Table of Contents 

Major Chapter Headings i 

Acknowledgments xiv 

License Agreement and Disclaimer xviii
i 

Major Chapter Headings

Part I: Program Overview

Chapter 1: Introduction to CrimeStat IV 1.1

By Ned Levine 

Uses of Spatial Statistics in Crime Analysis 1.1 

The CrimeStat IV Spatial Statistics Program 1.2

Statistical Routines 1.3 

Program Requirements 1.7

Installing the Program 1.9

Step-by-Step Instructions 1.16 

Options 1.16 

Short Applications 1.16 

Online Help 1.16 

References 1.18 

Chapter 2: Quickguide to CrimeStat IV 2.1

By Ned Levine 

Introduction 2.1 

Data Setup 2.2 

Primary File 2.2 

Secondary File 2.6 

Reference File 2.8 

Measurement Parameters 2.10 

Spatial Description 2.15 

Spatial Distribution 2.15 

Spatial Autocorrelation 2.20 

Distance Analysis I 2.30 

Distance Analysis II 2.38 

Hot Spot Analysis 2.40 

Hot Spot Analysis I 2.40 

Hot Spot Analysis II 2.50 

Hot Spot Analysis of Zones 2.56 

Spatial Modeling I 2.66 
ii 

Interpolation I 2.66 

Interpolation II 2.73 

Space-time Analysis 2.80 

Journey-to-crime 2.88 

Bayesian Journey-to-crime 2.96 

Spatial Modeling II 2.111 

Regression I Module 2.111 

Regression II Module 2.128 

Discrete Choice I 2.130 

Discrete Choice II 2.142 

Time Series Forecasting 2.145 

Crime Travel Demand 2.151 

Crime Travel Demand Data Preparation 2.152 

Project Directory 2.153 

Trip Generation 2.155 

Trip Distribution 2.174 

Mode Split 2.196 

Network Assignment 2.206 

File Worksheet 2.214 

Options 2.216 

Chapter 3: Entering Data into CrimeStat IV 3.1

By Ned Levine 

Organization of Program into Tabs 3.1 

Required Data 3.6

Primary File 3.11

Secondary File 3.19 

Reference File 3.21

Measurement Parameters 3.29

Distance Calculations 3.33

Saving Parameters 3.37 

Statistical Routines and Output 3.37 

A Tutorial with a Sample Data Set 3.38 

References 3.44 

Endnotes 3.46

Attachment 3.52
iii 

Part II: Spatial Description

Chapter 4: Centrographic Statistics 4.1

By Ned Levine 

Centrographic Statistics 4.1 

Mean Center 4.1 

Weighted Mean Center 4.4 

Median Center 4.6 

Center of Minimum Distance 4.12 

Standard Deviations of the X and Y Coordinates 4.14 

Standard Distance Deviation 4.14 

Standard Deviational Ellipse 4.17 

Geometric Mean 4.20 

Harmonic Mean 4.23 

Average Density 4.26 

Output Files 4.26 

Examples of Centrographic Statistics 4.30 

Directional Mean and Variance 4.34 

Convex Hull 4.45 

References 4.49 

Endnotes 4.51 

Attachments 4.54

Chapter 5: Spatial Autocorrelation Statistics 5.1

By Ned Levine 

Spatial Autocorrelation 5.1 

Indices of Spatial Autocorrelation 5.3 

Moran’s “I” Statistic 5.5 

Geary’s “C” Statistic 5.10 

Getis-Ord “G” Statistic 5.16 

Moran Correlogram 5.26 

Geary Correlogram 5.34 

Getis-Ord Correlogram 5.35 

References 5.39 

Attachments 5.39 
iv 

Chapter 6: Distance Analysis I and II 6.1

By Ned Levine 

Distance Analysis I 6.1 

Nearest Neighbor Index 6.1 

K-Order Nearest Neighbor 6.8 

Linear Nearest Neighbor Index 6.13 

Linear K-Order Nearest Neighbor Index 6.20 

Ripley’s K Statistic 6.22 

Assign Primary Points to Secondary Points 6.36 

Distance Analysis II 6.41 

Distance Matrices 6.41 

References 6.44 

Attachments 6.45 

Part III: Hot Spot Analysis

Chapter 7: Hot Spot Analysis of Points: I 7.1 

By Ned Levine 

Hot Spots 7.1 

Statistical Approaches to the Measurement of >Hot Spots 7.2 

Cluster Routines in CrimeStat 7.7 

Mode 7.9 

Fuzzy Mode 7.11 

Nearest Neighbor Hierarchical Clustering 7.16 

Risk-Adjusted Nearest Neighbor Hierarchical Clustering 7.36 

References 7.53 

Endnotes 7.58 

Attachments 7.61 

Chapter 8: Hot Spot Analysis of Points: II 8.1

By Richard Block, Carolyn Rebecca Block, & Ned Levine 

Spatial and Temporal Analysis of Crime (STAC) 8.1 

K-Means Partitioning Clustering 8.18 

Some Thoughts on the Concept of >Hot Spots= 8.33 

References 8.36 
v 

Endnotes 8.39 

Attachments 8.41 

Chapter 9: Hot Spot Analysis of Zones 9.1

By Ned Levine 

Assigning Point Data to Zones 9.1 

Local Indicator of Spatial Association 9.3 

Anselin’s Local Moran 9.4 

Getis-Ord Local “G” 9.16 

Zonal Nearest Neighbor Hierarchical Clustering 9.21 

References 9.46 

Endnotes 9.47 

Attachments 9.48 

Part IV: Spatial Modeling I

Chapter 10: Kernel Density Interpolation 10.1

By Ned Levine 

Introduction 10.1 

Kernel Density Interpolation 10.1 

Single Kernel Density Interpolation 10.13 

Dual Kernel Density Interpolation 10.22 

Advantages and Limitations of Kernel Density Interpolation 10.34 

Conclusion 10.36 

References 10.37 

Endnotes 10.40 

Attachments 10.42 

Chapter 11: Head-Bang Interpolation 11.1

By Ned Levine 

Interpolation II Tab 11.1 

Head-Bang Statistic 11.1 

Interpolated Head-Bang Statistic 11.19 

References 11.26 
vi 

Chapter 12: Space-Time Analysis 12.1

By Ned Levine 

Measurement of Time in CrimeStat 12.1 

Space-Time Interaction 12.3 

Knox Index 12.4 

Mantel Index 12.9 

Spatial-Temporal Moving Average 12.13 

Correlated Walk Analysis 12.14 

References 12.45 

Endnotes 12.47 

Attachments 12.48 

Chapter 13: Journey-to-Crime Estimation 13.1

By Ned Levine 

Location Theory 13.1 

Travel Demand Modeling 13.2 

Travel Behavior of Criminals 13.8 

Predicting the Location of Serial Offenders 13.11 

The CrimeStat Journey-to-crime Routine 13.12 

Journey-to-crime Estimation Using Mathematical Functions 13.16 

Empirically Estimating a Journey-to-crime Calibration Function 13.38 

Journey-to-crime Estimation Using a Calibrated File 13.49 

How Accurate are the Methods? 13.61 

Confirmation of These Results 13.69 

Draw Crime Trips 13.72 

References 13.74 

Endnotes 13.83 

Attachments 13.84 

Chapter 14: Bayesian Journey-to-Crime Estimation 14.1

By Ned Levine & Richard Block 

Bayesian Probability 14.1 

The Bayesian Journey-to-crime Estimation Module 14.10 

Data Preparation for Bayesian Journey-to-crime Estimation 14.10 

Bayesian Journey-to-crime Diagnostics 14.16 
vii 

Which is the Most Accurate and Precise Method? 14.20 

Estimate Likely Origin of a Serial Offender 14.30 

Probability Filters 14.47 

Guidelines for Analysts 14.54 

Summary 14.58 

References 14.59 

Part V: Spatial Modeling II

Chapter 15: OLS Regression Modeling 15.1 

By Ned Levine & Dominique Lord 

Functional Relationships 15.1 

Normal Linear Relationships 15.1 

Corrections to Violated Assumptions in Normal Linear Regression 15.19 

Diagnostic Tests and OLS 15.30 

MCMC Version of Normal (OLS) 15.32 

References 15.33 

Chapter 16: Poisson Regression Modeling 16.1 

By Dominique Lord, Ned Levine, & Byung-Jung Park 

Count Data Models 16.1 

Poisson Regression 16.1 

Poisson Regression with Linear Dispersion Correction 16.13 

Poisson-Gamma (Negative Binomial) Regression 16.14 

Limitations of the Maximum Likelihood Approach 16.24 

References 16.25 

Chapter 17: Estimating Complex Models with 

Markov Chain Monte Carlo Simulation 17.1

By Dominique Lord, Ned Levine, Byung-Jung Park, Srinivas Geedipally, 

Haiyan Teng, & Li Shing 

Markov Chain Monte Carlo (MCMC) 

 Simulation of Regression Functions 17.1 

Risk Analysis 17.26 

Issues in MCMC Modeling 17.30 
viii 

Improving the Performance of the MCMC Algorithm 17.44 

References 17.53 

Chapter 18: Binomial Regression Modeling 18.1 

By Ned Levine, Dominique Lord, & Byung-Jung Park 

Introduction 18.1 

Generalized Linear Models 18.1 

Logistic Model 18.3 

Logit Regression 18.12 

Probit Model 18.18 

Conclusion 18.21 

References 18.26 

Chapter 19: Spatial Regression Modeling 19.1 

By Ned Levine, Dominique Lord, Byung-Jung Park, Srinivas Geedipally, 

Haiyan Teng, & Li Sheng 

Spatial Regression Modeling 19.1 

MCMC Normal-CAR Model 19.9 

MCMC Normal-SAR Model 19.11 

MCMC Poisson-Gamma-CAR Model 19.12 

MCMC Poisson-Gamma-SAR Model 19.13 

MCMC Poisson-Lognormal-CAR/SAR Model 19.13 

MCMC Binomial-Logit-CAR/SAR Model 19.14 

Spatial Weights Function 19.14 

Estimation Procedures for Spatial Models 19.15 

Examples of Spatial Regression Modeling 19.19

Caveat 19.33 

Summary 19.34 

References 19.35 

Chapter 20: The CrimeStat Regression Module 20.1 

By Ned Levine, Dominique Lord, Byung-Jung Park, Srinivas Geedipally, 

Haiyan Teng, Li Sheng, & Ian Cahill 

The CrimeStat Regression Module 20.1 

Regression I Module 20.1 
ix 

Output 20.11 

Diagnostics Relevant for Spatial Regression 20.21 

Regression II Module 20.22 

Conclusion 20.25 

Chapter 21: Discrete Choice Modeling 21.1 

By Wim Bernasco & Richard Block 

Introduction 21.1 

Discrete Choice Framework 21.3 

Multinomial and Conditional Logit 21.5 

Data Structures 21.7 

The Multinomial Logit Model 21.7 

The Conditional Logit Model 21.19 

Conclusion 21.26 

References 21.28 

Attachments 21.30 

Chapter 22: The CrimeStat Discrete Choice Module 22.1 

By Wim Bernasco, Richard Block, Ned Levine & Ian Cahill 

Discrete Choice I Module 22.1 

Create Data set for Conditional Discrete Choice Model 22.2 

Example of Running a Multinomial Logit Model 22.16 

Example of Creating and Running a Conditional Logit Model 22.16 

Discrete Choice II Module 22.27 

Make Prediction 22.27 

Chapter 23: Time Series Forecasting 23.1 

By Wil Gorr & Andreas M. Olligschlaeger 

Introduction 23.1 

Time Series Data 23.2 

Extrapolative Time Series Forecasting 23.4 

Classical Decomposition: Seasonality 23.15 

The Detection Problem 23.16 

Conclusions 23.20 

References 23.26 
x 

Chapter 24: The CrimeStat Time Series Forecasting Module 24.1

By Wil Gorr & Andreas M. Olligschlaeger 

Introduction 24.1 

Rationale of the Module 24.1 

Overview of the Module 24.2 

Data Preparation for Time Series Forecasting 24.3 

Running the Time Series Forecasting module 24.9 

Output 24.9 

Guidelines for Running Forecast Models 24.16

Counterfactual Detection v. Forecasting 24.18 

Example with Pittsburgh Month Crime Data 24.18 

Conclusion 24.19 

References 24.22 

Part VI: Crime Travel Demand Modeling

Chapter 25: Overview of Crime Travel Demand Modeling 25.1 

By Ned Levine 

Travel Demand Forecasting 25.1 

Need for More Complex Travel Model of Crime 25.2 

Crime Travel Demand Framework 25.5 

Crime Travel Definitions 25.8 

The CrimeStat Travel Demand Module 25.12 

Crime Travel Demand v. Journey-to-Crime 25.14 

Models v. Description 25.15 

Uses of a Crime Travel Demand Model 25.17 

References on Travel Demand Modeling 25.20 

References 25.22 

Chapter 26: Data Preparation for 

 Crime Travel Demand Modeling 26.1 

By Ned Levine 

Choice of a Zonal System 26.1 

Obtaining Crime Data 26.9 

Developing a Predictive Model 26.21 
xi 

Where to obtain these data? 26.28 

Creating an Integrated Data Set 26.29 

Conclusion 26.40 

References 26.41 

Chapter 27: Trip Generation Modeling 27.1 

By Ned Levine 

Background 27.1 

Modeling Trip Generation 27.2 

Approaches Toward Trip Generation Modeling 27.6 

Diagnostic Tests 27.20 

Available Regression Models 27.28 

Adding Special Generators 27.29 

Adding External Trips 27.30 

Balancing Predicted Origins and Predicted Destinations 27.31 

Summary of the Trip Generation Model 27.32 

The CrimeStat Trip Generation Model 27.32 

Calibrate Model 27.34 

Make Trip Generation Prediction 27.38 

Balance Predicted Origins & Destinations 27.40 

Example of the Trip Generation Model 27.41 

Strengths and Weaknesses of Regression Modeling of Trips 27.64 

Conclusion 27.66 

References 27.67 

Chapter 28: Trip Distribution Modeling 28.1

By Ned Levine, Richard Block, Dan Helms, & Phil Canter 

Theoretical Background 28.1 

The Gravity Model 28.4 

Travel Impedance 28.8 

Alternative Model: Intervening Opportunities 28.14 

Method of Estimation 28.15 

CrimeStat IV Trip Distribution Module 28.16 

Calibrate Impedance Function 28.24 

Setup of Origin-Destination Model 28.28 

Running the Origin-Destination Model 28.42 
xii 

Comparing Observed & Predicted Trips 28.49 

Uses of Trip Distribution Analysis 28.73 

References 28.76 

Attachments 28.79 

Chapter 29: Mode Split Modeling 29.1 

By Ned Levine 

Theoretical Background 29.1 

Utility of Travel and Mode Choice 29.1 

Tools for Estimating Mode Split in CrimeStat 29.11 

Relative Accessibility 29.11 

CrimeStat IV Mode Split Tools 29.28 

Usefulness of Mode Split Modeling of Crime Trips 29.37 

Limitations to the Mode Split Methodology for Crime Analysis 29.41 

Conclusions 29.43 

References 29.44 

Chapter 30: Network Assignment 30.1

By Ned Levine 

Theoretical Background 30.1 

Networks 30.2 

Shortest Path Algorithms 30.9 

Routine Algorithms 30.30 

The CrimeStat Network Assignment Module 30.32 

Modeling Network Assignment of Crime Types 30.45 

Uses of Network Assignment of Crime 30.45 

Conclusion 30.48 

References 30.49 

Attachments 30.50 

Chapter 31: Case Studies in Crime Travel Demand Modeling I: 

Travel Patterns of Chicago Robbery Offenders 31.1

By Richard Block 

Case Study I: Travel Patterns of Chicago Robbery Offenders 31.1

Data for the Chicago Study 31.4
xiii 

Trip Generation 31.6 

Trip Distribution 31.8

Mode Split 31.14 

Network Assignment 31.14

Conclusions 31.22

References 31.25 

Chapter 32: Case Studies in Crime Travel Demand Modeling II: 

Application of Travel Demand Behavior Model on 

Crime Data from Las Vegas, Nevada 32.1

By Dan Helms 

Introduction 32.1 

The Las Vegas Metropolitan Area 32.2 

Source Data Provenance and Organization 32.3 

Trip Generation 32.14 

Trip Distribution 32.20 

Mode Split 32.27 

Network Assignment 32.27 

Modeling Different Crime Types 32.28 

Conclusions 32.32 

References 32.38 

CrimeStat IV References R-1

Appendix A: Some Notes on the Statistical Comparison 

of Two Samples A-1

By Ned Levine 

Appendix B: Ordinary Least Squares and Poisson Regression Models B-1 

By Luc Anselin 

Appendix C: Negative Binomial Regression Models 

and Estimation Methods C-1

By Dominique Lord & Byung-Jung Park 
xiv 

Acknowledgments

CrimeStat IV (version 4.0) was developed under the direction of Dr. Ned Levine of Ned 

Levine & Associates, Houston, TX with Grant 2005-IJ-CX-K037 from the Office of Science and 

Technology, National Institute of Justice (NIJ), Washington, DC. The developer would like to 

thank the many individuals who contributed to this program over the years since its inception: 

1. Ms. Haiyan Teng of Houston, TX, the primary programmer for versions 2.0 

through 4.0. Her high level of programming competence and mathematical 

expertise was essential for the successful completion of the crime travel demand 

routines, the Bayesian Journey to Crime routine, the Head-Bang routine, and the 

Markov Chain Monte Carlo regression routines as well as ensuring that all new 

routines were properly integrated into the main program. She is a co-author of 

three chapters. 

2. Mr. Ron Wilson, formerly project manager at the Mapping and Analysis for 

Public Safety Program (MAPS) at NIJ and currently a researcher at the U.S. 

Department of Housing and Urban Development, who supported the project 

through much of this development and provided valuable feedback on the new 

routines and their utility. He also has been instrumental in pushing for the 

development of CrimeStat libraries that will be released separately later this year. 

3. Mr. Joel Hunt, current project manager at the Mapping and Analysis for Public 

Safety Program (MAPS) at NIJ. He took over administrative management of the 

project towards for NIJ towards the end of the development but has been 

supportive throughout. 

4. Professor Richard Block of Loyola University in Chicago has contributed as a 

methodological and criminal justice advisor to the project since early in its 

development. He has played a critical role in conducting quality control tests 

and is the author of one chapter and the co-author of five chapters. 

5. Dr. Shaw-pin Miaou of Transmidas Consulting Services in College Station, TX 

provided detailed instructions for building the MCMC Poisson-Gamma and 

Poisson-Gamma-CAR regression models. Ms. Haiyan Teng and Dr. Li Sheng 

converted the instructions into C++ and wrote numerical libraries for it. 

6. Dr. Dominique Lord of Texas A & M University in College Station, TX provided 

detailed technical help on the Poisson and binomial maximum likelihood and 
xv 

MCMC methods. He is also the co-author of six chapters on the regression 

module. 

7. Dr. Byung-Jung Park of the Korea Transport Institute in Goyang, South Korea 

provided mathematical clarification of the models and also has developed 

alternative dispersion and spatial autoregressive functions. He is also the 

co-author of five chapters. 

8. Professor Wim Bernasco of the Netherlands Institute for the Study of Crime and 

Law Enforcement and the Department of Spatial Economics, Faculty of 

Economics and Business Administration, VU University Amsterdam, Netherlands, 

for designing the discrete choice module and is the co-author of chapters 21 and 22. 

9. Professor Wil Gorr of Carnegie-Mellon University in Pittsburgh, PA, for 

designing the time series forecasting module and is the co-author of chapters 23 

and 24. 

10. Dr. Andreas Olligschlaeger of TruNorth Data Systems, Inc. of Baden, PA for 

programming the time series forecasting module. He is the co-author of chapters 

23 and 24. 

11. Professor Shashi Shekhar of the University of Minnesota in Minneapolis for 

supervising the conversion of CrimeStat II routines into libraries. 

12. Mr. Ian Cahill of Cahill Software, Edmonton, Alberta for providing OLS, 

Poisson, negative binomial, and multinomial regression maximum likelihood 

code, based on his MLE++ software package. http://www.magma.ca/~cahill. The 

code forms the engine of the maximum likelihood routines though additional 

capabilities have been added. He is a co-author on two chapters. 

13. Dr. Srinivas Geedipally of the Texas Transportation Institute, Dallas, TX, for 

developing the MCMC version of the normal distribution along with the spatial 

autocorrelation variants. He is a co-author on three chapters. 

14. Dr. Li Sheng, of Houston, TX helped Ms. Teng in programming the MCMC 

routines. In particular, he created a numerical library that allowed the algorithms 

to run much faster. He is a co-author on three chapters. 
xvi 

15. Dr. David Wong of George Mason University for providing advice on the 

Getis-Ord “G” statistic. 

16. Mr. Daniel Helms of Scytale Consulting, Reston, VA, served as a criminal justice 

advisor to the project and played an important role in testing the crime travel 

demand routines that was developed in version 3. 

17. Mr. Long Doan of Doan Consulting, Falls Church, VA was the original 

programmer for the project. Mr. Doan’s brilliance in programming was essential 

to the development of the initial program. 

18. Professor Luc Anselin of the University of Illinois at Urbana-Champaign 

provided technical advice and documentation on the regression models used in the 

crime travel demand model that was developed in version 3. 

19. Professor Peter Stopher of the University of Sidney in Australia provided 

technical advice on the crime travel demand model developed in version 3. 

20. Professor Luc Anselin of Arizona State University, Tempe, AZ, provided 

technical advice on the OLS and Poisson regression models. 

21. Mr. Phil Canter, formerly of the Baltimore County Police Department, Towson, 

MD, has been with the project since its inception. For this round, he provided 

support and data for analysis. 

22. Ms. Sandra Wortham of Wortham Design, Wilmington, DE designed the 

graphical icons used in the program. 

23. The GNU project library for providing F-test and t-test code. http://www.gnu.org.

24. Dr. Carolyn Rebecca Block of the Illinois Criminal Justice Information Authority 

for providing the STAC routine. 

25. All the other individuals from the MAPS unit who have supported the project in 

earlier stages: for the third version, Ms. Debra Stoe; for the second version, Ms. 

Elizabeth Groff of the Institute of Law and Justice, Mr. Eric Jefferis of the 

University of Akron, and Professor Robert Langworthy of the University of 

Central Florida, Orlando, FL; and, for the first version, Ms. Cindy Mamalian and 

Dr. Nancy LaVigne of the Urban Institute. 
xvii 

26. Ms. Patsy Lee of the Greater Manchester Police Department, Manchester, 

England, for providing crime data on Manchester. 

27. To Dr. Alan Robertson and Mr. Barry Fosberg of the Houston Police Department 

for providing crime data on Houston; 

28. To the individuals of the Baltimore Metropolitan Council who provided network 

and other data on both Baltimore County and the City of Baltimore, in particular 

Jacqueline Zee, Matt de Rouville, and Gene Bandy. Thanks also to Alan Clark of 

the Houston-Galveston Area Council for making available data on Houston motor 

vehicle crashes. 

29. To individuals who have provided detailed feedback and information for this and 

previous versions of CrimeStat: Professor Eric Renshaw of the University of 

Strathclyde in Glasgow, Mr. John DeVoe of Siebel Systems, Professor Jim 

LeBeau of Southern Illinois University, Mr. Bryan Hill of the Glendale (Arizona) 

Police Department, Professor Karl Kim of the University of Hawaii, Mr. Luben 

Dimov of Louisiana State University, Mr. Weijie Zhou of the Houston-Galveston 

Area Council, and Mr. Martin Hittleman of Valley Community College in Los 

Angeles. 

30. To the individuals who provided example applications for the manual: Renato 

Assunção, Cláudio Beato, Bráulio Silva of the Federal University of Minas Gerais 

in Belo Horizonte, Brazil; Daniel Bibel of the Massachusetts State Police; 

Gilberto Câmara, Silvana Amaral, Antônio Miguel V. Monteiro, and José A. 

Quintanilha of the Instituto Nacional de Pesquisas Espaciais in Brazil; Spencer 

Chainey of InfoTech Enterprises Europe in London, England; Richard Crepeau of 

Appalachian State University; Jaishankar Karuppannan of the University of 

Madras in Chepauk, India; Yongmei Lu of Southwest Texas State University; 

David McGrath of the Johnstown Castle Research Centre in Wexford, Ireland; 

Nathalie Pavy and Jean Bousquet of Laval University of Quebec; Dietrich 

Oberwittler and Marc Wiesenhütter of the Max Planck Institute for Foreign and 

International Criminal Law in Freiburg, Germany; Derek Paulsen of Appalachian 

State University; Gaston Pezzuchi of the Buenos Aires Province Police Force; 

Mike Saweda of the University of Ottawa; Takahito Shimada of the National 

Police Agency in Chiba, Japan; Daisy Smith and Steph Winstanley of the Greater 

Manchester Police Department; Brent Snook, Paul Taylor & Craig Bennell of the 

University of Liverpool, England; Matthew Stone of the California Department of 

Health Services, Chaosheng Zhang of the National University of Ireland in 
xviii 

Galway, Ireland; Marta A. Guerra of the Centers for Disease Control and 

Prevention; Richard Hoskins of the State of Washington Department of Health; 

Tom Reynolds of the University of Texas School of Public Health along with Luc 

Anselin, Richard Block, Carolyn Block, Phil Canter, Long Doan, Daniel Helms, 

Jim LeBeau, Ron Wilson and Bryan Hill mentioned above. 

31. To the dozens of individuals who provided feedback and suggestions for 

improving the program. They are, unfortunately, too numerous to list. 

32. Finally, this program is dedicated to my wife, Dr. C. Elizabeth Castro, for being 

so patient and supportive throughout this long process. She is an inspiration to me 

for this whole effort. 

Disclaimer and License Agreement

This project was supported by Grant 2005-IJ-CX-K037 awarded by the National Institute 

of Justice, Office of Justice Programs, US Department of Justice and built on earlier National 

Institute of Justice grants. Points of view in this document are those of the author and do not 

necessarily represent the official position or policies of the US Department of Justice. 

CrimeStat® is a registered trademark of Ned Levine & Associates. The program is 

copyrighted by and the property of Ned Levine and Associates and is intended for the use of law 

enforcement agencies, criminal justice researchers, and educators. It can be distributed freely 

for educational or research purposes, but cannot be re-sold. It must be cited correctly in any 

publication or report that uses results from the program. The correct citation is: 

Ned Levine, CrimeStat IV: A Spatial Statistics Program for the Analysis of Crime 

Incident Locations (version 4.0). Ned Levine & Associates, Houston, TX, and the 

National Institute of Justice, Washington, DC, June 2013. 

The National Institute of Justice, Office of Justice Programs, United States Department of 

Justice reserves a royalty-free, non-exclusive, and irrevocable license to reproduce, publish, or 

otherwise use, and authorize others to use this program for Federal government purposes. This 

program cannot be distributed without the permission of both Ned Levine and Associates and the 

National Institute of Justice, except as noted above. 

With respect to this software and documentation, neither Ned Levine and Associates, the 

United States Government nor any of their respective employees make any warranty, express or 
xix 

implied, including but not limited to the warranties of merchantability and fitness for a particular 

purpose. In no event will Ned Levine and Associates, the United States Government or any of 

their respective employees be liable for direct, indirect, special, incidental, or consequential 

damages arising out of the use or inability to use the software or documentation. Neither Ned 

Levine and Associates, the United States Government, nor their respective employees are 

responsible for any costs including, but not limited to, those incurred as a result of lost profits or 

revenue, loss of time or use of software, loss of data, the costs of recovering such software or 

data, the cost of substitute software, or other similar costs. Any actions taken or documents 

printed as a result of using this software and its accompanying documentation remain the 

responsibility of the user. 

Any questions about the use of this program should be directed to either: 

Dr. Ned Levine Mr. Joel Hunt 

Ned Levine & Associates Mapping and Analysis for Public Safety Program 

Houston, TX National Institute of Justice 

crimestat@nedlevine.com U. S. Department of Justice 

 810 7th St, NW