consists of a brief summary (e.g., 


incident type; date, time, location of incident; suspect or 
wanted subjects, vehicle used, etc.) of incidents over the last 
three watches (see appendix D). The 24-hr activity report is a 
useful tool to inform officers of events on their beat while 
they were off-duty. The weekly and monthly activity reports 
were found less frequently at the field level, but were 
routinely produced for managers at the sites we visited. The 
format of the weekly and monthly reports is less detailed than 
the 24-hr activity report; it is generally a summary crime 
count broken out by precinct/district/substation, sector, or 
beat (see appendix D). 

Finally, under crime and incivility, some departments 

continue to produce the hot sheet. This is a list of stolen 
vehicles by license number. Many departments have done away 
with the hot sheet because of the advances in electronic 
communications that allow almost immediate license checks via 
mobile computers. 

General Offender: Detain 

The wanted poster is as old as law enforcement in America. 
Years ago, the larger departments (e.g., New York, Chicago, and 
Los Angeles) formed specialized units to produce in-house 
published materials. These units were normally within the 
identification sections of the department. Many of the 
departments that we visited, and departments that we spoke with 


by telephone, have assigned these duties to crime analysts, 
thanks in large part to digital photo reproduction and desktop 
publishing software. Once again, this task has fallen to the 
crime analysis unit because they are the ones familiar with 
computer applications. These types of reports notify field 
officers of wanted subjects, sometimes with and sometimes 
without arrest warrants, but at least with probable cause to 
arrest. In nearly every case a narrative description of the 
wanted person is accompanied by a digital photo/s and the 
appropriate unit to notify when the offender is apprehended 
(see appendix E). 

General Offender: Information Only 

These types of reports are similar in format to the 
general offender: detain reports (see appendix E). They differ 
because the persons named are not subject to arrest. The 
primary purpose of the report is to inform field personnel of 
the presence of the named individuals within the departments 
jurisdiction. 

Corrections agencies, both custodial and community, now 
routinely provide inmate information to local law enforcement 
agencies. In some states, corrections departments are required 
to notify local law enforcement of released sex offenders, for 
example. Many of the departments that we spoke with produced 


reports that informed officers of penitentiary mandatory 
releases, parolees, and probationers (see appendix E). 

A common form of report that nearly every department in 
our sample produced could be generally termed alerts. These 
are reports of known offenders that engage in various criminal 
activities, but are not at present wanted by authorities. For 
example, a gang unit may want to advise patrol officers of 
known street drug peddlers working in their area. The reports 
consist of information about the subjects and include digital 
photo/s (see appendix E). 

Analytic Output: Strategic 

Strategic analysis supports long-range planning. Like 
tactical analysis, this type of analysis consists of both 
formatted reports produced at regular intervals and reports of 
a more ad hoc nature. In the mail survey we found that more 
than 75% of the respondents engaged in various forms of 
strategic analysis. Only two of the departments that we 
visited specifically designated crime analysts for non-tactical 
activities. That is not to say that the analysts in the other 
departments did not engage in non-tactical analysis; they, of 
course, did. 


Nature and Extent of Crime 

This type of activity reports are the most common form of 
strategic analysis. At a minimum, they describe crime totals 
broken down by jurisdiction, from the smallest (e.g., beat) to 
the biggest (e.g., the city, county, or state, depending on the 
departments jurisdiction) (see appendix D and F). They may 
also be broken down by other relevant geographical areas (e.g., 
housing complex, apartments, industrial area, etc.). The 
reports may also include information about cleared cases, 
number of arrests, victim demographics, times and seasonal 
variations, and comparisons between time periods (e.g., this 
month this year v. this month last year). All of the 
departments that we visited and spoke with produced some 
variation of this type of report, usually on a monthly and/or 
yearly basis. The reports are presented in some combination of 
narrative, graphs, and charts. In some departments the reports 
are automatically generated by the records management system. 
In some departments they are rather elaborate documents with 
professional-like graphics, suitable for distribution to a wide 
audience (see appendix F). 

Planning 

With the introduction and diffusion of community policing, 
formal rational planning has become more common in police 
administration. In a recent survey, conducted in 2000, OShea 


(2000) found that nearly 1 out of 3 police departments with 
over 100 sworn personnel engaged in formal strategic planning. 
Depending on the level of sophistication with which a 
department engages in strategic planning, the demands for 
information are correspondingly increased. Collection, 
collation, analysis, and dissemination of this information have 
been viewed as a crime analysis function. This was clearly the 
intent of LEAA when they chose to link crime analysis funding 
with the effort to diffuse formal planning. If this is so, 
then the outcome is an increase in the demands made upon the 
crime analysis unit. We found that in most of the departments 
that we visited and spoke with in telephone interviews, the 
crime analysis units were normally asked to contribute, albeit 
to a limited extent, to the formal planning process. 

Most departments that we spoke with contributed in some 
fashion to the budget preparation process. Nearly every 
department that is involved in the budget process submits the 
activity reports (see immediately preceding section). However, 
in some instances, the analytic process was a bit more 
sophisticated. The method that one department used illustrates 
this type of report: 

We have been asked to estimate 
crime in the city for the budget 
people. Ive done some simple 
regression analysis in Excel to 
project crime trends. They use 
the numbers when they go to the 
Council. 


Several departments that we visited engaged in a formal 
strategic planning process. In some cases, the crime analysis 
units were asked to provide information to planners. Usually, 
this information consisted of crime counts found in the 
activity reports (see above). None of the crime analysis units 
that we visited or that we interviewed by phone were formally 
integrated into the planning process. None of the departments 
conducted a formal analysis of department strengths, 
weaknesses, opportunities, and threats (SWOT). Some 
departments did administer surveys (normally citizen, but in 
some cases sworn personnel and victim). Some departments have 
completed surveys that are loosely linked to the initiation of 
community policing (either formation or evaluation), and some 
have begun to utilize citizen surveys as a component of their 
performance measurement system. For example: 

We randomly select and send 
surveys to the public. Theyre 
taken every quarter and broken 
into precincts. We get the 
percent satisfaction. The 
results are shared with 
commanders so they can work on 
improving them. Theyre 
performance evaluators. 

Surveys are anonymous and 
randomly selected from victims. 
We use them as part of our 
performance measures and they are 
included in the annual report. 


Resource Allocation 

Several crime analysis units that we spoke with are the 
primary staff resource to department planners who are charged 
with assessing the beat boundaries and manpower allocation, 
both by shift and by beat. Many of the departments that we 
visited and that we interviewed by phone said that they are 
utilizing their mapping software to help in manpower allocation 
decisions. Several methods were described by analysts: 

Based on our call volume we 
realign and redraw the county 
precincts and zones within 
precincts. The areas with the 
most calls get the most deputies. 

We study call frequencies in 
zones and add staff to stations 
where needed. 

We conduct semi-annual review of 
reports and the outcome of each. 
Thats how we determine workload 
distribution. 

We use software that goes through 
the calls for service and lays 
out how many officers we should 
have working at each station and 
on each shift. The Chief and his 
staff love the program (see 
appendix F). 

Policy Research 

Over the past twenty or so years the police mandate has 
substantially broadened. The professional model of policing 
was driven by crime control and its proponents discouraged 
police activity outside of that mandate. Too much interaction 


with the community, the logic went, set the stage for corrupt 
practices. The mandate for community policing is grounded in 
service delivery, broadly defined, and includes activities that 
are likely to facilitate not only crime control, but also 
quality of life. This implies that police executives will be 
forced to develop a working knowledge of social policies that 
are vastly more complex and varied than those associated with 
the professional model of policing. 

In many departments, the crime analyst has been charged 
with gathering background information in various policy areas 
to inform police executives. We found that most of the 
departments that we visited or spoke with by phone were from 
time to time asked to engage in this form of analysis. Several 
examples of this type of product are illustrative: 

We are always getting asked by 
the chief to research something. 
Its part of our job and I 
personally like it. I know most 
crime analysts that I talk to 
dont. They think its not crime 
analysis. But I like it. It 
gives me a chance to do something 
interesting. 

Weve been working on a report 
for the chief on racial 
profiling. He wants it for the 
city council. 

The problem has been to figure 
out what to do with homeless 
people. Its one that a lot of 
places have trouble with and they 
asked us to look into it. 


Last year they [crime prevention 
unit] asked us to do some 
research in how to get the city 
to condemn property. It was 
interesting and the crime 
prevention people were happy. 

The department is thinking about 
changing the way it measures 
performance. The deputy chief 
asked us to find out what other 
departments are doing and to try 
to figure out if our data base 
could be changed to support a 
better performance measurement 
system. 

Its been tough to get community 
groups formed and to get them to 
work together once they are. 
Tough to keep them interested and 
to get them to attend meetings 
when theres no big problem right 
then. Weve been looking around 
to figure out how other places 
have been doing in that sort of 
thing. 


IV. POLICY IMPLICATIONS 

INTRODUCTION 

The surveys that we administered to large and small 
departments provided a panoramic view of crime analysis 
operations. Some of our intuitions, as well as some of the 
anecdotal evidence about crime analysis were confirmed by the 
findings; some were rejected. Most important, the findings 
stimulated a deeper, more critical set of questions that 
provided the framework for the second phase of this project, 
the follow-up phone interviews and site visits. The deeper 
probe revealed a more detailed view of the crime analysis 
function and a better understanding of the complex set of 
interrelated characteristics that affect its operations. In 
this section we briefly review those characteristics and 
propose the policies that they imply. 

ORGANIZATION 

Specialized Function 

Brief: Division of labor is the cornerstone of 
Webers ideal bureaucratic organization. In a 
complex organization, the rational manager breaks 
down the work of the organization into its component 
parts; each part is assigned to a separate department 
that performs a specialized function. In the end, 


structure becomes the means by which an organization 
achieves technical and economic advantages associated 
with specialization. Some tasks are not appropriate 
for specialization. When a specialized unit is 
created unnecessarily, the inefficiencies associated 
with redundancy are created. It is also particularly 
important for managers to realize that the conditions 
that initially justify a specialized function can 
change over time. Organizational paradigms change, 
technologies change, the characteristics of personnel 
change, etc. The crime analysis function has evolved 
into a specialized unit in American policing, or so 
the findings that we report here seem to indicate. 
Some have argued that greater efforts should be 
directed toward pushing data to the end users (i.e., 
beat officers and detectives) in a fashion that 
facilitates analysis at the lowest levels; others 
argue that pulling data to a specialized unit and 
conducting the analysis there is more efficient and 
more effective. This is a legitimate issue for 
police executives to consider; it has generally not 
been one that police policymakers have addressed. 
Policy Implication: The appropriateness of 
functionally differentiated crime analysis should not 
be assumed. Academics and practitioners should work 


toward constructing a clear understanding of what we 
mean by crime analysis and what its appropriate place 
in the organization is and should be. 

Line/Staff 
Brief: The role a functionally differentiated unit 
should play, either staff or line, is an important 
aspect of organizational dynamics. Police executives 
consider crime analysis a staff function. Crime 
analysis units are viewed by executives, as well as 
members of the unit, as a support and advisory 
function to line members. This is clear and 
uncontroversial. The question is not whether crime 
analysis should be a staff function; the question is 
rather to whom the staff function should be directed. 
The findings suggest that crime analysts value 
tactical analysis over strategic analysis. Managers 
seem to share that view, even though most crime 
analysis units are under the authority of 
administrative divisions. Emphasis on tactical 
analysis is troublesome when one considers the 
following: (1) Tactical crime analysis is a 
specialization because it requires technical skills 
and training that the ordinary sworn member does not 
have. After all, organizational complexity is what 
drives specialization. Hardware and software 


technology is moving toward more user-friendly 
applications. Education levels and familiarity with 
computers among police recruits is increasing. These 
changes should reduce the need for tactical analysis 
to be performed by experts (consider, e.g., Chicago 
PDs user-friendly mapping program, ICAM). As the 
need for support decreases, so does the need for a 
functionally differentiated unit, that is, if it 
limits its efforts to tactical analysis; (2) Current 
thinking about police administration stresses the 
importance of strategic management, which demands 
substantially higher levels of strategic intelligence 
(e.g., stakeholder assessments, forecasting, policy 
evaluation, performance measurement, organizational 
intelligence, citizen surveys, etc.). The necessary 
technical skills and training to support these 
activities parallel those that we associate with the 
crime analyst. Overemphasis on tactical analysis 
draws the analysts skills away from areas in which it 
can be useful. 
Policy Implication: Managers should carefully 
reconsider the target audience for crime analysis 
operations. Overemphasis on tactical analysis should 
be avoided. 


Centralization 

Brief: The question of centralization in the crime 
analysis context arises when the following occurs: a 
functionally differentiated unit is formed; the unit 
has multiple members; and, there are multiple 
dispersed units that are the targets for crime 
analysis support. At issue is whether crime analysis 
operations should have an independent and distinct 
chain of command (centralized), or whether its 
members should be embedded in the chain of command 
within the dispersed units that they support and are 
physically assigned (decentralized). For example, 
should a crime analyst assigned to support a precinct 
station fall within the precinct station chain of 
command, and thereby under its authority, or should 
the crime analyst fall within a department-wide crime 
analysis unit chain of command and ultimately be 
responsible to the crime analysis operations superior 
officer? Regardless of which arrangement, frequently 
when the unit is decentralized, a central crime 
analysis authority remains; thus, the principle of 
unity of command is violated. Many of the 
departments that we visited and interviewed by phone 
followed this practice, arguing that there was need 
for a centralized line of authority, but it was 


impossible to ignore the local one. To do so, they 
insisted, would damage the relationship between crime 
analysis and target. Often, according to 
respondents, this hybrid decentralization arrangement 
leads to situations in which the crime analysis unit 
mission is subordinated to a variety of questionable, 
and at times trivial, needs of the target superior. 
Policy Implication: The crime analysis unit, when it 
is multi-member and when it supports multi-targets, 
should be structured with a single, distinct unit 
chain of command. 

Coordination 
Brief: As organizations become more complex it is 
necessary to divide labor. The benefits of the 
division of labor have been discussed above. There 
are, at the same time, costs associated with the 
division of labor. Functionally differentiated units 
tend to develop independent, unit-level missions that 
may or may not conform to the overall mission of the 
organization. Management must seek to coordinate 
inter-unit operations to insure that all units remain 
focused on the organizations strategic mission. In 
its staff capacity, the crime analysis unit should 
serve as a structural means to facilitate inter-unit 
coordination. Crime analysts, as the departments 


information specialists, should be the primary center 
for identifying tactical and strategic problems, and 
ultimately for generating alternative responses. To 
do so, however, the unit must develop sound, formal 
links with other units. We found that this was not 
the case. Links with other units are informal; 
interactions between units are primarily ad hoc. 
Although it appears that nearly three-quarters of 
large police departments have chosen to specialize 
the crime analysis function, the operations of the 
function seem to be, at best, loosely integrated into 
the fabric of the organization. One is struck by the 
perception of many crime analysts that they must 
sell the unit to others in the department. Unit 
members appear insecure, reluctant to assert 
themselves, and generally unwilling to take their 
place in the organization. 
Policy Implication: Law enforcement executives should 
consider designing formal, structural arrangements to 
link the crime analysis unit with logically connected 
operations and units (e.g., crime prevention, POP, 
planning, COMSTAT, etc.). 

Performance Measures 
Brief: Measuring individual, unit, and organizational 
performance is critical to the management function. 


We found little evidence of formal performance 
measurement in the crime analysis units that we 
visited and spoke with in phone interviews. This 
deficiency appears to be linked to the overall ad hoc 
nature of the relationship between crime analysis and 
the targets to which they provide support. This 
relationship could best be described as tentative. 
Crime analysts, as noted above, feel the need to 
sell their product to other members of the 
department. Analysts have no clear sense about which 
products are considered useful to the target. They 
produce, deliver, and through anecdotal evidence draw 
conclusions about the value of their work. Neither 
the analysts, nor the analysts managers are clear 
about how and how well targets use their product. 
Individual-level quantifiable measures (e.g., number 
of patterns identified, alerts generated, forecast 
accuracy, etc.) cannot be established until managers 
know what products the crime analysis unit should be 
producing. And this has to come through a systematic 
assessment of crime analysis output. Most units that 
we visited and spoke with by phone interview were 
aware of this deficiency and have made efforts to 
remedy it, but with only minor success. 


Policy Implication: Managers should design formal 
mechanisms (e.g., surveys, focus groups, interviews, 
etc.) to discover the relative value that end-users 
place in their products and thus begin the process to 
develop useful performance measures. Managers should 
also look to outside sources (e.g., COPS, 
International Association of Crime Analysts, etc.) 
for guidance in establishing the value of various 
crime analytic output. 

Organizational Demand 
Brief: The performance of a unit is a function of the 
demands made upon it by the organization and of the 
appreciation with which its output is received by 
colleagues. This was supported by our findings. 
Agencies in which managers demanded more 
sophisticated products and agencies in which the work 
of the analysts were appreciated scored higher on all 
dimensions. The findings that we reported here were 
disappointing on several levels. The most striking 
finding was that with all the advances in technology, 
crime analysts continue to count crime rather than 
analyze it. Analysts rarely perform sophisticated 
tactical or strategic analysis. The products that 
dominate are primarily crime counts, simple digital 
maps, and notices to alert officers about offenders 


or suspects. Low-level output may well correspond to 
current demand and target response. If managers are 
simply not aware of the range of possible support 
products that a fully-functioning crime analysis unit 
is capable of, then low-level demand is likely and 
thus low-level output is to be expected. Also, we 
estimate that at least 6 out of 10 larger law 
enforcement agencies do not engage in strategic 
planning. This suggests that many departments are 
unlikely to place a high value on strategic analysis, 
again, creating an environment of low demand for this 
more sophisticated form of analysis. 
Policy Implication: Organizations should develop 
formal inservice curriculum to increase the level of 
manager awareness about the potential applications of 
tactical and strategic analysis. Crime analyst units 
should sell themselves, in a sense, but in a more 
formal, effective manner. This of course assumes 
that crime analysts must first themselves understand 
what an ideal crime analysis unit is capable of. 
Here, too, police departments may have to look to 
outside sources (e.g., COPS, Police Foundation, 
International Association of Crime Analysts, academic 
institutions, etc.) for assistance. 


PERSONNEL 

Staffing 

Brief: We found that the ratio of analysts to sworn 
members was slightly less than 1 per 100. While we 
have no evidence to suggest that this is an optimum 
ratio, it would appear to conform to standards 
currently recommended by professionals working in the 
area. The staffing issue becomes relevant only when 
one considers smaller departments, those with less 
than 100 sworn personnel. Some have suggested that 
smaller departments probably cannot justify dedicated 
crime analysts. Since integrated justice information 
systems are now more affordable and thus a realistic 
option for small departments, the crime analysis 
function can be constructed in a manner that would 
permit smaller departments to share a unit between 
multiple jurisdictions. 
Policy Implication: Crime analysis units should be 
staffed at a level of 1 analyst per 100 sworn 
personnel. Small departments should consider either 
contracting out for crime analysis or forming 
agreements with neighboring agencies to integrate 
their information systems and share the services of a 
crime analyst/s. 


Job Description 
Brief: Formal job descriptions were routinely found 
in the departments that we visited and spoke with in 
phone interviews. Ordinarily, they were drafted to 
be used for job announcements. We also found them 
contained in departmental general orders. The entry-
level position generally called for a person with an 
undergraduate degree in one of the social sciences7, 
familiarity with basic office software, and good 
verbal and written communication skills. Basic 
entry-level requirements for job announcement 
purposes seem to be reasonably well described. 
However, when it comes to articulating the routine, 
daily activities of a crime analyst, the findings 
were less encouraging. We found that only one of the 
departments that we visited or spoke with had a 
formal job manual for the crime analyst position. 
Several were in the process of drafting one. Nearly 
every analyst that we spoke with believed that a 
manual was necessary, but they also pointed out that 
drafting one was a complex and difficult task that 
required a collaborative effort between analysts and 
managers. The general absence of a formal manual 
further illustrates the ad hoc nature of crime 


analysis in American law enforcement and is a further 
indication that the function has not been given the 
careful, deliberate consideration that a 
differentiated function should. 
Policy Implication: Managers with a specialized crime 
analysis function should construct a formal crime 
analysis position manual. 

7 We use the term "called for" as distinct from "required." Many
departments call for a degree, but many will substitute
experience for the academic credential.

Training 
Brief: When it comes to crime analysis, training does 
not appear to be a high priority issue. This is 
partially explained by the mission, either explicitly 
or implicitly articulated by managers and analysts. 
The tendency is to emphasize tactical analysis. Most 
analysts that we spoke with resented engaging in 
activities that cannot be directly linked to the 
identification and apprehension of offenders. To 
this end, managers and analysts have placed great 
value in data manipulation (data bases and 
spreadsheet) and various presentation tools (e.g., 
word processing, graphics, digital imaging, etc.). 
In many instances, training has been unnecessary, or 
neglected, because the people that have been drawn to 
the analyst position have acquired the valued skills 
on their own. When training is offered in crime 


analysis, according to the analysts we interviewed, 
the courses that are provided are elementary and as a 
rule geared toward the entry-level person. 
Relatively little energy or inclination has been 
directed toward higher-level data analysis. This 
type of training is especially necessary if the 
analysis function is going to become more 
sophisticated, considering that the entry-level 
requirement for analysts requires only the most basic 
understanding of statistical methods. The overall 
crime analysis function has advanced greatly in the 
storage, access, and dissemination of data. However, 
the fact remains: according to our findings, crime 
analysis continues to count crime far more 
effectively than it analyzes it. The training 
implications are obvious. 
Policy Implication: Inservice training curriculum 
should be developed to train crime analysts to 
conduct sophisticated analytic operations. Police 
departments may also consider requiring more advanced 
statistical training as a requirement for hiring 
(e.g., graduate-level research design and methods 
coursework), thereby reducing some of the need for 
inservice training. This, of course, carries with it 


human resources implications (e.g., more attractive 
pay, benefits, and career path). If police 
departments choose to train in-house, then 
arrangements will have to be made to contract with 
trainers with a background in statistical methods 
(e.g., partnerships with local academic 
institutions). 

Selection 
Brief: Selecting a civilian crime analyst is 
substantially different from selecting a sworn crime 
analyst. When it comes to civilians, most of the 
departments that we visited or spoke with began the 
process by posting a job announcement, often via the 
city or department web site. Some departments hired 
civilians from within the city personnel system. In 
those that hired from outside the city system, the 
pattern generally consisted of some combination of 
basic written exam, oral board interview, and 
background investigation. When selecting civilians, 
the most common complaint was the long processing 
time. With sworn members, the selection issues are 
different. One could reasonably assume that when a 
civilian applies for a non-sworn position he/she has 
an interest in the work. This may not be the case 


when the selection pool consists of sworn members. 
Because the position is specialized, it requires 
skills and training that may not be commonly found 
among the sworn population. If the position falls 
within the boundaries of a collective bargaining 
agreement, the selection process may be driven by 
standards that are unrelated to the job description 
(e.g., seniority). The appeal of the assignment may 
be unrelated to the work (e.g., steady watch, 
straight days, weekends off, etc.). This was the 
case in several of the departments that we spoke with 
in phone interviews. Members of the unit who did not 
have the desirable qualifications were selected 
because they bid successfully. Unit productivity 
suffered dramatically in these departments. Another 
selection issue, especially important for agencies 
forming a crime analysis unit, concerns selection of 
the unit leader. The departments that we visited and 
spoke with in phone interviews, all of which scored 
high in all domains of crime analysis, agreed that 
the units success was directly related to the energy 
of the units leader, particularly its first leader. 
The idea that the success of a crime analysis unit is 
linked to a leader, or policy entrepreneur, is 
supported by our findings. A common characteristic 


of the outstanding units that we visited was a 
dynamic leader who was a powerful influence on the 
units success. 
Policy Implication: The selection process for 
civilian crime analysts should be as brief as 
possible to avoid loosing qualified people to other 
jobs. Sworn crime analysts should be exempt from 
collective bargaining requirements for position 
assignments. Departments should carefully consider 
unit leadership, especially when a new unit is being 
formed. A policy entrepreneur is vital to a new 
unit success. 

Career Path 
Brief: Like the selection process, the career path 
depends upon whether the crime analyst is a civilian 
or sworn member. A sworn members assignment to the 
crime analysis unit, in a technical sense, has no 
effect on his/her career path. Vertical movement for 
a sworn officer is strictly a function of how well 
the officer performs on a promotion exam. Previous 
assignments have little, if anything, to do with 
promotion decisions. In the civilians case, the 
career path is very limited when compared to a sworn 
member. Ordinarily, for civilian crime analysts, 
there is little opportunity for vertical movement and 


in almost all cases none for lateral movement. There 
are exceptions: In several California departments the 
crime analyst position allows for limited lateral and 
vertical movement to other municipal departments that 
have similarly defined positions. In one department, 
the crime analyst has been eligible for advancement 
to higher-ranking civilian administrative positions 
within the department. The consequence of the lack 
of mobility, according to some analysts that we spoke 
with, has been high turnover. This is somewhat 
ironic: according to proponents of civilianization, 
turnover is reduced when sworn members are replaced 
by civilians. The tenure for sworn members in a 
crime analysis unit, the argument goes, is shortened 
through frequent transfers and promotions. We found 
that the absence of lateral and vertical opportunity 
may, at the other extreme, also result in high 
turnover. 
Policy Implication: Managers will have to weigh the 
benefits of civilian crime analysts against the costs 
of high turnover due to a narrow career path. If the 
benefits are sufficiently high, then policymakers 
should consider opening opportunities for civilian 
members, both laterally and vertically. 

Compensation 


Brief: Compensation and career path policy issues are 
similar. When the crime analyst is a sworn member, 
we found that compensation was not an issue that 
demanded administrative attention. Compensation for 
sworn members is solely a function of vertical 
movement. A move to crime analysis for a sworn 
member is always either a lateral transfer or a new 
assignment upon promotion. It has no bearing on 
compensation. When it comes to civilian members, 
compensation does become an administrative issue and 
is closely tied to career path. The limits in 
vertical movement obviously limit compensation 
increases for civilians. In addition, however, we 
found that when compared to sworn members, civilians 
with similar responsibility and who perform similar 
tasks (when compared both within and between 
departments) are paid substantially less. This is a 
condition that does not go unnoticed by civilian 
members. It would appear that, like career path, an 
unintended consequence of cost savings associated 
with civilianization is high turnover. 
Policy Implication: Managers will have to weigh the 
benefits of civilian crime analysts against the costs 
of high turnover due to compensation disparities. If 
the benefits are sufficiently high, then policymakers 


should consider pay schedules that correspond to 
sworn positions with similar responsibilities and 
tasks. 

OPERATIONS 

Hardware and Software Technology 

Brief: Law enforcement agencies have improved their 
hardware and software inventories considerably over 
the past twenty years, due in part to more 
sophisticated managers and in part to remarkable 
advances in and affordability of the technology. 
Nearly every law enforcement agency, both big and 
small, has automated systems that are capable of 
storing and processing large data sets. Judging from 
the surveys (mail and phone) and our interviews with 
crime analysis personnel across the country, we found 
that city, county, and federal government are 
providing law enforcement managers with the necessary 
financial resources to equip their departments with 
basic automated systems. These encouraging findings 
aside, we did discover that when it comes to hardware 
and software, system planning is weak. Many of the 
departments that we visited and the analysts that we 
spoke with complained of poorly planned systems that 
have been developed piecemeal over time. Many 


departments that we surveyed said they did not have a 
formal records management system, suggesting very low 
levels of planning. This is not a problem confined 
to law enforcement; information system planning is a 
problem faced by a wide range of public and private 
sector organizations. The fact remains, however, 
that the overall weaknesses of law enforcement 
agencies to engage in strategic planning aggravates 
information systems planning. Rational, 
comprehensive strategic planning has not been a part 
of the police administrative culture. When this is 
found in any organization, public or private, 
weaknesses in adapting to changing technology are 
likely to follow. 
Policy Implication: Law enforcement agencies should 
construct a comprehensive, time-bounded rational 
information systems plan. If the department is 
engaging in strategic planning, then the information 
systems plan should be specifically assigned to an 
action plan team. If the department does not engage 
in formal strategic planning, then a distinct 
information systems plan should be developed, 
including an articulated mission, goals, objectives, 
and tasks. A team should be formed, including a 


representation from crime analysis, to construct the 
plan and oversee its implementation. 


Output: Tactical 
Brief: As we have noted several times throughout this 
report, analysts and managers place a high value on 
tactical analysis. Crime analysis personnel appear 
to believe that their first priority is to provide 
support for field officers, whose primary 
responsibility is to identify and apprehend 
offenders. This belief is consistent with an 
emphasis on crime control, indicative of the 
professional model of policing. Those who have been 
responsible for charting the course for crime 
analysis have directed the deployment of resources to 
that end. Owning and using advanced hardware and 
software has been held up by crime analysts as 
evidence of a more sophisticated, professional, 
specialized function. Few would disagree that data 
are managed substantially more efficiently and 
effectively than, say, twenty years ago. If the 
ultimate aim is to be able to collect more data, 
access it more easily, and count it better, then we 
have arrived. If, however, the aim is to analyze 
the data using more sophisticated methodologies, that 
is, making the best use of the analytic tools 
available, then we clearly have not arrived. In the 
end, the substance of tactical output that crime 


analysis units currently produce is remarkably 
similar to what was produced twenty years ago. Most 
of what we found on briefing room bulletin boards 
were various notices directed toward patrol and 
investigative units about individual offenders 
(wanted, known to be working, recently released, 
etc). The turnaround time for release of these 
notices is faster than twenty years ago because of 
desktop publishing software and digital photography, 
but the substance is essentially the same. We also 
found pin maps in various places, also distributed 
primarily to line officers. These also are more 
easily produced and noticeably more current than 
twenty years ago because of the advances in 
geographical information systems, but they too are 
essentially the same. And lastly, we saw an 
occasional pattern or series notice, sometimes 
discovered by the crime analysis unit and sometimes 
discovered by a field unit, normally an investigative 
unit. There was sporadic evidence of more 
sophisticated analysis (e.g., geographic profiling, 
point-pattern analysis, standard deviation-based 
early warning systems, etc.). Applied researchers 
are working on advanced methodologies (particularly 
crime mapping applications) directed toward tactical 


analysis. However, the evidence demonstrated that 
these advanced methods were more often the exception 
than the rule. As we have noted above, counting 
crime dominates current tactical analysis. Analysts 
are expected to either know or have the capacity to 
learn basic PC skills sufficient to operate the 
machine, manage data sets (data file construction and 
manipulation, querying, report construction, etc.), 
generate digital maps (and work with data in that 
context), and publish notices and alerts. Higher-
level research design and methodologies, including 
intermediate-level statistics, are not demanded, nor 
are they currently performed by crime analysts. In 
short, crime analysis is, by all appearances, 
underutilizing the vastly improved data sets that are 
now available. 
Policy Implication: Law enforcement policymakers 
should demand higher-level preservice and inservice 
training for crime analysts that will equip them with 
higher-order analytic skills. Law enforcement 
policymakers should enter into partnerships with 
academic institutions, especially applied 
researchers, to develop approaches and methods to 
utilize law enforcement data sets for tactical 
purposes. 


Output: Strategic 
Brief: Law Enforcement Assistance Administration 
funding in the 1970s provided the first concentrated 
effort to support the creation of formal crime 
analysis units. Crime analysis was viewed as a 
necessary adjunct to the fulfillment of LEAAs 
primary goal (i.e., to encourage and facilitate 
formal, comprehensive, rational planning in the 
criminal justice system). The need for rational 
planning has not changed. If anything, the current 
model of policing stresses the value of formal 
strategic planning more strongly than ever. 
Strategic planning requires the support of 
specialized personnel that are skilled in research 
design and methods. Site visits and phone interviews 
indicated that strategic analysis of the sort that 
supports strategic planning is rarely conducted. 
Several of the sites that we visited did dedicate 
crime analysts to strategic analysis tasks, but this 
was normally limited to annual reporting and manpower 
allocation. We rarely found crime analysis personnel 
formally linked to the departments long-range 
planning process. The data do not permit us to 
speculate the reasons for this apparent 
underutilization of the crime analysis unit; however, 


several possibilities occur as possible explanations. 
First, the emphasis on tactical crime analysis is 
consistent with the professional policing model, the 
mission of which is driven by crime control and 
offender apprehension. Second, long-range, strategic 
planning has not been part of the organizational 
fabric of policing. Although the current model of 
policing encourages police managers to embrace 
strategic planning, it has simply not taken hold as 
yet. Therefore, since the demand for strategic 
analysis is low, we would expect to find it 
subordinated to tactical crime analysis, which we 
did. 
Policy Implication: Managers who have adopted formal 
long-range planning should consider creating formal 
links with their crime analysis unit and develop 
practices that will stimulate strategic analysis. 


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APPENDIX A 

MAIL SURVEY FREQUENCIES 
PHONE SURVEY QUESTIONNAIRE 
PHONE SURVEY FREQUENCIES 


Mail Survey Frequencies 
Frequency Tables 

How crime analysts organized? 

									Cumulative 
			Frequency 	Percent 	Valid Percent 	Percent 
Valid 	dispersed 	71 		13.1 		18.6 		18.6 
	separate unit 	276 		50.7 		72.3 		90.8 
	some dispersed, 
	some separate 	35 		6.4 		9.2 		100.0 
	Total 		382 		70.2 		100.0 
Missing System 		162 		29.8 
Total 			544 		100.0 

Where on org. chart crime analysis unit? 

									Cumulative 
			Frequency 	Percent 	Valid Percent 	Percent 

Valid 	Administration 	148 		27.2 		44.0 		44.0 
	Patrol 		27 		5.0 		8.0 		52.1 
	Investigations 	92 		16.9 		27.4 		79.5 
	other 		69 		12.7 		20.5 		100.0 
	Total 		336 		61.8 		100.0 
Missing System 		208 		38.2 
Total 			544 		100.0 

How rate efforts in training? - computer hardware 
									Cumulative 
			Frequency 	Percent 	Valid Percent 	Percent 
Valid 	none offered 	92 		16.9 		17.4 		17.4 
	poor 		48 		8.8 		9.1 		26.5 
	only fair 	149 		27.4 		28.2 		54.7 
	good 		195 		35.8 		36.9 		91.7 
	excellent 	44 		8.1 		8.3 		100.0 
	Total 		528 		97.1 		100.0 
Missing System 		16 		2.9 
Total 			544 		100.0 

How rate efforts in training? - computer software 
									Cumulative 
			Frequency 	Percent 	Valid Percent 	Percent 
Valid 	none offered 	32 		5.9 		6.0 		6.0 
	poor 		47 		8.6 		8.9 		14.9 
	only fair 	162 		29.8 		30.6 		45.5 
	good 		227 		41.7 		42.8 		88.3 
	excellent 	62 		11.4 		11.7 		100.0 
	Total 		530 		97.4 		100.0 
Missing System 		14 		2.6 
Total 			544 		100.0 


How rate efforts in training? - processing data 
									Cumulative 
			Frequency 	Percent 	Valid Percent 	Percent 
Valid 	none offered 	64 		11.8 		12.1 		12.1 
	poor 		45 		8.3 		8.5 		20.6 
	only fair 	164 		30.1 		31.0 		51.6 
	good 		213 		39.2 		40.3 		91.9 
	excellent 	43 		7.9 		8.1 		100.0 
	Total 		529 		97.2 		100.0 
Missing System 		15 		2.8 
Total 			544 		100.0 

How rate efforts in training? - statistical analysis 
									Cumulative 
			Frequency 	Percent 	Valid Percent 	Percent 
Valid 	none offered 	114 		21.0 		21.6 		21.6 
	poor 		77 		14.2 		14.6 		36.1 
	only fair 	132 		24.3 		25.0 		61.1 
	good 		160 		29.4 		30.2 		91.3 
	excellent 	46 		8.5 		8.7 		100.0 
	Total 		529 		97.2 		100.0 
Missing System 		15 		2.8 
Total 			544 		100.0 

How rate efforts in training? - crime analysis 
									Cumulative 
			Frequency 	Percent 	Valid Percent 	Percent 
Valid 	none offered 	91 		16.7 		17.3 		17.3 
	poor 		58 		10.7 		11.0 		28.3 
	only fair 	133 		24.4 		25.3 		53.6 
	good 		178 		32.7 		33.8 		87.5 
	excellent 	66 		12.1 		12.5 		100.0 
	Total 		526 		96.7 		100.0 
Missing System 		18 		3.3 
Total 			544 		100.0 

How rate efforts in training? - GIS (mapping) 
									Cumulative 
			Frequency 	Percent 	Valid Percent 	Percent 
Valid 	none offered 	176 		32.4 		33.6 		33.6 
	poor 		64 		11.8 		12.2 		45.8 
	only fair 	103 		18.9 		19.7 		65.5 
	good 		119 		21.9 		22.7 		88.2 
	excellent 	62 		11.4 		11.8 		100.0 
	Total 		524 		96.3 		100.0 
Missing System 		20 		3.7 
Total 			544 		100.0 


How rate efforts in training? - graphics 
									Cumulative 
			Frequency 	Percent 	Valid Percent 	Percent 
Valid 	none offered 	178 		32.7 		33.7 		33.7 
	poor 		75 		13.8 		14.2 		47.9 
	only fair 	121 		22.2 		22.9 		70.8 
	good 		123 		22.6 		23.3 		94.1 
	excellent 	31 		5.7 		5.9 		100.0 
	Total 		528 		97.1 		100.0 
Missing System 		16 		2.9 
Total 			544 		100.0 

How rate efforts in training? - report writing 
									Cumulative 
			Frequency 	Percent 	Valid Percent 	Percent 
Valid 	none offered 	80 		14.7 		15.2 		15.2 
	poor 		33 		6.1 		6.3 		21.5 
	only fair 	108 		19.9 		20.6 		42.1 
	good 		246 		45.2 		46.9 		89.0 
	excellent 	58 		10.7 		11.0 		100.0 
	Total 		525 		96.5 		100.0 
Missing System 		19 		3.5 
Total 			544 		100.0 

Connected to state/region violent crimes info. dbase? 
									Cumulative 
			Frequency 	Percent 	Valid Percent 	Percent 
Valid 	no 		288 		52.9 		54.5 		54.5 
	yes 		240 		44.1 		45.5 		100.0 
	Total 		528 		97.1 		100.0 
Missing System 		16 		2.9 
Total 			544 		100.0 

Member of formal IJIS? 
									Cumulative 
			Frequency 	Percent 	Valid Percent 	Percent 
Valid 	no 		359 		66.0 		69.7 		69.7 
	yes 		156 		28.7 		30.3 		100.0 
	Total 		515 		94.7 		100.0 
Missing System 		29 		5.3 
Total 			544 		100.0 


Participate in formal info sharing program? 
									Cumulative 
			Frequency 	Percent 	Valid Percent 	Percent 
Valid 	no 		227 		41.7 		43.1		43.1 
	yes 		300 		55.1 		56.9 		100.0 
	Total 		527 		96.9 		100.0 
Missing System 		17 		3.1 
Total 			544 		100.0 

Paperless info system, totally online? 
									Cumulative 
			Frequency 	Percent 	Valid Percent 	Percent 
Valid 	no 		459 		84.4 		86.4 		86.4 
	yes 		72 		13.2 		13.6 		100.0 
	Total 		531 		97.6 		100.0 
Missing System 		13 		2.4 
Total 			544 		100.0 

How many police reports entered into system? 

Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Valid none 
22 
4.0 
4.1 
4.1 
some 
56 
10.3 
10.4 
14.6 
most 
122 
22.4 
22.8 
37.3 
all 
336 
61.8 
62.7 
100.0 
Total 
536 
98.5 
100.0 
Missing System 
8 
1.5 
Total 
544 
100.0 

Entered into computer info system? - Initial case 

Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Valid no 
60 
11.0 
11.0 
11.0 
yes 
484 
89.0 
89.0 
100.0 
Total 
544 
100.0 
100.0 

Entered into computer info system? - Arrest 

Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Valid no 
57 
10.5 
10.5 
10.5 
yes 
487 
89.5 
89.5 
100.0 
Total 
544 
100.0 
100.0 


Entered into computer info system? - Traffic accident 

Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Valid no 
172 
31.6 
31.6
31.6 
yes 
372 
68.4 
68.4 
100.0 
Total 
544 
100.0 
100.0 

Entered into computer info system? - Intelligence 

Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Valid no 
326 
59.9 
59.9 
59.9 
yes 
218 
40.1 
40.1 
100.0 
Total 
544 
100.0 
100.0 

Entered into computer info system? - Field interview 

Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Valid no 
210 
38.6 
38.6 
38.6 
yes 
334 
61.4 
61.4 
100.0 
Total 
544 
100.0 
100.0 

Entered into computer info system? - Vice case 

Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Valid no 
287 
52.8 
52.8 
52.8 
yes 
257 
47.2 
47.2 
100.0 
Total 
544 
100.0 
100.0 

Entered into computer info system? - Calls for service 

Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Valid no 
93 
17.1 
17.1 
17.1 
yes 
451 
82.9 
82.9 
100.0 
Total 
544 
100.0 
100.0 

Entered into computer info system? - Investigative 

Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Valid no 
159 
29.2 
29.2 
29.2
yes 
385 
70.8 
70.8 
100.0 
Total 
544 
100.0 
100.0 


Entered into computer info system? - Evidence 

Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Valid no 
169 
31.1 
31.1 
31.1 
yes 
375 
68.9 
68.9 
100.0 
Total 
544 
100.0 
100.0 

Entered into computer info system? - Pawn shop 

Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Valid no 
264 
48.5 
48.5 
48.5 
yes 
280 
51.5 
51.5 
100.0 
Total 
544 
100.0 
100.0 

Entered into computer info system? - Nickname 

Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Valid no 
211 
38.8 
38.8 
38.8 
yes 
333 
61.2 
61.2 
100.0 
Total 
544 
100.0 
100.0 

Entered into computer info system? - MO files  

Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Valid no 
304 
55.9 
55.9 
55.9 
yes 
240 
44.1 
44.1 
100.0 
Total 
544 
100.0 
100.0 

Use for database management? - Mainframe  

Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Valid no 
158 
29.0 
30.8 
30.8 
yes 
355 
65.3 
69.2 
100.0 
Total 
513 
94.3 
100.0 
Missing System 
31 
5.7 
Total 
544 
100.0 

Use for database management? - Minicomputer 

Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Valid no 
364 
66.9 
71.1 
yes 
148 
27.2 
28.9 
100.0 
Total 
512 
94.1 
100.0 
Missing System 
32 
5.9 
Total 
544 
100.0 


Use for database management? - PC/Mac 

Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Valid no 
231 
42.5 
45.0 
45.0 
yes 
282 
51.8 
55.0 
100.0 
Total 
513 
94.3 
100.0 
Missing System 
31 
5.7 
Total 
544 
100.0 

Use spreadsheet software for crime analysis? 

Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Valid no 
189 
34.7 
35.2 
35.2 
yes 
348 
64.0 
64.8 
100.0 
Total 
537 
98.7 
100.0 
Missing System 
7 
1.3 
Total 
544 
100.0 

Use statistical package for crime analysis? 

Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Valid no 
429 
78.9 
80.0 
80.0 
yes 
107 
19.7 
20.0 
100.0 
Total 
536 
98.5 
100.0 
Missing System 
8 
1.5 
Total 
544 
100.0 

Extent of informally created and managed crime analysis applications? 

Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Valid informal applications 
do not exist 86 15.8 16.2 16.2 
very few, they exist, but 
it's rare to find one 157 28.9 29.6 45.8 
moderate number, not 
uncommon, but not 240 44.1 45.2 91.0 
everywhere 
almost every unit has 
at least one 48 8.8 9.0 100.0 
Total 531 97.6 100.0 
Missing System 13 2.4 
Total 544 100.0 


How many crime analysts have departement Internet access?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid none 

62 

11.4 

11.7 

11.7 

some 

92 

16.9 

17.4 

29.2 

most 

49 

9.0 

9.3 

38.4 

all 

325 

59.7 

61.6 

100.0 

Total 

528 

97.1 

100.0 

Missing System 

16 

2.9 

Total 

544 

100.0 

Specialized software department uses? - Crime analysis  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

326 

59.9 

59.9 

59.9 

yes 

218 

40.1 

40.1 

100.0 

Total 

544 

100.0 

100.0 

Specialized software department uses? - Intelligence 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

381 

70.0 

70.0 

70.0 

yes 

163 

30.0 

30.0 

100.0 

Total 

544 

100.0 

100.0 

Specialized software department uses? - Operations analysis 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

423 

77.8 

77.8 

77.8 

yes 

121 

22.2 

22.2 

100.0 

Total 

544 

100.0 

100.0 

Specialized software department uses? - GIS (mapping)  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

243 

44.7 

44.7 

44.7 

yes 

301 

55.3 

55.3 

100.0 

Total 

544 

100.0 

100.0 

Specialized software department uses? - Other 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

153 

28.1 

63.2 

yes 

89 

16.4 

36.8 

100.0 

Total 

242 

44.5 

100.0 

Missing System 

302 

55.5 

Total 

544 

100.0 


Which data does department geocode and map? - Any Part I crimes 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

36 

6.6 

11.5 

yes 

276 

50.7 

88.5 

100.0 

Total 

312 

57.4 

100.0 

Missing System 

232 

42.6 

Total 

544 

100.0 

Which data does department geocode and map? - Any Part II crimes 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

95 

17.5 

30.7 

30.7 

yes 

214 

39.3 

69.3 

100.0 

Total 

309 

56.8 

100.0 

Missing System 

235 

43.2 

Total 

544 

100.0 

Which data does department geocode and map? - Arrests  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

124 

22.8 

40.3 

40.3 

yes 

184 

33.8 

59.7 

100.0 

Total 

308 

56.6 

100.0 

Missing System 

236 

43.4 

Total 

544 

100.0 

Which data does department geocode and map? - Calls for service 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

90 

16.5 

29.2 

29.2 

yes 

218 

40.1 

70.8 

100.0 

Total 

308 

56.6 

100.0 

Missing System 

236 

43.4 

Total 

544 

100.0 

Which data does department geocode and map? - Prison release 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

262 

48.2 

85.3 

yes 

45 

8.3 

14.7 

100.0 

Total 

307 

56.4 

100.0 

Missing System 

237 

43.6 

Total 

544 

100.0 


Which data does department geocode and map? - Parole/probation  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

233 

42.8 

75.9 

75.9 

yes 

74 

13.6 

24.1 

100.0 

Total 

307 

56.4 

100.0 

Missing System 

237 

43.6 

Total 

544 

100.0 

Which data does department geocode and map? - Traffic  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

164 

30.1 

53.2 

53.2 

yes 

144 

26.5 

46.8 

100.0 

Total 

308 

56.6 

100.0 

Missing System 

236 

43.4 

Total 

544 

100.0 

Citizens allowed to view crime maps? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

143 

26.3 

42.6 

42.6 

yes 

193 

35.5 

57.4 

100.0 

Total 

336 

61.8 

100.0 

Missing System 

208 

38.2 

Total 

544 

100.0 

Who allowed to generate GIS maps? 

Frequency 
Percent 
Valid Percent 
Cumulative Percent 

Valid only designated 
unit personnel
281 
51.7 
87.3 
87.3

any sworn officer
41 7.5 
12.7 
100.0 

Total 
322 
59.2 
100.0 

Missing System 222 40.8 

Total 544 100.0 

Use 'hotspot' software? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

262 

48.2 

76.4 

76.4 

yes 

81 

14.9 

23.6 

100.0 

Total 

343 

63.1 

100.0 

Missing System 

201 

36.9 

Total 

544 

100.0 


Department have official webpage? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

68 

12.5 

12.8 

yes 

462 

84.9 

87.2 

100.0 

Total 

530 

97.4 

100.0 

Missing System 

14 

2.6 

Total 

544 

100.0 

Provide crime stats for public to view? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

230 

42.3 

46.8 

46.8 

yes 

261 

48.0 

53.2 

100.0 

Total 

491 

90.3 

100.0 

Missing System 

53 

9.7 

Total 

544 

100.0 

Provide means for public to communicate via web? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

97 

17.8 

19.9 

19.9 

yes 

391 

71.9 

80.1 

100.0 

Total 

488 

89.7 

100.0 

Missing System 

56 

10.3 

Total 

544 

100.0 

How many employees does department provide E-mail accounts? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid none 

34 

6.3 

6.4 

6.4 

some 

194 

35.7 

36.3 

42.7 

most 

150 

27.6 

28.1 

70.8 

all 

156 

28.7 

29.2 

100.0 

Total 

534 

98.2 

100.0 

Missing System 

10 

1.8 

Total 

544 

100.0 

Does department have CAD system? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

59 

10.8 

10.9 

yes 

480 

88.2 

89.1 

100.0 

Total 

539 

99.1 

100.0 

Missing System 

5 

.9 

Total 

544 

100.0 


How many of department's PC's networked? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid none 

7 

1.3 

1.3 

1.3 

some 

53 

9.7 

9.8 

11.1 

most 

222 

40.8 

41.1 

52.2 

all 

258 

47.4 

47.8 

100.0 

Total 

540 

99.3 

100.0 

Missing System 

4 

.7 

Total 

544 

100.0 

Does department have formal records management system? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

46 

8.5 

8.5 

8.5 

yes 

495 

91.0 

91.3 

99.8 

3 

1 

.2 

.2 

100.0 

Total 

542 

99.6 

100.0 

Missing System 

2 

.4 

Total 

544 

100.0 

Best describes RMS? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid dept pers 

102 

18.8 

20.6 

20.6 

consultant 

154 

28.3 

31.1 

51.7 

comm software 

239 

43.9 

48.3 

100.0 

Total 

495 

91.0 

100.0 

Missing System 

49 

9.0 

Total 

544 

100.0 

Rate your department's records management system?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid poor 

53 

9.7 

10.8 

10.8 

fair 

165 

30.3 

33.5 

44.3 

good 

212 

39.0 

43.1 

87.4 

excellent 

62 

11.4 

12.6 

100.0 

Total 

492 

90.4 

100.0 

Missing System 

52 

9.6 

Total 

544 

100.0 


Technical support? 

Frequency 
Percent 
Valid Percent
Cumulative  Percent 

Valid do best we can 28 5.1 5.2 5.2 
city/county 115 21.1 21.2 26.4 
dept. support 214 39.3 39.5 65.9 
comb tech and 
best we can 185 34.0 34.1 100.0 
Total 542 99.6 100.0 
Missing System 2 .4 
Total 544 100.0 

Problems in generating quality data? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid major 

108 

19.9 

20.2 

20.2 

minor 

355 

65.3 

66.4 

86.5 

not 

72 

13.2 

13.5 

100.0 

Total 

535 

98.3 

100.0 

Missing System 

9 

1.7 

Total 

544 

100.0 

Problems in generating quality data?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid major 

52 

9.6 

9.8 

9.8 

minor 

322 

59.2 

60.4 

70.2 

not 

159 

29.2 

29.8 

100.0 

Total 

533 

98.0 

100.0 

Missing System 

11 

2.0 

Total 

544 

100.0 

Problems in generating quality data? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid major 

109 

20.0 

20.5 

20.5 

minor 

218 

40.1 

40.9 

61.4 

not 

206 

37.9 

38.6 

100.0 

Total 

533 

98.0 

100.0 

Missing System 

11 

2.0 

Total 

544 

100.0 


Problems in generating quality data? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid major 

62 

11.4 

11.7 

11.7 

minor 

234 

43.0 

44.0 

55.6 

not 

236 

43.4 

44.4 

100.0 

Total 

532 

97.8 

100.0 

Missing System 

12 

2.2 

Total 

544 

100.0 

Problems in generating quality data? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid major 

137 

25.2 

25.8 

25.8 

minor 

207 

38.1 

39.0 

64.8 

not 

187 

34.4 

35.2 

100.0 

Total 

531 

97.6 

100.0 

Missing System 

13 

2.4 

Total 

544 

100.0 

Extent utilized (inmate release?) 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid not utilized 

323 

59.4 

61.4 

61.4 

utilized some 

155 

28.5 

29.5 

90.9 

highly utilized 

48 

8.8 

9.1 

100.0 

Total 

526 

96.7 

100.0 

Missing System 

18 

3.3 

Total 

544 

100.0 

Extent utilized (parole?)  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid not utilized 

297 

54.6 

56.6 

56.6 

utilized some 

173 

31.8 

33.0 

89.5 

highly utilized 

55 

10.1 

10.5 

100.0 

Total 

525 

96.5 

100.0 

Missing System 

19 

3.5 

Total 

544 

100.0 



Extent utilized (probation?)  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid not utilized 

311 

57.2 

59.5 

59.5 

utilized some 

175 

32.2 

33.5 

92.9 

highly utilized 

37 

6.8 

7.1 

100.0 

Total 

523 

96.1 

100.0 

Missing System 

21 

3.9 

Total 

544 

100.0 

Extent utilzed (neighboring police?) 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid not utilized 

193 

35.5 

36.5 

36.5 

utilized some 

271 

49.8 

51.2 

87.7 

highly utilized 

65 

11.9 

12.3 

100.0 

Total 

529 

97.2 

100.0 

Missing System 

15 

2.8 

Total 

544 

100.0 

Extent utilized (state police?) 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid not utilized 

251 

46.1 

48.0 

48.0 

utilized some 

233 

42.8 

44.6 

92.5 

highly utilized 

39 

7.2 

7.5 

100.0 

Total 

523 

96.1 

100.0 

Missing System 

21 

3.9 

Total 

544 

100.0 

Extent utilized (federal law enforcement?)  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid not utilized 

234 

43.0 

44.7 

44.7 

utilized some 

260 

47.8 

49.7 

94.5 

highly utilized 

29 

5.3 

5.5 

100.0 

Total 

523 

96.1 

100.0 

Missing System 

21 

3.9 

Total 

544 

100.0 



Extent utilized (local city sources?)  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid not utilized 

192 

35.3 

36.4 

36.4 

utilized some 

293 

53.9 

55.5 

91.9 

highly utilized 

43 

7.9 

8.1 

100.0 

Total 

528 

97.1 

100.0 

Missing System 

16 

2.9 

Total 

544 

100.0 

Extent utilized (local county sources?)  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid not utilized 

240 

44.1 

45.7 

45.7 

utilized some 

252 

46.3 

48.0 

93.7 

highly utilized 

33 

6.1 

6.3 

100.0 

Total 

525 

96.5 

100.0 

Missing System 

19 

3.5 

Total 

544 

100.0 

Extent utilized (state sources?)  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid not utilized 

316 

58.1 

60.2 

60.2 

utilized some 

194 

35.7 

37.0 

97.1 

highly utilized 

14 

2.6 

2.7 

99.8 

3 

1 

.2 

.2 

100.0 

Total 

525 

96.5 

100.0 

Missing System 

19 

3.5 

Total 

544 

100.0 

Extent utlilized (federal sources?)  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid not utilized 

350 

64.3 

66.7 

66.7 

utilized some 

168 

30.9 

32.0 

98.7 

highly utilized 

7 

1.3 

1.3 

100.0 

Total 

525 

96.5 

100.0 

Missing System 

19 

3.5 

Total 

544 

100.0 



Extent utilized (courts?) 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid not utilized 

205 

37.7 

38.8 

38.8 

utilized some 

271 

49.8 

51.3 

90.2 

highly utilized 

52 

9.6 

9.8 

100.0 

Total 

528 

97.1 

100.0 

Missing System 

16 

2.9 

Total 

544 

100.0 

Extent utilized (state dept. of motor vehicle?)  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid not utilized 

192 

35.3 

36.4 

36.4 

utilized some 

210 

38.6 

39.8 

76.3 

highly utilized 

125 

23.0 

23.7 

100.0 

Total 

527 

96.9 

100.0 

Missing System 

17 

3.1 

Total 

544 

100.0 

How often department uses frequencies? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

87 

16.0 

17.6 

17.6 

sometimes 

138 

25.4 

28.0 

45.6 

often 

149 

27.4 

30.2 

75.9 

very often 

119 

21.9 

24.1 

100.0 

Total 

493 

90.6 

100.0 

Missing System 

51 

9.4 

Total 

544 

100.0 

How often department uses mean, median, mode? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

177 

32.5 

36.4 

36.4 

sometimes 

165 

30.3 

34.0 

70.4 

often 

92 

16.9 

18.9 

89.3 

very often 

52 

9.6 

10.7 

100.0 

Total 

486 

89.3 

100.0 

Missing System 

58 

10.7 

Total 

544 

100.0 



How often departments uses standart deviation? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

246 

45.2 

50.9 

50.9 

sometimes 

154 

28.3 

31.9 

82.8 

often 

59 

10.8 

12.2 

95.0 

very often 

24 

4.4 

5.0 

100.0 

Total 

483 

88.8 

100.0 

Missing System 

61 

11.2 

Total 

544 

100.0 

How often department uses crosstabs?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

288 

52.9 

60.0 

60.0 

sometimes 

117 

21.5 

24.4 

84.4 

often 

55 

10.1 

11.5 

95.8 

very often 

20 

3.7 

4.2 

100.0 

Total 

480 

88.2 

100.0 

Missing System 

64 

11.8 

Total 

544 

100.0 

How often department uses correlation?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

204 

37.5 

42.1 

42.1 

sometimes 

178 

32.7 

36.8 

78.9 

often 

84 

15.4 

17.4 

96.3 

very often 

18 

3.3 

3.7 

100.0 

Total 

484 

89.0 

100.0 

Missing System 

60 

11.0 

Total 

544 

100.0 

How often department uses regression?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

310 

57.0 

64.3 

64.3 

sometimes 

133 

24.4 

27.6 

91.9 

often 

28 

5.1 

5.8 

97.7 

very often 

11 

2.0 

2.3 

100.0 

Total 

482 

88.6 

100.0 

Missing System 

62 

11.4 

Total 

544 

100.0 



How often deparment uses cluster analysis? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

193 

35.5 

39.9 

39.9 

sometimes 

136 

25.0 

28.1 

68.0 

often 

97 

17.8 

20.0 

88.0 

very often 

58 

10.7 

12.0 

100.0 

Total 

484 

89.0 

100.0 

Missing System 

60 

11.0 

Total 

544 

100.0 

How often department undertakes target profile analysis? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

127 

23.3 

25.0 

25.0 

sometimes 

225 

41.4 

44.3 

69.3 

often 

102 

18.8 

20.1 

89.4 

very often 

54 

9.9 

10.6 

100.0 

Total 

508 

93.4 

100.0 

Missing System 

36 

6.6 

Total 

544 

100.0 

How often department undertakes victim analysis? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

152 

27.9 

29.9 

29.9 

sometimes 

267 

49.1 

52.5 

82.3 

often 

72 

13.2 

14.1 

96.5 

very often 

18 

3.3 

3.5 

100.0 

Total 

509 

93.6 

100.0 

Missing System 

35 

6.4 

Total 

544 

100.0 

How often department undertakes link analysis?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

167 

30.7 

33.5 

33.5 

sometimes 

216 

39.7 

43.3 

76.8 

often 

92 

16.9 

18.4 

95.2 

very often 

24 

4.4 

4.8 

100.0 

Total 

499 

91.7 

100.0 

Missing System 

45 

8.3 

Total 

544 

100.0 



How often department undertakes temporal analysis? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

206 

37.9 

41.6 

41.6 

sometimes 

133 

24.4 

26.9 

68.5 

often 

97 

17.8 

19.6 

88.1 

very often 

59 

10.8 

11.9 

100.0 

Total 

495 

91.0 

100.0 

Missing System 

49 

9.0 

Total 

544 

100.0 

How often department undertakes spatial analysis?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

180 

33.1 

36.2 

36.2 

sometimes 

135 

24.8 

27.2 

63.4 

often 

103 

18.9 

20.7 

84.1 

very often 

79 

14.5 

15.9 

100.0 

Total 

497 

91.4 

100.0 

Missing System 

47 

8.6 

Total 

544 

100.0 

How often department undertakes financial analysis? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

190 

34.9 

37.6 

37.6 

sometimes 

237 

43.6 

46.9 

84.6 

often 

60 

11.0 

11.9 

96.4 

very often 

18 

3.3 

3.6 

100.0 

Total 

505 

92.8 

100.0 

Missing System 

39 

7.2 

Total 

544 

100.0 

How often department undertakes flowcharting? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

173 

31.8 

34.2 

34.2 

sometimes 

259 

47.6 

51.2 

85.4 

often 

65 

11.9 

12.8 

98.2 

very often 

9 

1.7 

1.8 

100.0 

Total 

506 

93.0 

100.0 

Missing System 

38 

7.0 

Total 

544 

100.0 



How often department undertakes program evaluation? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

177 

32.5 

35.0 

35.0 

sometimes 

218 

40.1 

43.1 

78.1 

often 

92 

16.9 

18.2 

96.2 

very often 

19 

3.5 

3.8 

100.0 

Total 

506 

93.0 

100.0 

Missing System 

38 

7.0 

Total 

544 

100.0 

How often department undertakes case management? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

88 

16.2 

17.3 

17.3 

sometimes 

186 

34.2 

36.6 

53.9 

often 

160 

29.4 

31.5 

85.4 

very often 

74 

13.6 

14.6 

100.0 

Total 

508 

93.4 

100.0 

Missing System 

36 

6.6 

Total 

544 

100.0 

How often department undertakes crime scene profiling?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

167 

30.7 

33.1 

33.1 

sometimes 

253 

46.5 

50.1 

83.2 

often 

66 

12.1 

13.1 

96.2 

very often 

19 

3.5 

3.8 

100.0 

Total 

505 

92.8 

100.0 

Missing System 

39 

7.2 

Total 

544 

100.0 

How often does department undertake crime forcasting?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

157 

28.9 

30.9 

30.9 

sometimes 

241 

44.3 

47.4 

78.3 

often 

81 

14.9 

15.9 

94.3 

very often 

29 

5.3 

5.7 

100.0 

Total 

508 

93.4 

100.0 

Missing System 

36 

6.6 

Total 

544 

100.0 



How ofen department undertakes crime trends? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

58 

10.7 

11.2 

11.2 

sometimes 

191 

35.1 

36.9 

48.1 

often 

178 

32.7 

34.4 

82.4 

very often 

91 

16.7 

17.6 

100.0 

Total 

518 

95.2 

100.0 

Missing System 

26 

4.8 

Total 

544 

100.0 

How often department undertakes citizen surveys? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

178 

32.7 

35.0 

35.0 

sometimes 

260 

47.8 

51.1 

86.1 

often 

52 

9.6 

10.2 

96.3 

very often 

19 

3.5 

3.7 

100.0 

Total 

509 

93.6 

100.0 

Missing System 

35 

6.4 

Total 

544 

100.0 

How often department undertakes victim surveys?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

243 

44.7 

48.3 

48.3 

sometimes 

216 

39.7 

42.9 

91.3 

often 

31 

5.7 

6.2 

97.4 

very often 

13 

2.4 

2.6 

100.0 

Total 

503 

92.5 

100.0 

Missing System 

41 

7.5 

Total 

544 

100.0 

How often department undertakes employee surveys? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

246 

45.2 

48.7 

48.7 

sometimes 

226 

41.5 

44.8 

93.5 

often 

27 

5.0 

5.3 

98.8 

very often 

6 

1.1 

1.2 

100.0 

Total 

505 

92.8 

100.0 

Missing System 

39 

7.2 

Total 

544 

100.0 



How often department undertakes enviromental surveys? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

366 

67.3 

72.6 

72.6 

sometimes 

123 

22.6 

24.4 

97.0 

often 

9 

1.7 

1.8 

98.8 

very often 

6 

1.1 

1.2 

100.0 

Total 

504 

92.6 

100.0 

Missing System 

40 

7.4 

Total 

544 

100.0 

How often department undertakes intelligence analysis?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

103 

18.9 

20.3 

20.3 

sometimes 

253 

46.5 

49.8 

70.1 

often 

105 

19.3 

20.7 

90.7 

very often 

47 

8.6 

9.3 

100.0 

Total 

508 

93.4 

100.0 

Missing System 

36 

6.6 

Total 

544 

100.0 

How often department undertakes productivity analysis?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

174 

32.0 

34.1 

34.1 

sometimes 

229 

42.1 

44.9 

79.0 

often 

75 

13.8 

14.7 

93.7 

very often 

32 

5.9 

6.3 

100.0 

Total 

510 

93.8 

100.0 

Missing System 

34 

6.3 

Total 

544 

100.0 

How often department undertakes civil litigation analysis?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

376 

69.1 

74.8 

74.8 

sometimes 

110 

20.2 

21.9 

96.6 

often 

14 

2.6 

2.8 

99.4 

very often 

3 

.6 

.6 

100.0 

Total 

503 

92.5 

100.0 

Missing System 

41 

7.5 

Total 

544 

100.0 



How often department undertakes patrol strategy analysis? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

113 

20.8 

22.2 

22.2 

sometimes 

235 

43.2 

46.1 

68.2 

often 

127 

23.3 

24.9 

93.1 

very often 

35 

6.4 

6.9 

100.0 

Total 

510 

93.8 

100.0 

Missing System 

34 

6.3 

Total 

544 

100.0 

How often department undertakes workload distribution? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

121 

22.2 

23.7 

23.7 

sometimes 

236 

43.4 

46.2 

69.9 

often 

124 

22.8 

24.3 

94.1 

very often 

30 

5.5 

5.9 

100.0 

Total 

511 

93.9 

100.0 

Missing System 

33 

6.1 

Total 

544 

100.0 

How often department undertakes displacement/diffusion analysis? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid never 

306 

56.3 

61.2 

61.2 

sometimes 

157 

28.9 

31.4 

92.6 

often 

30 

5.5 

6.0 

98.6 

very often 

7 

1.3 

1.4 

100.0 

Total 

500 

91.9 

100.0 

Missing System 

44 

8.1 

Total 

544 

100.0 

Best describe department's utilization of crime totals 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid neither 

8 

1.5 

1.5 

1.5 

count 

174 

32.0 

32.8 

34.3 

analyze 

348 

64.0 

65.7 

100.0 

Total 

530 

97.4 

100.0 

Missing System 

14 

2.6 

Total 

544 

100.0 



Best describe department's utilization of arrest toatls? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid neither 

12 

2.2 

2.3 

2.3 

count 

314 

57.7 

59.4 

61.6 

analyze 

203 

37.3 

38.4 

100.0 

Total 

529 

97.2 

100.0 

Missing System 

15 

2.8 

Total 

544 

100.0 

Best describe department's utilization of clearance rates? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid neither 

42 

7.7 

8.0 

8.0 

count 

310 

57.0 

58.8 

66.8 

analyze 

175 

32.2 

33.2 

100.0 

Total 

527 

96.9 

100.0 

Missing System 

17 

3.1 

Total 

544 

100.0 

Best describe department's utilization of calls for service? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid neither 

26 

4.8 

4.9 

4.9 

count 

208 

38.2 

39.5 

44.4 

analyze 

293 

53.9 

55.6 

100.0 

Total 

527 

96.9 

100.0 

Missing System 

17 

3.1 

Total 

544 

100.0 

Best describe department's utilization of traffic accidents? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid neither 

51 

9.4 

9.8 

9.8 

count 

210 

38.6 

40.2 

50.0 

analyze 

261 

48.0 

50.0 

100.0 

Total 

522 

96.0 

100.0 

Missing System 

22 

4.0 

Total 

544 

100.0 



Best describe department's utilization of citizen complaints? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid neither 

73 

13.4 

14.1 

14.1 

count 

235 

43.2 

45.5 

59.7 

analyze 

208 

38.2 

40.3 

100.0 

Total 

516 

94.9 

100.0 

Missing System 

28 

5.1 

Total 

544 

100.0 

Degree the results of crime analysis efforts utilized by command level mgrs.?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid not utilized 

48 

8.8 

9.2 

9.2 

utilized some 

280 

51.5 

53.9 

63.2 

highly utilized 

191 

35.1 

36.8 

100.0 

Total 

519 

95.4 

100.0 

Missing System 

25 

4.6 

Total 

544 

100.0 

Degree the results of crime analysis efforts utilized by middle mgrs.?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid not utilized 

36 

6.6 

6.9 

6.9 

utilized some 

307 

56.4 

58.9 

65.8 

highly utilized 

178 

32.7 

34.2 

100.0 

Total 

521 

95.8 

100.0 

Missing System 

23 

4.2 

Total 

544 

100.0 

Degree the results of crime analysis efforts utilized by first-line mgrs.? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid not utilized 

60 

11.0 

11.5 

11.5 

utilized some 

316 

58.1 

60.5 

72.0 

highly utilized 

146 

26.8 

28.0 

100.0 

Total 

522 

96.0 

100.0 

Missing System 

22 

4.0 

Total 

544 

100.0 



Degree the results of crime analysis efforts utilized by detectives? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid not utilized 

53 

9.7 

10.2 

10.2 

utilized some 

283 

52.0 

54.3 

64.5 

highly utilized 

185 

34.0 

35.5 

100.0 

Total 

521 

95.8 

100.0 

Missing System 

23 

4.2 

Total 

544 

100.0 

Degree the results of crime analysis efforts utilized by patrol officers? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid not utilized 

83 

15.3 

16.0 

16.0 

utilized some 

326 

59.9 

62.9 

79.0 

highly utilized 

109 

20.0 

21.0 

100.0 

Total 

518 

95.2 

100.0 

Missing System 

26 

4.8 

Total 

544 

100.0 

Degree the results of crime analysis efforts utilized by specialized unit officers? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid not utilized 

72 

13.2 

14.0 

14.0 

utilized some 

293 

53.9 

56.9 

70.9 

highly utilized 

150 

27.6 

29.1 

100.0 

Total 

515 

94.7 

100.0 

Missing System 

29 

5.3 

Total 

544 

100.0 

Degree the results of crime analysis efforts utilized by traffic officers?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid not utilized 

180 

33.1 

35.4 

35.4 

utilized some 

282 

51.8 

55.5 

90.9 

highly utilized 

46 

8.5 

9.1 

100.0 

Total 

508 

93.4 

100.0 

Missing System 

36 

6.6 

Total 

544 

100.0 



Degree the results of crime analysis efforts utilized by training unit? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid not utilized 

241 

44.3 

47.5 

47.5 

utilized some 

241 

44.3 

47.5 

95.1 

highly utilized 

25 

4.6 

4.9 

100.0 

Total 

507 

93.2 

100.0 

Missing System 

37 

6.8 

Total 

544 

100.0 

Degree the results of crime analysis efforts utilized by elected officials?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid not utilized 

143 

26.3 

29.1 

29.1 

utilized some 

307 

56.4 

62.5 

91.6 

highly utilized 

41 

7.5 

8.4 

100.0 

Total 

491 

90.3 

100.0 

Missing System 

53 

9.7 

Total 

544 

100.0 

Degree the results of crime analysis efforts utilized by community at large?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid not utilized 

154 

28.3 

31.4 

31.4 

utilized some 

288 

52.9 

58.7 

90.0 

highly utilized 

49 

9.0 

10.0 

100.0 

Total 

491 

90.3 

100.0 

Missing System 

53 

9.7 

Total 

544 

100.0 

Degree the results of crime analysis efforts utilized by outside law enforcement? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid not utilized 

145 

26.7 

29.5 

29.5 

utilized some 

294 

54.0 

59.8 

89.2 

highly utilized 

53 

9.7 

10.8 

100.0 

Total 

492 

90.4 

100.0 

Missing System 

52 

9.6 

Total 

544 

100.0 



Degree the results of crime analysis efforts utilized by prosecutors?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid not utilized 

261 

48.0 

53.5 

53.5 

utilized some 

203 

37.3 

41.6 

95.1 

highly utilized 

24 

4.4 

4.9 

100.0 

Total 

488 

89.7 

100.0 

Missing System 

56 

10.3 

Total 

544 

100.0 

Spend too much time counting crime and not enough time analyzing it? 
 
Frequency -Percent -Valid Percent -Cumulative Percent 
Valid strongly agree 123 22.6 23.6 23.6 
agree 225 41.4 43.1 66.7 
neutral 73 13.4 14.0 80.7 
disagree 79 14.5 15.1 95.8 
strongly disagree 22 4.0 4.2 100.0 
Total 522 96.0 100.0 
Missing System 22 4.0 
Total 544 100.0 

Spend too much time producing administrative reports and not enough time analyzing 
crime? 

Frequency -Percent -Valid Percent -Cumulative Percent 
Valid strongly agree 83 15.3 15.9 15.9 
agree 202 37.1 38.6 54.5 
neutral 121 22.2 23.1 77.6 
disagree 108 19.9 20.7 98.3 
strongly disagree 9 1.7 1.7 100.0 
Total 523 96.1 100.0 
Missing System 21 3.9 
Total 544 100.0 

Spend too much time constructing notices, alerts, etc. and not enough time analyzing 
crime? 

Frequency -Percent -Valid Percent -Cumulative Percent 
Valid strongly agree 34 6.3 6.5 6.5 
agree 102 18.8 19.6 26.1 
neutral 188 34.6 36.1 62.2 
disagree 172 31.6 33.0 95.2 
strongly disagree 25 4.6 4.8 100.0 
Total 521 95.8 100.0 
Missing System 23 4.2 
Total 544 100.0 


Command staff appreciate calue of crime analysis? 

Frequency- Percent -Valid Percent -Cumulative Percent 
Valid strongly disagree 21 3.9 4.0 4.0 
disagree 71 13.1 13.7 17.7 
neutral 101 18.6 19.4 37.1 
agree 257 47.2 49.4 86.5 
strongly agree 70 12.9 13.5 100.0 
Total 520 95.6 100.0 
Missing System 24 4.4 
Total 544 100.0 

Middle managers appreciate value of crime analysis? 

Frequency -Percent -Valid Percent-Cumulative Percent 
Valid strongly disagree 13 2.4 2.5 2.5 
disagree 83 15.3 15.9 18.4 
neutral 104 19.1 19.9 38.2 
agree 255 46.9 48.8 87.0 
stongly agree 68 12.5 13.0 100.0 
Total 523 96.1 100.0 
Missing System 21 3.9 
Total 544 100.0 

Patrol oficers appreciate value of crime analysis? 

Frequency -Percent -Valid Percent -Cumulative Percent 
Valid strongly disagree 21 3.9 4.0 4.0 
disagree 103 18.9 19.7 23.7 
neutral 152 27.9 29.1 52.8 
agree 204 37.5 39.0 91.8 
strongly agree 43 7.9 8.2 100.0 
Total 523 96.1 100.0 
Missing System 21 3.9 
Total 544 100.0 

Detectives appreciate value of crime analysis? 

Frequency -Percent -Valid Percent -Cumulative Percent 
Valid strongly disagree 8 1.5 1.5 1.5 
disagree 61 11.2 11.8 13.3 
neutral 83 15.3 16.0 29.3 
agree 286 52.6 55.1 84.4 
strongly agree 81 14.9 15.6 100.0 
Total 519 95.4 100.0 
Missing System 25 4.6 
Total 544 100.0 


Technical support for crime analysis is substandard? 

Frequency -Percent -Valid Percent -Cumulative Percent 
Valid strongly agree 114 21.0 21.8 21.8 
agree 156 28.7 29.9 51.7 
neutral 109 20.0 20.9 72.6 
disagree 117 21.5 22.4 95.0 
strongly disagree 26 4.8 5.0 100.0 
Total 522 96.0 100.0 
Missing System 22 4.0 
Total 544 100.0 

Computer software used for crime analysis is inferior? 

Frequency -Percent -Valid Percent -Cumulative Percent 
Valid strongly agree 99 18.2 19.0 19.0 
agree 123 22.6 23.7 42.7 
neutral 102 18.8 19.6 62.3 
disagree 147 27.0 28.3 90.6 
strongly disagree 49 9.0 9.4 100.0 
Total 520 95.6 100.0 
Missing System 24 4.4 
Total 544 100.0 

Computer hardware used for crime analysis is unacceptably out-of-date? 

Frequency -Percent -Valid Percent -Cumulative Percent 
Valid strongly agree 53 9.7 10.2 10.2 
agree 63 11.6 12.1 22.4 
neutral 119 21.9 22.9 45.3 
disagree 176 32.4 33.9 79.2 
strongly disagree 108 19.9 20.8 100.0 
Total 519 95.4 100.0 
Missing System 25 4.6 
Total 544 100.0 

Department provide sufficent incentives to attract good crime analysts? 

Frequency -Percent -Valid Percent -Cumulative Percent 
Valid strongly disagree 100 18.4 19.2 19.2 
disagree 155 28.5 29.8 49.0 
neutral 168 30.9 32.3 81.3 
agree 82 15.1 15.8 97.1 
strongly agree 15 2.8 2.9 100.0 
Total 520 95.6 100.0 
Missing System 24 4.4 
Total 544 100.0 


Department provides sufficient incentives to retain good crime analysts? 

Frequency -Percent -Valid Percent-Cumulative Percent 
Valid strongly disagree 99 18.2 19.1 19.1 
disagree 142 26.1 27.5 46.6 
neutral 179 32.9 34.6 81.2 
agree 83 15.3 16.1 97.3 
strongly agree 14 2.6 2.7 100.0 
Total 517 95.0 100.0 
Missing System 27 5.0 
Total 544 100.0 

There are effective feedback channels in the deaprtment to inform crime analysts 
about how useful (or not) their products are to field officers? 

Frequency -Percent -Valid Percent -Cumulative Percent 
Valid strongly disagree 60 11.0 11.6 11.6 
disagree 167 30.7 32.4 44.0 
neutral 157 28.9 30.4 74.4 
agree 119 21.9 23.1 97.5 
strongly agree 13 2.4 2.5 100.0 
Total 516 94.9 100.0 
Missing System 28 5.1 
Total 544 100.0 


COPS Crime Analysis USAPG 00-30 

Phone Questionnaire 

Question # 1 Page # 1 
Hello, my name is ____________ and I'm calling from the University of South Alabama on 
behalf of the Department of Justice COPS Office (Community Oriented Policing). Could I 
please speak with Title? 

If asked about purpose of call, say: We are gathering information for COPS regarding 
crime analysis activities of law enforcement agencies around the nation. The 
Department has been selected to participate in the study and your Title has been 
identified as the person I need to talk to. 

If Title is not available, ask: Is the person second in command under the Title 

available? If not, ask: When would be a good time to call back to talk to the Title
?
Once the appropriate person is on the phone, say:
Hello, my name is ____________ and I'm calling from the University of South Alabama on

behalf of the Department of Justice COPS Office. We're conducting a brief survey regarding 
crime analysis activities of law enforcement agencies around the nation. Your responses are 
fully confidential. We will not release any information that would allow you or your 
department to be identified. The survey should take less than 10 minutes. 

TEMPORARY QUESTION 
-Check List-(Number of items: 1 Min: 1 Max: 1) 
1 Start 

Question # 2 Page # 2 

Is there anyone in your department whose primary job responsibility is crime analysis? 

-Check List-(Number of items: 3 Min: 1 Max: 1) 
1 Yes 
2 No 
3 DK/NA 

SKIPS from Q2
IF q2 NE 1 SKIP TO: 
4


Question # 3 Page # 3 

Are any of the persons responsible for crime analysis civilian employee? 

-Check List-(Number of items: 3 Min: 1 Max: 1) 
1 Yes 
2 No 
3 DK/NA 

Question # 4 Page # 4 

Does your department have at least one computer? 

-Check List-(Number of items: 3 Min: 1 Max: 1) 
1 Yes 
2 No 
3 DK/NA 

SKIPS from Q4
IF q4 NE 1 SKIP TO: 25

Question # 5 Page # 5 

Does your department have at least one computer with access to the Internet? 

-Check List-(Number of items: 3 Min: 1 Max: 1) 
1 Yes 
2 No 
3 DK/NA 

SKIPS from Q5
IF q5 NE 1 SKIP TO: 
9

Question # 6 Page # 6 

Does your department have an official web page? 

-Check List-(Number of items: 3 Min: 1 Max: 1) 
1 Yes 
2 No 
3 DK/NA 

SKIPS from Q6
IF q6 NE 1 SKIP TO: 
9


Question # 7 Page # 7 

Do you provide crime statistics on the web for the public to view? 

-Check List-(Number of items: 3 Min: 1 Max: 1) 
1 Yes 
2 No 
3 DK/NA 

Question # 8 Page # 8 

Do you provide a means for the public to communicate to the department via the web? 

-Check List-(Number of items: 3 Min: 1 Max: 1) 
1 Yes 
2 No 
3 DK/NA 

Question # 9 Page # 9 

For how many employees does your department provide E-mail accounts? Would that be 

. . . (read responses) 

-Check List-(Number of items: 5 Min: 1 Max: 1) 
1 None 
2 Some 
3 Most 
4 All 
5 DK/NA 

Question # 10 Page # 10 
How many police reports are entered into your department's computer? Would that be . . 
. (read responses) 

-Check List-(Number of items: 5 Min: 1 Max: 1) 
1 None 
2 Some 

3 Most 

4 All 
5 DK/NA 

SKIPS from Q10
IF q10=[1,5] SKIP TO: 12

Question # 11 Page # 11 

Which of the following reports and information are entered into the department's 
computer? (read responses; mark all that apply) 


-Check List-(Number of items: 13 Min: 1 Max: 12) 
1 Initial case reports 
2 Arrest reports 
3 Traffic accident reports 
4 Intelligence reports 
5 Field interview reports 
6 Vice case reports 
7 Calls for service 
8 Investigative reports 
9 Evidence reports 
10 Pawn shop file 
11 Nickname file 
12 MO files 
13 None 

Question # 12 Page # 12 

Is your department connected to a state or regional violent crimes information database? 

-Check List-(Number of items: 3 Min: 1 Max: 1) 
1 Yes 
2 No 
3 DK/NA 

Question # 13 Page # 13 

Does your department participate in a formal regional information-sharing program? 

-Check List-(Number of items: 3 Min: 1 Max: 1) 
1 Yes 
2 No 

3 
DK/NA 

Question # 14 Page # 14 
Is your department a member of a formal Integrated Justice Information System (IJIS)? 

-Check List-(Number of items: 3 Min: 1 Max: 1) 

1 
Yes 

2 
No 

3 
DK/NA 
Question # 15 Page # 15 

Does your department use crime mapping software?
If yes, ask: What is the name of the software? (Type answer in the space provided. If "Don't
know" type DK)

-Check List Open-(Number of items: 3 Min: 1 Max: 1) 
1 No 
2 DK/NA 


3 Yes 

SKIPS from Q15
IF q15 NE 1 SKIP TO: 20

Question # 16 Page # 16 
Which of the following types of data does your department geocode and map? (read 
responses, mark all that apply) 

(Part I crimes include homicides, rape, aggravated assault, burglary, theft, and vehicle theft) 

-Check List Open-(Number of items: 8 Min: 1 Max: 7) 
1 Part I crimes 
2 Part II crimes (all other crimes) 
3 Arrests 
4 Calls for service 
5 Corrections 
6 Traffic 
7 None 
8 Any other types of data? 

Question # 17 Page # 17 

Are citizens allowed to view crime maps? 

-Check List-(Number of items: 3 Min: 1 Max: 1) 
1 Yes 
2 No 
3 DK/NA 

Question # 18 Page # 18 
Who is allowed to generate crime maps? (read responses) 

-Check List-(Number of items: 3 Min: 1 Max: 1) 
1 Only designated unit personnel 
2 Any sworn officer 
3 DK/NA 

Question # 19 Page # 19 
Do you use "hotspot" software such as STAC (pronounced: "stack")? 
If yes, ask: What is the name of the software? (Type answer in the space provided. If "Don't 
know", type DK) 

-Check List Open-(Number of items: 3 Min: 1 Max: 1) 
1 No 
2 DK/NA 

3 Yes 

Question # 20 Page # 20 


Does your department have a formal records management system? 

-Check List-(Number of items: 3 Min: 1 Max: 1) 
1 Yes 
2 No 
3 DK/NA 

SKIPS from Q20
IF q20 NE 1 SKIP TO: 23

Question # 21 Page # 21 

Which of the following best describes the source of your system? (read responses)
If it is a commercial software package, ask: What is the name of the package? (Type answer
in the space provided. If "Don't know", type DK)

-Check List Open-(Number of items: 5 Min: 1 Max: 1)
1 Provided by city or county MIS
2 Built by department personnel
3 Built by outside consultant
4 DK/NA
5 Commercial software package

Question # 22 Page # 22 
How would you rate your department's records management system? (read responses) 

-Check List-(Number of items: 5 Min: 1 Max: 1) 
1 Excellent 
2 Good 
3 Only fair 
4 Poor 
5 DK/NA 

Question # 23 Page # 23 

Please tell me how much you agree or disagree with this statement. 

The computer software we use for crime analysis is inferior. Do you strongly agree, 
agree, disagree, or strongly disagree? 

-Check List-(Number of items: 7 Min: 1 Max: 1) 
Strongly agree 
Agree 
Disagree 
Strongly Disagree 
Neither/neutral 


6 Don't do Computer Crime Analysis 
7 DK/NA 

Question # 24 Page # 24 
What about this statement: The computer hardware we use for crime analysis is unacceptably 

out-of-date. Do you strongly agree, agree, disagree, or strongly disagree? 

-Check List-(Number of items: 7 Min: 1 Max: 1) 
1 Strongly agree 
2 Agree 
3 Disagree 
4 Strongly Disagree 
5 Neither/neutral 
6 Don't do Computer Crime Analysis 
7 DK/NA 

Question # 25 Page # 25 

The next few questions deal with the training of individuals in your department who are 
responsible for crime analysis. Please tell me whether individuals in your department 
receive either initial or in-service training in either of the following areas: (read responses; 
mark all that apply) 

-Check List-(Number of items: 4 Min: 1 Max: 2) 
1 Crime analysis 
2 Report writing 
3 Neither 
4 DK/NA 

SKIPS from Q25
IF q4 NE 1 SKIP TO: 27

Question # 26 Page # 26 
What about training in these areas? (read responses; mark all that apply) 

-Check List-(Number of items: 6 Min: 1 Max: 5) 
1 GIS mapping 
2 Computer hardware 
3 Computer software 
4 Statistical analysis 
5 Graphics 
6 None 

Question # 27 Page # 27 

A few departments may conduct very sophisticated types of crime analysis. Please tell 
me whether or not your department conducts the following types? (read responses; mark 
all that apply) 


-Check List-(Number of items: 12 Min: 1 Max: 11) 
1 Target profile analysis 
2 Victim analysis 
3 Link analysis 
4 Temporal analysis, dealing with times when crimes occur 
5 Spatial analysis, dealing with places where crimes occur 
6 Financial analysis 
7 Intelligence analysis 
8 Productivity analysis 
9 Civil litigation analysis 
10 Patrol strategy analysis 
11 Displacement and diffusion analysis 
12 None 

Question # 28 Page # 28 

Please tell me whether or not your department conducts the following types of research? 

(read responses; mark all that apply) 

-Check List-(Number of items: 8 Min: 1 Max: 7) 
1 Flowcharting 
2 Program evaluation 
3 Case management 
4 Crime scene profiling 
5 Crime forecasting 
6 Crime trends 
7 Workload distribution 
8 None 

Question # 29 Page # 29 

Please tell me whether or not your department conducts the following types of surveys? 

(read responses; mark all that apply) 

-Check List-(Number of items: 5 Min: 1 Max: 4) 
1 Citizen surveys 
2 Victim surveys 
3 Employee surveys 
4 Environmental surveys 
5 None 

Question # 30 Page # 30 
Next I'm going to give you two terms that describe how law enforcement agencies might 

utilize certain types of data. The terms are "count" and "analyze". "Count" means basically 

keeping track of the number of occurrences. "Analyze" means looking for trends and 

relationships in the data. Please tell me which of the terms best describes your department's 


utilization of crime totals such as numbers of robberies, number of thefts, etc. Do you count or 

analyze crime totals, or do you not utilize that type data? 

-Check List-(Number of items: 4 Min: 1 Max: 1) 
1 Count 
2 Analyze 
3 Not utilize 
4 DK/NA 

Question # 31 Page # 31 

What about arrest totals? Do you count, analyze, or not utilize arrest totals? 

-Check List-(Number of items: 4 Min: 1 Max: 1) 
1 Count 
2 Analyze 
3 Not utilize 
4 DK/NA 

Question # 32 Page # 32 

What about clearance rates? 

-Check List-(Number of items: 4 Min: 1 Max: 1) 
1 Count 
2 Analyze 
3 Not utilize 
4 DK/NA 

Question # 33 Page # 33 

Calls for service 

-Check List-(Number of items: 4 Min: 1 Max: 1) 
1 Count 
2 Analyze 
3 Not utilize 
4 DK/NA 

Question # 34 Page # 34 

Traffic accidents 

-Check List-(Number of items: 4 Min: 1 Max: 1) 
1 Count 
2 Analyze 
3 Not utilize 


4 DK/NA 

Question # 35 Page # 35 

Citizen complaints 

-Check List-(Number of items: 4 Min: 1 Max: 1) 
1 Count 
2 Analyze 
3 Not utilize 
4 DK/NA 

Question # 36 Page # 36 

Please tell me whether or not the results of your crime analysis efforts are utilized by 
each of the following? (read first 5 responses; mark all that apply) 

-Check List Open-(Number of items: 7 Min: 1 Max: 5) 
1 Elected officials 
2 Community at large 
3 Outside law enforcement 
4 Prosecutors 
5 None 
6 Don't do Crime Analysis 
7 Are results used by any others? 

Question # 37 Page # 37 
Does your department use a computer-aided dispatch system (CAD) ? 

-Check List-(Number of items: 3 Min: 1 Max: 1) 
1 Yes 
2 No 
3 DK/NA 

Question # 38 Page # 38 

Please tell me the extent to which you agree or disagree with the following statement. 

We spend too much time counting crime and not enough time analyzing it. Do you 
strongly agree, agree, disagree, or strongly disagree? 

-Check List-(Number of items: 6 Min: 1 Max: 1) 
1 Strongly agree 
2 Agree 
3 Disagree 
4 Strongly Disagree 
5 Neither/neutral 
6 DK/NA 


Question # 39 Page # 39 
On a scale of 0 to 10 with zero being no effort to 10 being virtually perfect, how would you 

rate your department's overall crime analysis effort? 

-Check List-(Number of items: 12 Min: 1 Max: 1) 
10 
21 
32 
43 
54 
65 
76 
87 
98 
10 9 
11 10 
12 DK/NA 

Question # 40 Page # 40 

What is the population of the jurisdiction your department serves? (If DK/NA, prompt: Is 

that under 10,000; Over 25, 000; etc.?) 

-Check List-(Number of items: 6 Min: 1 Max: 1) 
1 Under 5,000 
2 5-9,999 
3 10-24,999 
4 25-49,999 
5 50,000 or more 
6 DK/NA 

Question # 41 Page # 41 

What is your formal job title? 

-Check List Open-(Number of items: 7 Min: 1 Max: 1) 
Chief of Police 
Deputy Chief of Police 
Sheriff 
Chief Deputy Sheriff 
Crime Analyst 


6 DK/NA 
7 Other 

SKIPS from Q41
IF q40=[3,4,5] SKIP TO: 43

Question # 42 Page # 42 
Finally, I need to know the total number of Part I crimes which occurred in your jurisdiction in 

1999. If this information is not readily available, I can fax you a form to fill out and fax back 

to us. Would you like to handle it that way? 

-Check List Open-(Number of items: 2 Min: 1 Max: 1) 
0 Faxed 
1 Number of Part 1 Crimes 

Question # 43 Page # 43 
That completes the survey. Thanks for your time; have a nice day, etc. 

Enter the name of department, the city, and the state. Enter EXACTLY as they appear in the
Call Window.

-Dbase-(Number of items: 6)
Text:
Name of Department
Text:
City
Text:
State

Question # 44 Page # 44 
Enter crime index total from master data list: 

-Dbase-(Number of items: 2) 
Crime index total 
Integer: 0 _ i _ 15,000 


Telephone Survey Frequencies 
Frequency Table 

Is there anyone in your department whose primary job responsibility is 
crime analysis? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid Yes 

194 

23.8 

23.8 

23.8 

No 

620 

76.2 

76.2 

100.0 

Total 

814 

100.0 

100.0 

Are any of the persons responsible for crime analysis civilian employees? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid Yes 

94 

11.5 

48.5 

48.5 

No 

100 

12.3 

51.5 

100.0 

Total 

194 

23.8 

100.0 

Missing System 

620 

76.2 

Total 

814 

100.0 

Does your department have at least one computer?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

24 

2.9 

2.9 

2.9 

yes 

790 

97.1 

97.1 

100.0 

Total 

814 

100.0 

100.0 

Does your department have at least one computer with access to the
Internet?

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

92 

11.3 

11.6 

11.6 

yes 

698 

85.7 

88.4 

100.0 

Total 

790 

97.1 

100.0 

Missing System 

24 

2.9 

Total 

814 

100.0 

Does your department have an official web page? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

323 

39.7 

46.7 

yes 

369 

45.3 

53.3 

100.0 

Total 

692 

85.0 

100.0 

Missing System 

122 

15.0 

Total 

814 

100.0 



Do you provide crime statistics on the web for the public to view? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

235 

28.9 

67.0 

yes 

116 

14.3 

33.0 

100.0 

Total 

351 

43.1 

100.0 

Missing System 

463 

56.9 

Total 

814 

100.0 

Do you provide a means for the public to communicate with the department 
via the web?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

57 

7.0 

15.9 

15.9 

yes 

301 

37.0 

84.1 

100.0 

Total 

358 

44.0 

100.0 

Missing System 

456 

56.0 

Total 

814 

100.0 

For how many employees does your department provide E-mail accounts? 

Frequency -Percent -Valid Percent -Cumulative Percent 
Valid none 207 25.4 26.2 26.2 
some 337 41.4 42.7 68.9 
most 73 9.0 9.2 78.1 
all 170 20.9 21.5 99.6 
DK/NA 3 .4 .4 100.0 
Total 790 97.1 100.0 
Missing System 24 2.9 
Total 814 100.0 

How many police reports are entered into your department's computer? 

Frequency -Percent -Valid Percent-Cumulative Percent 
Valid none 63 7.7 8.0 8.0 
some 29 3.6 3.7 11.6 
most 87 10.7 11.0 22.7 
all 610 74.9 77.2 99.9 
DK/NA 1 .1 .1 100.0 
Total 790 97.1 100.0 
Missing System 24 2.9 
Total 814 100.0 



Are the initial case reports entered? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

36 

4.4 

5.0 

yes 

690 

84.8 

95.0 

100.0 

Total 

726 

89.2 

100.0 

Missing System 

88 

10.8 

Total 

814 

100.0 

Are the arrest reports entered? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

33 

4.1 

4.5 

4.5 

yes 

693 

85.1 

95.5 

100.0 

Total 

726 

89.2 

100.0 

Missing System 

88 

10.8 

Total 

814 

100.0 

Are traffic accident reports entered?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

207 

25.4 

28.5 

28.5 

yes 

519 

63.8 

71.5 

100.0 

Total 

726 

89.2 

100.0 

Missing System 

88 

10.8 

Total 

814 

100.0 

Are intelligence reports entered? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

297 

36.5 

40.9 

40.9 

yes 

429 

52.7 

59.1 

100.0 

Total 

726 

89.2 

100.0 

Missing System 

88 

10.8 

Total 

814 

100.0 

Are field interview reports entered? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

328 

40.3 

45.2 

yes 

398 

48.9 

54.8 

100.0 

Total 

726 

89.2 

100.0 

Missing System 

88 

10.8 

Total 

814 

100.0 



Are vice case reports entered? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

260 

31.9 

35.8 

yes 

466 

57.2 

64.2 

100.0 

Total 

726 

89.2 

100.0 

Missing System 

88 

10.8 

Total 

814 

100.0 

Are calls for service entered? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

119 

14.6 

16.4 

16.4 

yes 

607 

74.6 

83.6 

100.0 

Total 

726 

89.2 

100.0 

Missing System 

88 

10.8 

Total 

814 

100.0 

Are investigative reports entered? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

80 

9.8 

11.0 

11.0 

yes 

646 

79.4 

89.0 

100.0 

Total 

726 

89.2 

100.0 

Missing System 

88 

10.8 

Total 

814 

100.0 

Are evidence reports entered? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

139 

17.1 

19.1 

19.1 

yes 

587 

72.1 

80.9 

100.0 

Total 

726 

89.2 

100.0 

Missing System 

88 

10.8 

Total 

814 

100.0 

Are pawn shop files entered? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

478 

58.7 

65.8 

yes 

248 

30.5 

34.2 

100.0 

Total 

726 

89.2 

100.0 

Missing System 

88 

10.8 

Total 

814 

100.0 



Are nickname files entered?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

289 

35.5 

39.8 

yes 

437 

53.7 

60.2 

100.0 

Total 

726 

89.2 

100.0 

Missing System 

88 

10.8 

Total 

814 

100.0 

Are MO files entered? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

410 

50.4 

56.5 

56.5 

yes 

316 

38.8 

43.5 

100.0 

Total 

726 

89.2 

100.0 

Missing System 

88 

10.8 

Total 

814 

100.0 

No reports are entered. 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

720 

88.5 

99.2 

99.2 

yes 

6 

.7 

.8 

100.0 

Total 

726 

89.2 

100.0 

Missing System 

88 

10.8 

Total 

814 

100.0 

Is your department connected to a state or regional violent crimes 
information database?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

351 

43.1 

46.2 

46.2 

yes 

409 

50.2 

53.8 

100.0 

Total 

760 

93.4 

100.0 

Missing System 

54 

6.6 

Total 

814 

100.0 

Does your department participate in a formal regional information-sharing 
program? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

244 

30.0 

31.7 

yes 

525 

64.5 

68.3 

100.0 

Total 

769 

94.5 

100.0 

Missing System 

45 

5.5 

Total 

814 

100.0 



Is your department a member of a formal Integrated Justice Information
System (IJIS)?

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

374 

45.9 

53.4 

yes 

326 

40.0 

46.6 

100.0 

Total 

700 

86.0 

100.0 

Missing System 

114 

14.0 

Total 

814 

100.0 

Does your department use crime mapping software? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

648 

79.6 

82.0 

82.0 

DK/NA 

23 

2.8 

2.9 

84.9 

yes 

119 

14.6 

15.1 

100.0 

Total 

790 

97.1 

100.0 

Missing System 

24 

2.9 

Total 

814 

100.0 

Are part I crimes mapped? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

14 

1.7 

11.8 

11.8 

yes 

105 

12.9 

88.2 

100.0 

Total 

119 

14.6 

100.0 

Missing System 

695 

85.4 

Total 

814 

100.0 

Are part II crimes mapped? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

31 

3.8 

26.1 

26.1 

yes 

88 

10.8 

73.9 

100.0 

Total 

119 

14.6 

100.0 

Missing System 

695 

85.4 

Total 

814 

100.0 

Are arrests mapped? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

46 

5.7 

38.7 

yes 

73 

9.0 

61.3 

100.0 

Total 

119 

14.6 

100.0 

Missing System 

695 

85.4 

Total 

814 

100.0 



Are calls for service mapped? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

40 

4.9 

33.6 

yes 

79 

9.7 

66.4 

100.0 

Total 

119 

14.6 

100.0 

Missing System 

695 

85.4 

Total 

814 

100.0 

Are corrections data mapped? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

97 

11.9 

81.5 

81.5 

yes 

22 

2.7 

18.5 

100.0 

Total 

119 

14.6 

100.0 

Missing System 

695 

85.4 

Total 

814 

100.0 

Is traffic data mapped? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

45 

5.5 

37.8 

37.8 

yes 

74 

9.1 

62.2 

100.0 

Total 

119 

14.6 

100.0 

Missing System 

695 

85.4 

Total 

814 

100.0 

No data are mapped? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid 0 

113 

13.9 

95.0 

95.0 

1 

6 

.7 

5.0 

100.0 

Total 

119 

14.6 

100.0 

Missing System 

695 

85.4 

Total 

814 

100.0 

Are citizens allowed to view crime maps? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid Yes 

74 

9.1 

62.2 

62.2 

No 

36 

4.4 

30.3 

92.4 

DK/NA 

9 

1.1 

7.6 

100.0 

Total 

119 

14.6 

100.0 

Missing System 

695 

85.4 

Total 

814 

100.0 



Who is allowed to generate crime maps? 

Frequency -Percent -Valid Percent -Cumulative Percent 
Valid Only designated 
unit personnel 88 10.8 73.9 73.9 
Any sworn officer 25 3.1 21.0 95.0 
DK/NA 6 .7 5.0 100.0 
Total 119 14.6 100.0 
Missing System 695 85.4 
Total 814 100.0 

Do you use "hotspot" software such as STAC 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid No 

90 

11.1 

75.6 

75.6 

DK/NA 

21 

2.6 

17.6 

93.3 

Yes 

8 

1.0 

6.7 

100.0 

Total 

119 

14.6 

100.0 

Missing System 

695 

85.4 

Total 

814 

100.0 

Does your department have a formal records management system? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

116 

14.3 

14.8 

14.8 

yes 

667 

81.9 

85.2 

100.0 

Total 

783 

96.2 

100.0 

Missing System 

31 

3.8 

Total 

814 

100.0 

Which of the following best describes the source of your system? 

Frequency -Percent -Valid Percent -Cumulative Percent 
Valid Provided by city or 
county MIS 78 9.6 11.7 11.7 
Built by department 
personnel 126 15.5 18.9 30.6 
Built by outside 
consultant 155 19.0 23.2 53.8 
DK/NA 15 1.8 2.2 56.1 
Commercial 
software package 293 36.0 43.9 100.0 
Total 667 81.9 100.0 
Missing System 147 18.1 
Total 814 100.0 


How would you rate your department's records management system?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid poor 

21 

2.6 

3.2 

3.2 

only fair 

111 

13.6 

16.8 

20.0 

good 

359 

44.1 

54.4 

74.4 

excellent 

169 

20.8 

25.6 

100.0 

Total 

660 

81.1 

100.0 

Missing System 

154 

18.9 

Total 

814 

100.0 

The computer software we use for crime analysis is unacceptably out-of-date. 

Frequency -Percent -Valid Percent -Cumulative Percent 
Valid strongly agree 56 6.9 8.6 8.6 
agree 219 26.9 33.6 42.2 
neutral 52 6.4 8.0 50.2 
disagree 244 30.0 37.4 87.6 
strongly disagree 81 10.0 12.4 100.0 
Total 652 80.1 100.0 
Missing System 162 19.9 
Total 814 100.0 

The computer hardware we use for crime analysis is unacceptably out-of-date. 

Frequency -Percent -Valid Percent -Cumulative Percent 
Valid strongly agree 39 4.8 5.9 5.9 
agree 143 17.6 21.5 27.4 
neutral 28 3.4 4.2 31.6 
disagree 311 38.2 46.8 78.5 
strongly disagree 143 17.6 21.5 100.0 
Total 664 81.6 100.0 
Missing System 150 18.4 
Total 814 100.0 

Does your department provide training for crime analysis? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

399 

49.0 

49.0 

49.0 

yes 

415 

51.0 

51.0 

100.0 

Total 

814 

100.0 

100.0 

Does your department provide training for report writing? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

121 

14.9 

14.9 

14.9 

yes 

693 

85.1 

85.1 

100.0 

Total 

814 

100.0 

100.0 



Does your department provide training for GIS mapping? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

642 

78.9 

81.3 

yes 

148 

18.2 

18.7 

100.0 

Total 

790 

97.1 

100.0 

Missing System 

24 

2.9 

Total 

814 

100.0 

Does your department provide training for computer hardware? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

425 

52.2 

53.8 

53.8 

yes 

365 

44.8 

46.2 

100.0 

Total 

790 

97.1 

100.0 

Missing System 

24 

2.9 

Total 

814 

100.0 

Does your department provide training for computer software? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

284 

34.9 

35.9 

35.9 

yes 

506 

62.2 

64.1 

100.0 

Total 

790 

97.1 

100.0 

Missing System 

24 

2.9 

Total 

814 

100.0 

Does your department provide training for statistical analysis? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

534 

65.6 

67.6 

67.6 

yes 

256 

31.4 

32.4 

100.0 

Total 

790 

97.1 

100.0 

Missing System 

24 

2.9 

Total 

814 

100.0 

Does your department provide training for graphics? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

618 

75.9 

78.2 

yes 

172 

21.1 

21.8 

100.0 

Total 

790 

97.1 

100.0 

Missing System 

24 

2.9 

Total 

814 

100.0 



Do you perform target profile analysis? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

697 

85.6 

85.6 

yes 

117 

14.4 

14.4 

100.0 

Total 

814 

100.0 

100.0 

Do you perform victim analysis?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

620 

76.2 

76.2 

76.2 

yes 

194 

23.8 

23.8 

100.0 

Total 

814 

100.0 

100.0 

Do you perform link analysis? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

663 

81.4 

81.4 

81.4 

yes 

151 

18.6 

18.6 

100.0 

Total 

814 

100.0 

100.0 

Do you perform temporal analysis? 

Frequency 

Percent 

Valid Percent 

Cumulative 
Percent 

Valid no 

475 

58.4 

58.4 

58.4 

yes 

339 

41.6 

41.6 

100.0 

Total 

814 

100.0 

100.0 

Do you perform spatial analysis? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

443 

54.4 

54.4 

54.4 

yes 

371 

45.6 

45.6 

100.0 

Total 

814 

100.0 

100.0 

Do you perform financial analysis?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

679 

83.4 

83.4 

yes 

135 

16.6 

16.6 

100.0 

Total 

814 

100.0 

100.0 


Do you perform intelligence analysis? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

509 

62.5 

62.5 

yes 

305 

37.5 

37.5 

100.0 

Total 

814 

100.0 

100.0 

Do you perform productivity analysis? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

475 

58.4 

58.4 

58.4 

yes 

339 

41.6 

41.6 

100.0 

Total 

814 

100.0 

100.0 

Do you perform civil litigation analysis? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

743 

91.3 

91.3 

91.3 

yes 

71 

8.7 

8.7 

100.0 

Total 

814 

100.0 

100.0 

Do you perform patrol strategy analysis? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

452 

55.5 

55.5 

55.5 

yes 

362 

44.5 

44.5 

100.0 

Total 

814 

100.0 

100.0 

Do you perform displacement and diffusion analysis? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

703 

86.4 

86.4 

86.4 

yes 

111 

13.6 

13.6 

100.0 

Total 

814 

100.0 

100.0 

Do you perform flowcharting? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

606 

74.4 

74.4 

yes 

208 

25.6 

25.6 

100.0 

Total 

814 

100.0 

100.0 



Do you perform program evaluation? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

439 

53.9 

53.9 

yes 

375 

46.1 

46.1 

100.0 

Total 

814 

100.0 

100.0 

Do you engage in case management? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

259 

31.8 

31.8 

31.8 

yes 

555 

68.2 

68.2 

100.0 

Total 

814 

100.0 

100.0 

Do you perform crime scene profiling? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

552 

67.8 

67.8 

67.8 

yes 

262 

32.2 

32.2 

100.0 

Total 

814 

100.0 

100.0 

Do you do crime forecasting? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

657 

80.7 

80.7 

80.7 

yes 

157 

19.3 

19.3 

100.0 

Total 

814 

100.0 

100.0 

Do you analyze crime trends? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

447 

54.9 

54.9 

54.9 

yes 

367 

45.1 

45.1 

100.0 

Total 

814 

100.0 

100.0 

Do you analyze workload distribution? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

410 

50.4 

50.4 

yes 

404 

49.6 

49.6 

100.0 

Total 

814 

100.0 

100.0 


Do you administer citizen surveys? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

380 

46.7 

46.7 

46.7 

yes 

434 

53.3 

53.3 

100.0 

Total 

814 

100.0 

100.0 

Do you administer victim surveys? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

509 

62.5 

62.5 

62.5 

yes 

305 

37.5 

37.5 

100.0 

Total 

814 

100.0 

100.0 

Do you administer employee surveys?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

445 

54.7 

54.7 

54.7 

yes 

369 

45.3 

45.3 

100.0 

Total 

814 

100.0 

100.0 

Do you administer environmental surveys? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

756 

92.9 

92.9 

92.9 

yes 

58 

7.1 

7.1 

100.0 

Total 

814 

100.0 

100.0 

Do you count, analyze, or not utilize arrest totals? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid Not utilize 

84 

10.3 

10.4 

10.4 

Count 

487 

59.8 

60.2 

70.6 

Analyze 

238 

29.2 

29.4 

100.0 

Total 

809 

99.4 

100.0 

Missing System 

5 

.6 

Total 

814 

100.0 

Do you count, analyze, or not utilize arrest totals? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid Not utilize 

55 

6.8 

6.8 

6.8 

Count 

571 

70.1 

70.8 

77.6 

Analyze 

181 

22.2 

22.4 

100.0 

Total 

807 

99.1 

100.0 

Missing System 

7 

.9 

Total 

814 

100.0 


Do you count, analyze, or not utilize clearance rates? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid Not utilize 

130 

16.0 

16.2 

16.2 

Count 

483 

59.3 

60.3 

76.5 

Analyze 

188 

23.1 

23.5 

100.0 

Total 

801 

98.4 

100.0 

Missing System 

13 

1.6 

Total 

814 

100.0 

Do you count, analyze, or not utilize calls for service? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid Not utilize 

67 

8.2 

8.3 

8.3 

Count 

485 

59.6 

60.2 

68.6 

Analyze 

253 

31.1 

31.4 

100.0 

Total 

805 

98.9 

100.0 

Missing System 

9 

1.1 

Total 

814 

100.0 

Do you count, analyze, or not utilize traffic accidents? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid Not utilize 

85 

10.4 

10.6 

10.6 

Count 

421 

51.7 

52.3 

62.9 

Analyze 

299 

36.7 

37.1 

100.0 

Total 

805 

98.9 

100.0 

Missing System 

9 

1.1 

Total 

814 

100.0 

Do you count, analyze, or not utilize citizen complaints? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid Not utilize 

82 

10.1 

10.2 

10.2 

Count 

405 

49.8 

50.5 

60.7 

Analyze 

315 

38.7 

39.3 

100.0 

Total 

802 

98.5 

100.0 

Missing System 

12 

1.5 

Total 

814 

100.0 

Do you provide analytic output to elected officials? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

345 

42.4 

42.4 

42.4 

yes 

469 

57.6 

57.6 

100.0 

Total 

814 

100.0 

100.0 



Do you provide analytic output to the community at large? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

434 

53.3 

53.3 

53.3 

yes 

380 

46.7 

46.7 

100.0 

Total 

814 

100.0 

100.0 

Do you provide analytic output to outside law enforcement? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

395 

48.5 

48.5 

48.5 

yes 

419 

51.5 

51.5 

100.0 

Total 

814 

100.0 

100.0 

Do you provide analytic output to prosecutors? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid no 

433 

53.2 

53.2 

53.2 

yes 

381 

46.8 

46.8 

100.0 

Total 

814 

100.0 

100.0 

Does your department use a computer-aided dispatch system (CAD) ? 

Frequency 

Percent 

Valid Percent 

Cumulative Percent

Valid yes 

501 

61.5 

61.5 

61.5 

no 

298 

36.6 

36.6 

98.2 

DK/NA 

15 

1.8 

1.8 

100.0 

Total 

814 

100.0 

100.0 

We spend too much time counting crime and not enough time analyzing it.  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid Strongly agree 

91 

11.2 

11.2 

11.2 

Agree 

342 

42.0 

42.0 

53.2 

Disagree 

279 

34.3 

34.3 

87.5 

Strongly Disagree 

32 

3.9 

3.9 

91.4 

Neither/neutral 

54 

6.6 

6.6 

98.0 

DK/NA 

16 

2.0 

2.0 

100.0 

Total 

814 

100.0 

100.0 



On a scale of 0 to 10 with zero being no effort to 10 being virtually 
perfect, how would you rate your department's overall crime analytic 
capacity?  

Frequency 

Percent 

Valid Percent 

Cumulative Percent 

Valid 0 

59 

7.2 

7.2 

7.2 

1 

14 

1.7 

1.7 

9.0 

2 

35 

4.3 

4.3 

13.3 

3 

75 

9.2 

9.2 

22.5 

4 

88 

10.8 

10.8 

33.3 

5 

193 

23.7 

23.7 

57.0 

6 

97 

11.9 

11.9 

68.9 

7 

119 

14.6 

14.6 

83.5 

8 

93 

11.4 

11.4 

95.0 

9 

14 

1.7 

1.7 

96.7 

10 

9 

1.1 

1.1 

97.8 

DK/NA 

18 

2.2 

2.2 

100.0 

Total 

814 

100.0 

100.0