CrimeStat IV 

Part I: Program Overview 

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Chapter 1: 

Introduction to CrimeStat IV 

Ned Levine 

Ned Levine & Associates 

Houston, TX 

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Table of Contents 

Uses of Spatial Statistics in Crime Analysis 1.1 

The CrimeStat IV Spatial Statistics Program 1.2 

Input and Output 1.2 

Statistical Routines 1.3 

Program Requirements 1.7 

Required Hardware and Operating System 1.7 

Available RAM Limits the Size of Files 1.7 

Multi-threading 1.8 

Required Software 1.9 

Installing the Program 1.9 

Adding an Item to the Start Menu 1.10 

Adding an Icon to the Desktop 1.10 

Installing the Sample Data Sets 1.11 

Step-by-Step Instructions 1.16 

Options 1.16 

Short Applications 1.16 

Online Help 1.16 

References 1.18 

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Chapter 1: 

Introduction to CrimeStat IV 

CrimeStat7 is a spatial statistics package that can analyze crime incident location data. 

Its purpose is to provide a variety of tools for the spatial analysis of crime incidents or other 

point locations. It is a stand-alone Windows program that can interface with most desktop 

geographic information systems (GIS). It is designed to operate with large crime incident data 

sets collected by metropolitan police departments. However, it can be used for other types of 

applications involving point locations, such as the location of arrests, motor vehicle crashes, 

emergency medical service pickups, or facilities (e.g., police stations). 

Uses of Spatial Statistics in Crime Analysis 

Most GIS packages, such as MapInfo7 , ArcGIS7 , and Maptitude7 , have very sophisticated 

data base operations (Pitney Bowes, 2012; ESRI, 2012a; Caliper, 2012). They have limited 

statistical methods, however, though this has been slowly changing over time as the programs 

have added various statistical functions. For most purposes, GIS can provide great utility for 

crime analysis, allowing the plotting of different incident locations and the ability to select 

subsets of the data (e.g., incidents by precinct, incidents by time of day). Most crime analysts 

visually inspect incident maps and, based on their experience, draw conclusions about shifts over 

time, >hot spots= and other patterns suggested by the data. 

There are times, however, when a more quantitative approach is needed. For example, 

an analyst wishing to examine patterns of streets robberies over time will need indices which 

document how the robberies may have shifted. For a neighborhood showing an apparent sudden 

increase in auto thefts, there needs to be a quantitative standard to define the >typical= level of 

auto thefts. In assigning police cars to patrol particular major arteries, the center of minimum 

travel needs to be identified in order to maximize response time to calls for service. For research, 

as well, quantification is important. In examining correlates of burglaries, for example, a 

researcher needs to determine the exposure level, namely how many residences or commercial 

buildings exist in a community in order to establish a level of burglary risk. Or a precinct station 

may want to target areas for which there is a high concentration of incidents occurring within a 

short time (>hot spots=). While some of these analyses can be conducted with GIS queries, 

quantification can allow a more precise identification and the ability to compare different types 

of incidents. In short, there are many uses for quantitative analysis for which a statistical 

program becomes important. 

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The CrimeStat IV Spatial Statistics Program 

CrimeStat is a program designed to provide statistical summaries and models of crime 

incident data. The program provides crime analysts and researchers with a wide range of spatial 

statistical procedures that can be linked to a GIS. The procedures vary from the simple to some 

very sophisticated >cutting edge= routines. The reasoning is that different audiences vary in their 

needs and requirements. The program should be of benefit to different organizations. For many 

crime analysts, simple descriptions of the spatial distribution will be sufficient with the aim 

being practical intervention over a short time period. For these persons, many of the techniques 

provided in CrimeStat will be unnecessary. 

For other analysts, statistical tools can supplement a much larger GIS effort, such as the 

sophisticated crime analysis system built by the Baltimore County Police Department (BCPD, 

2012). For other researchers, even more demanding techniques may be needed to detect the 

underlying spatial structure as a means for formulating a temporal-spatial theory. A pattern in 

and of itself has little meaning unless it is linked to some framework. The ability to quantify 

relationships with a large amount of data can address problems that previously were avoided and 

can be a first step in developing an explanatory framework or interventionist strategy. CrimeStat 

attempts to address both types of needs by providing statistics in a >toolbox= framework. We 

recognize that today=s exotic statistical techniques may become tomorrow=s practical diagnostics 

and want the program to be useful for many years. 

Input and Output 

CrimeStat is a full-featured Windows program using a graphical interface with database 

and expanded statistical functions. It can read files in various formats - dBase7 , which is a 

common file format in desktop GIS programs, Excel (both ‘xls’ and ‘xlsx’ formats), ArcGIS 

Shape (shp) files, MapInfo data (dat) files, and files conforming to the ODBC standard, such as 

Lotus 1-2-3, and Microsoft Access (Microsoft, 2010). In addition, many other GIS packages, 

such as Maptitude7 can read >dbf=, >shp=, >bna= or >mif= files. 

Output includes both displayed tables, which can be printed as text or copied to a word 

processing program, and graphical output. CrimeStat can write graphical objects to the ArcGis7 , 

MapInfo7 , Maptitude GIS programs, Surfer7 10, ArcGIS Spatial Analyst8 programs, and to those 

that can read Ascii grid files (e.g., Vertical Mapper7

; Rockware, 2012; Golden Software, 2012; 

ESRI, 2012b). 

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Statistical Routines 

CrimeStat IV includes statistics routines for both statistical description and modeling. 

These are divided into six general statistical categories with more than 80 individual routines: 

Data Setup 

Primary file 

Input file with X/Y coordinates 

Define coordinate system 

Define data units 

Secondary file 

Input second file with X/Y coordinates as baseline 

Define coordinate system 

Define data units 

Reference file 

Create reference grid 

Use existing reference grid 

Type of distance measurement 

Use direct distance 

Use indirect distance 

Use network distance 

Spatial Description 

Spatial distribution 

Mean center 

Standard distance deviation 

Standard deviational ellipse 

Median center 

Center of minimum distance 

Directional mean and variance 

Convex Hull 

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Spatial Autocorrelation 

Moran=s “I” spatial autocorrelation index 

Geary=s “C” spatial autocorrelation index 

Adjusted Geary’s “C” spatial autocorrelation index 

Getis-Ord Global “G” spatial autocorrelation index with simulation of credible 

intervals 

Moran Correlogram with simulation of credible intervals 

Geary Correlogram with simulation of credible intervals 

Getis-Ord Correlogram with simulation of credible intervals 

Distance analysis I 

Nearest neighbor analysis 

Ripley=s “K” statistic 

Assign primary points to secondary points 

Distance Analysis II 

Within primary file distance matrix 

Between primary file and secondary file distance matrix 

Between primary file and grid distance matrix 

Between secondary file and grid distance matrix 

Hot Spot Analysis 

Hot spot analysis I 

Mode 

Fuzzy mode 

Nearest neighbor hierarchical clustering with simulation of credible intervals 

Risk-adjusted nearest neighbor hierarchical clustering with simulation of credible 

intervals 

Hot spot analysis II 

Spatial and temporal analysis of crime routine (STAC) with simulation of credible 

intervals 

K-mean clustering 

Hot spot analysis of Zones 

Anselin=s local Moran test with simulation of credible intervals 

Getis-Ord local “G” test with simulation of credible intervals 

Zonal nearest neighbor hierarchical clustering with simulation of credible 

intervals 

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Risk-adjusted zonal nearest neighbor hierarchical clustering with simulation of 

credible intervals 

Spatial Modeling I 

Interpolation I 

Single variable kernel density interpolation 

Dual variable kernel density interpolation 

Interpolation II

 Head-Bang analysis

 Interpolated Head-Bang analysis 

Space-time analysis 

Knox index 

Mantel index 

Correlated walk model for analysis and prediction 

 Journey-to-crime analysis 

Calibrate Journey-to-crime function 

Journey-to-crime estimation 

Draw crime trips 

Bayesian Journey-to-crime analysis

 Diagnostics for Journey-to-crime methods 

Estimate likely origin of a serial offender 

Spatial Modeling II 

Regression I 

MLE OLS and Poisson regression models 

MCMC Poisson and Logit regression models 

MCMC Poisson and Logit exposure regression models 

MCMC spatial Poisson and Logit regression models 

MCMC spatial Poisson and Logit exposure regression models 

Regression II 

Using OLS regression models to make predictions 

Using Poisson spatial regression models to make predictions 

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Discrete Choice I 

Create dataset for conditional logit model 

Estimate multinomial logit model 

Estimate conditional logit model 

Discrete Choice II 

Using multinomial logit model to make predictions 

Using conditional logit model to make predictions 

Time Series Forecasting 

Time Series Data 

Extrapolative Time Series Forecasting 

Classical Decomposition: Seasonality 

The Detection Problem 

Crime Travel Demand 

Trip Generation 

Skewness diagnostics 

Calibrate model 

Make prediction 

Balance predicted origins & destinations 

Trip Distribution 

Calculate observed origin-destination trips 

Calibrate impedance function 

Calibrate origin-destination model 

Apply predicted origin-destination model 

Compare observed and predicted origin-destination trip lengths 

Mode Split 

Calculate mode split for trips 

Network Assignment 

Check for one-way streets 

Create a transit network from primary file 

Network assignment of trips to travel network 

Many of these routines allow variations yielding an even larger number of statistics to be 

calculated. Two features of the program should be noted. First, and foremost, CrimeStat is a 

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program that specializes in the analysis of point locations. Over the years, many statistical tools 

have been developed for analyzing point locations. Many of these have either not been 

implemented as computer programs or were collected together as part of a specialized statistical 

system. They have been typically unavailable to crime analysts and the major statistical packages 

(e.g., SAS7 , SPSSJ , Systat7 ) do not include these routines. Consequently, we have collected those 

that are most appropriate for crime analysis and detection and organized them into a single 

package with a common graphical interface. They represent a wide variety of tools that can be 

used for crime analysis. CrimeStat can also analyze zonal data by treating them as >pseudo=

points. For example, the centroid of a census tract can be treated as a point and a value 

associated with the tract (e.g., its population) can be treated as an Intensity value (see chapter 3). 

Second, CrimeStat includes a variety of modeling tools for analyzing multivariate 

relationships. These include spatial regression routines for analyzing skewed distributions along 

with spatial autocorrelation, discrete choice modeling routines for modeling unique decisions, 

time series forecasting routines for analyzing sudden changes in the level of incidents in a zone, 

and a crime travel demand module for analyzing crime patterns over an entire metropolitan area. 

Program Requirements 

Required Hardware and Operating System 

CrimeStat IV was developed for the Windows 7 and Windows 8 operating systems, 

though it will also work with the Windows 2000, or Windows XP operating systems; it is not 

hardware dependent so that any processor that can run Windows 7 or Windows 8 will suffice. 

Some of the routines can also run on Windows XP and earlier Windows operating systems. 

However, the program was not designed around nor fully tested for those operating systems. It is 

highly recommended that the program be run on a more current version of Windows. 

While it can run on a relatively slow computer (e.g., 250 MHz clock speed) with limited 

RAM (e.g., 64 MB), it will run much better on a 2.6 GHz computer (or faster) with more than 2 

GB of RAM. In general, the faster the processor used and the more RAM, the quicker the 

program will run. The program is very intensive with respect to calculations. Some of the 

statistics produce very large matrices (e.g., the trip distribution routines in the Crime Travel 

Demand module). Depending on the size of the data files that will be processed, there may be 

hundreds of millions of calculations on any one run. It is critical, therefore, that the computer 

be fast and have sufficient amounts of RAM. 

Available RAM Limits the Size of Files 

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the Markov Chain Monte Carlo (MCMC) regression routines, the discrete choice module, the 

temporal modeling module, and several of the trip distribution routines will push the limits of 

most computer systems. For example, the 32 bit Windows operating system has a maximum 

addressable limit of 4 GB (i.e., 4 billion bytes) of RAM (actual and virtual). With a trip 

distribution matrix, there are M x N cells where M is the number of rows (origins) and N is the 

number of columns (destinations). With 8 bytes (64 bits) being assigned to a number in a cell 

(including the decimal and decimal places), practically the maximum matrix that could be loaded 

into memory would be about 22,000 x 22,000. One would never be able to use this amount since 

a lot of RAM will be taken up by the program and operating system. Nevertheless, using the 

calculation, the storage space required to save such a matrix will limit the size of the database, 

aside from taking a very long time to be calculated. In short, the size of the files that can be 

processed will depend on the particular routines being run. 

With a 64 bit operating system (e.g., Windows 7 or Windows 8), the theoretical 

maximum for addressable memory is 192 GB. Again, with 8 bytes per cell, the available RAM 

would allow a maximum square matrix of about 154,000 x 154,000 cells. Clearly, for large data 

sets, a 64 bit Windows operating system is preferable. 

Multi-threading 

CrimeStat is a multi-threaded application written to take advantage of multiple processors 

if the hardware and operating system support multiple processors. The program is designed to be 

multi-threading which means that it will take advantage of multiple processors (called ‘cores’) 

using Windows 8, Windows 7, and Windows Vista operating systems. These operating systems 

will support up to 64 core processors while Windows Server 2008 R2 supports up to 256 

processors. Earlier versions of Windows (e.g., Windows 2000) supported two core processors. 

Thus, if there are two processors and Windows 7 is the operating system, CrimeStat will 

calculate routines in about half the time. If there are four processors and Windows Server 2008 is 

the operating system, CrimeStat will calculate routines in about a quarter of the time. The 

multiples are not exact since processing time must be allocated for input of data and output of 

tables. Also, some of the routines (the Markov Chain Monte Carlo regression models, the 

temporal modeling module) are sequential so that the advantages of multi-threading will not play 

much of a part. 

For small data sets, this feature is not important as most runs will be very quick. 

However, for large data sets (e.g., 3000 cases or larger), the speed of calculations become 

important. For example, on a 1.6 GHz single-processor Pentium M7 computer with 1 GB of 

RAM running Windows XP Professional, it took about 4 minutes to complete a nearest neighbor 

analysis on 14,853 cases involving the calculating of distance from every point to every other 

point and identifying the 100 nearest neighbors. On a 2.4 GHz dual-processor Intel7 CoreTM 2 

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computer with 4 GB of RAM running a 64-bit Windows 7, it took about 50 seconds to complete 

the same task. On a 2.4 GHz quad-processor Intel7 CoreTM i7-2760QM processor with 16 GB of 

RAM and running a 64-bit Windows 7, the task took 16 seconds, more than three times faster 

than the ‘Core 2’ processor and 60 times faster than the ‘Pentium’ processor. The larger the file 

that is being processed, the more critical becomes the calculating efficiency of the computer. 

If a police department is expecting to run large data sets, it would benefit them to 

purchase fast multiple-processor computers with lots of RAM and fast hard disks to speed 

calculating times. The evolution of new processors is moving in this direction anyway so that 

multi-processor computers have become the norm. 

Required Software 

CrimeStat needs a Windows environment to operate. The program was designed for a 

Windows 7 operating system so it is better optimized for that system. In particular, Windows 7 

and Windows 8 have two features that allows CrimeStat to run more efficiently. First, they are 

multi-threading operating systems and can utilize multiple processors, as mentioned above. 

Second, they address memory in a more efficient way, as a large flat block. The 64 bit version of 

Windows 7 or Windows 8 in particular, will handle larger data blocks (called words) than the 

older 32 bit versions of Windows 7 and earlier operating systems. 

CrimeStat is a stand-alone program. Hence, it does not require any other program other 

than a Windows operating system. However, to be maximally useful, there should be an 

accompanying GIS program. While point data can be obtained from a non-GIS system (e.g., 

census files include lat/lon coordinates for the centroid of census units), the use of the GIS to 

assign the coordinates is almost necessary. Further, many of the outputs of CrimeStat are for 

GIS programs. For example, to view an ellipse of a hot spot or to view a three dimensional 

interpolation produced by CrimeStat will require an appropriate GIS package. 

Installing the Program 

CrimeStat comes compressed in a zipped file called CrimeStat.zip. To install the 

program, it is necessary to have a compression program that recognizes the >zip= format: 

1. Create a directory using Windows Explorer and copy the file to that directory. 

2. Double click on the file name in Explorer. When the name CrimeStat.zip is 

visible in the dialog box name field, double click the name with the left mouse 

button and point the extraction to the directory that you defined. CrimeStat will be 

installed in that directory. 

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3. The program help menu can also access the manual if the chapters of the manual 

are kept in the same directory as the program. 

Adding an Item to the Start Menu 

To add CrimeStat to the start menu: 

1. Click on the Start button in Windows followed by Settings then Taskbar. Click 

on Start Menu Programs followed by Add. 

2. In the dialog box, click on Browse, point to the directory where CrimeStat resides, 

and click on its name followed by Open. When the name CrimeStat is in the 

dialog box name field, click on the Next button. 

3. Double-click on the folder to which CrimeStat is to be assigned. 

4. Finally, type a name for CrimeStat (e.g., CrimeStat) followed by Finish. 

Adding an Icon to the Desktop 

To add CrimeStat to the desktop: 

1. Double-click on My Computer. 

2. Double-click on the drive in which CrimeStat resides followed by the directory 

that it is in (it may be several levels down). 

3. Click once on the name CrimeStat with the left button and then hold down the 

right mouse button. 

4. While holding the right mouse button, scroll to Create Shortcut. 

5. The name Shortcut to CrimeStat will be placed at the end of the list of files. 

6. Highlight the name by clicking on it once. Hold the left mouse button down and 

drag this name on to the desktop. 

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7. You can rename it CrimeStat by clicking on its icon with the right mouse button 

followed by Rename. 

8. Alternatively, you can use Windows Explorer to create a shortcut and then drag 

the shortcut to the desktop. 

Installing the Sample Data Sets 

There are eight sample data sets that can be used to run the program, also in >zip= format 

plus notes defining the variables. Since the data are simulated, they should not be used for real 

applications.1 They are provided to allow a user to become familiar with the program quickly. 

Many of these data sets have Read Me files that explain their data structure. However, 

ultimately, the value of the program must be tested on real data, rather than simulated data. 

1. General Sample Data.zip. The data are simulated incident points from 

Baltimore City and Baltimore County in Maryland. 

A. Incident.dbf - A simulated data set of 1061 incidents (e.g., robberies) in 

Baltimore County and the City of Baltimore 

B. Baltpop.dbf - The 1990 population, area and population density of 1349 

block groups in Baltimore County and the City of Baltimore 

2. Jtc Sample Data.zip. There are three files of simulated data for use with the 

Journey-to-crime routine (Chapter 13). The data should be stored in the same 

directory: 

A. JtcTest1.dbf - A simulated data set of 2000 robberies in Baltimore County 

that can be used for calibrating a travel demand function. Each record 

has a crime location and a residence location of the offender. 

B. JtcTest2.dbf - A simulated data set of 2500 burglaries in Baltimore County 

that can be used for calibrating a travel demand function. Each record 

has a crime location and a residence location of the offender. 

The data were simulated by a random number generator following the distribution of several types of crime 

incidents. Because the data were selected by a random generator, the points do not necessarily fall on 

streets or even stay within the boundaries of the jurisdiction. Their purpose is to provide a simple data set 

so users can become familiar with the program and should not be used for actual research. 

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C. Serial1.dbf - A simulated data set of the location of seven incidents 

committed by a single serial offender. To become familiar with the 

journey to crime routine, they can be treated as either robberies or 

burglaries. 

3. Bayesian Jtc Sample Data.zip. There are six files of simulated data for use 

with the Bayesian Journey-to-crime routine (Chapter 14). The data should be 

stored in the same directory: 

A. Bayesian_calibration_file.dbf – A simulated data set of 963 crimes 

committed by 88 serial offenders. Each record has an offender ID, the 

UCR code, and the crime location and residence location of the offender. 

B. Observed_OD_Distribution.dbf – A simulated matrix of crime trips from 

533 origin zones in Baltimore County (MD) and the City of Baltimore 

(MD) to 325 destination zones in Baltimore County. Each record 

includes the location of the origin zone, the location of the destination 

zone, and the number of crime ‘trips’ for each combination. 

C. Jtcfull.txt – A journey-to-crime calibration file that can be used to estimate 

the travel distance of offenders from each origin zone to each destination 

zone. 

D. S14A.dbf – the crime locations of an offender who committed 14 offences 

before being caught. Each record includes the UCR code and the crime 

location and residence location of the offender. 

E. TS15A.dbf – the crime locations of an offender who committed 15 

offences before being caught. Each record includes the UCR code, the 

date, the time, and the crime location and residence location of the 

offender. 

F. Test_Bayesian_Jtc_routine.param – a CrimeStat parameters file for 

loading these data into CrimeStat to run the routine. 

4. Correlated Walk Analysis Sample Data.zip. These are three files of simulated 

data for use with the Correlated Walk Analysis routine (Chapter 12): 

A. PredictableOffender1.dbf - A simulated data set for an algorithmic 

offender who committed 13 incidents. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

B. PredictableOffender2.dbf -A simulated data set for an algorithmic 

offender who committed 12 incidents. 

C. RealOffender1.dbf - A data set for a real offender who committed 12 

incidents - 10 larceny thefts, 1 robbery and 1 burglary. 

D. RealOffender2.dbf - A data set for a real offender who committed 15 

incidents - 10 larceny thefts, 2 assaults, 2 burglaries and 1 robbery. 

5. Crime Travel Demand Sample Data.zip. There are 13 files of data, CrimeStat 

parameter files, and a spreadsheet file for modeling travel behavior in Baltimore 

County, Md. They are examples used in the crime travel demand module 

(Chapters 25-32): 

A. Crime Travel Demand read me.pdf - a file that explains the three data sets 

and their fields and describes the eight parameter files. 

B. BCOrigins.dbf - a data set on 532 origin zones in both Baltimore County 

and the City of Baltimore from the late 1990s. There are data on crimes 

originating from each zone and demographic, economic and land use 

variables associated with those zones. 

C. BCDestinations.dbf - a data set of 325 destination zones in Baltimore 

County only. There are data on crimes occurring in each zone and 

demographic, economic and land use variables associated with those 

zones. 

D. ObservedODTrips.dbf - the actual trip distribution indicating the number 

of trips from each origin zone to each destination zone. 

E. Trip generation origin model.param - Runs trip generation model using 

the Poisson regression for the origin zones affecting Baltimore County. 

F. Trip generation destination model.param B Runs trip generation model for 

Baltimore County destinations. 

G. Make predicted origins.param B Applies modeled coefficients for the 

origin model to the same data set from which it was modeled. Then the 

routine adds in external trips. 

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H. Balance Origins and Destinations.param B Balances the number of trips 

by origin and by destination. In the example, the number of predicted 

destinations are held constant. 

I. Calibrate Origin-Destination Model Coefficients.param B Using the 

predicted origins and predicted destinations from the trip generation stage, 

estimates coefficients for distributing trips from origin zones to destination 

zones. 

J. Apply Origin-Destination Model.param B Inputs the predicted origins 

and predicted destinations from the trip generation stage as well as the 

modeled coefficients from H above. Outputs predicted trips for each 

origin-destination zone combination. For the graphic display, outputs top 

200 trips. 

K. Compare Observed and Predicted Trip Lengths.param B Inputs observed 

(actual) and predicted trip distribution and compares them by trip lengths. 

Calculates coincidence ratio and then compares the top 200 

origin-destination links. 

L. Mode Split Model.param B Inputs predicted origins, predicted 

destinations, and predicted trips along with estimates of the mode split 

function (see Excel spreadsheet below). Splits trips by origin-destination 

pair into specific travel modes. The output is both a table of 

origin-destination trips by mode as well as five ArcGis shape files 

representing zone-to-zone trips by mode. 

M. Mode split impedance defaults.xls - An Excel spreadsheet for estimating 

the coefficients of the mode split stage. This should be used in 

establishing the parameters for the mode split routine. 

6. Mode Split Impedance Defaults.xls. A spreadsheet for estimating the parameters 

of the mode split impedance function (Chapter 30). 

7. Discrete Choice Modeling Sample Data.zip. There are two files for running 

the multinomial logit model (Chapters 21 and 22): 

A. HoustonWeaponUse.dbf - A data set of weapon use during robberies in 

Houston ,TX. Each record contains an offender ID, a randomly assigned 

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crime location, the type of weapon used during the robbery (WEAPON) 

and 11 predictive variables. See the attachment to Chapter 22 for details. 

B. Run MNL model of Houston robberies.param – A CrimeStat parameters 

file for loading the multinomial logit model from these data. 

C. TheHagueBurglars.dbf – A file of 548 cleared burglaries from The Hague, 

Netherlands. The file contains information on the characteristics of the 

burglars and neighborhood identifiers. This will be combined with the 

data set on neighborhoods in The Hague. 

D. TheHagueNeighborhoods.dbf – a file of 89 neighborhoods in The Hague 

for the years 1996-2001.The data set is for use an alternatives files in 

creating a data set for the conditional logit model. This will be combined 

with the data set on burglars in The Hague. 

E. TheHagueNeighborhoodsXBurglars.dbf – this file is the result of matching 

the file TheHagueBurglars.dbf with TheHagueNeighborhoods.dbf using 

the ‘Create dataset for conditional discrete choice model’ routine under 

Discrete Choice I module. It is used to validate the results of combing 

TheHagueBurglars.dbf with TheHagueNeighborhoods.dbf files. 

8. Time Series Forecasting Sample Data.zip. There are two files for running the 

exponential smoothing and prediction routines (Chapters 23 and 24): 

A. Weekly crimes by Tract.dbf – A data set of weekly robberies by 140 

census tracts in Pittsburgh, PA, between 1990 and 1999. 

B. Monthly crimes by Tract.dbf – A data set of monthly robberies by 140 

census tracts in Pittsburgh, PA, between 1990 and 1999. 

Again, to repeat, many of these data are simulated. Though they are based on actual 

cases, the X and Y coordinates have been randomly assigned so that the real locations of the 

crime location or the offender’s residence are hidden. They should be used only to learn how to 

run individual routines. 

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To install any of these sample data files, it is necessary to have a compression program 

that recognizes the >zip= format: 

1. Create a data directory using Windows Explorer and copy the files to that 

directory. 

2. In Windows Explorer, double-click on its name and then follow the instructions. 

Step-by-Step Instructions 

This manual will go through the program step-by-step to address how it can be used by a 

crime mapping/analysis unit within a police department. Chapter 2 provides a quick guide for all 

the data definition and program routines and Chapter 3 provides detailed instructions on setting 

up data to run with CrimeStat. The statistical routines are described in parts II, III, IV, V, and 

VI. Part II presents a number of statistics for spatial description. Part III presents hot spot 

analysis techniques for both points and zones. Part IV presents a number of statistics for spatial 

modeling (called Spatial Modeling I) while part V presents multivariate tools for spatial 

modeling (called Spatial Modeling II). Finally, part VI presents a crime travel demand module. 

The different statistics are presented and detailed examples of each technique are shown. 

Options 

There is an option tab that allows the saving and loading of program parameters and the 

setting of colors for each of main headings: Data setup, Spatial description, Hot Spot Analysis, 

Spatial modeling I, Spatial Modeling II, and Crime Travel Demand. One can also output 

simulated data during the simulation runs; this will be explained in the appropriate section. 

Short Applications 

The manual also includes a number of applications conducted by other researchers and 

analysts. These are presented as one page sidebars at the end of each of the chapters. Most of 

these are from criminal justice. But, applications from other fields have also been included. 

The aim is to show the diversity of applications that researchers and analysts have used with the 

various routines in CrimeStat. 

Online Help 

In addition, there is on-line help for the program. There is a Help button that can be 

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pushed to access all the help items. In addition, the program has context-sensitive help. On any 

page or routine, clicking on the Help button at the bottom of the screen will pop up an 

appropriate help item. The on-line help can also access the program manual. For this to be 

available, be sure to store the chapters of the manual in the same directory as the program. 

Accessing the Help Menu in Windows 

CrimeStat IV works with the Windows Vista, Windows 7, Windows Server 2008 R2, and 

Windows 8 operating systems. However, these operating systems do not include the help menu 

file that was available in previous versions of Windows (WinHlp32.exe) and clicking on the 

CrimeStat help button may not work. If you have this problem, Microsoft has developed a 

special file that allows help menus to be viewed. It will be necessary to obtain the file and install 

it. The URL is found at: 

http://support.microsoft.com/kb/917607 

Select the file that is appropriate for your operating system and follow the instructions on 

the page. Additional diagnostic information is also provided on the page. 

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References 

BCPD (2012). Baltimore County Police Crime Statistics and More. Baltimore County Police 

Department: Towson, MD. 

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