CrimeStat: Spatial Statistics Program for the Analysis of Crime Incident Locations

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About CrimeStat

CrimeStat IV (version 4.01) is the most recent version of CrimeStat, a spatial statistics program for the analysis of crime incident locations. CrimeStat was developed by Ned Levine & Associates of Houston, Texas, under the direction of Ned Levine, Ph.D., and funded by grants from NIJ (grants 1997-IJ-CX-0040, 1999-IJ-CX-0044, 2002-IJ-CX-0007, and 2005-IJ-CX-K037).

CrimeStat is Windows-based and interfaces with most desktop GIS programs. It provides statistical tools to aid law enforcement agencies and criminal justice researchers in their crime mapping efforts. Many police departments around the country use CrimeStat, as do criminal justice and other researchers.

The program includes more than 100 statistical routines for the spatial analysis of crime and other incidents. CrimeStat inputs incident locations (e.g., robbery locations) in dbf, point shp or ASCII formats using either spherical or projected coordinates. It calculates various spatial statistics and writes graphical objects to ArcGIS, MapInfo, Surfer for Windows and other GIS packages.

Organization of CrimeStat IV

CrimeStat is organized into seven sections:

Section 1: Data Setup

  • Primary file — File of incident or point locations with X and Y coordinates. Zones can be treated as pseudo-point using a single point within the zone (e.g., the centroid). The coordinate system can be either spherical (lat/lon) or projected. Intensity and weight values are allowed. Each incident can have an associated time value.
  • Secondary file — Associated file of incident or point locations with X and Y coordinates. The coordinate system must be the same as the primary file. Intensity and weight values are allowed. The secondary file is used for comparison with the primary file in several routines.
  • Reference file — Grid file that overlays the study area. Normally, it is a regular grid though irregular ones can be imported. CrimeStat can generate the grid if given the X and Y coordinates for the lower-left and upper-right corners.
  • Measurement parameters — Page identifies the type of distance measurement (direct, indirect or network) to be used and specifies parameters for the area of the study region and the length of the street network. CrimeStat IV can use a network for linking points. Each segment can be weighted by travel time, travel speed, travel cost or simple distance. This allows the interaction between points to be estimated more realistically.

Section 2: Spatial Description

  • Spatial distribution — Statistics for describing the spatial distribution of incidents including the mean center, center of minimum distance, standard deviational ellipse, the convex hull and directional mean.
  • Spatial autocorrelation — Statistics for describing the spatial autocorrelation between zones including general (global) spatial autocorrelation indices — Moran's I, Geary's C and the Getis-Ord General G, and correlograms that calculate spatial autocorrelation for different distance separations — the Moran, Geary and Getis-Ord correlograms. Several of these routines can simulate confidence intervals with a Monte Carlo simulation.
  • Distance analysis I — Statistics for describing properties of distances between incidents including nearest neighbor analysis, linear nearest neighbor analysis and Ripley's K statistic. There is also a utility that assigns the primary points to the secondary points, either on the basis of nearest neighbor distance or point-in-polygon, and then sums the results by the secondary point values.
  • Distance analysis II — Calculates matrices representing the distance between points for the primary file, for the distance between the primary and secondary points, and for the distances between the primary or secondary file and the reference grid.

Section 3: Hot Spot Analysis

  • Hot spot analysis I — Routines for conducting hot spot analysis including the mode, the fuzzy mode, hierarchical nearest neighbor clustering (Nnh) and risk-adjusted nearest neighbor hierarchical clustering (Rnnh). The Nnh and Rnnh hot spots can be output as ellipses or convex hulls. Both routines can simulate confidence intervals with a Monte Carlo simulation.
  • Hot spot analysis II — Routines for conducting hot spot analysis including the Spatial and Temporal Analysis of Crime (STAC) and K-means clustering. STAC and K-means hot spots can be output as ellipses or convex hulls. Both routines can simulate confidence intervals with a Monte Carlo simulation.
  • Hot spot analysis of zones — Routines for conducting hot spot analysis on zonal data including Anselin's local Moran, the Getis-Ord local G statistics, and zonal hierarchical nearest neighbor clustering. The zonal hierarchical nearest neighbor hot spots can be output as ellipses or convex hulls. All of these routines can simulate confidence intervals with a Monte Carlo simulation.

Section 4: Spatial Modeling I

  • • Interpolation I — Single-variable kernel density estimation routine for producing a surface or contour estimate of the density of incidents (e.g., burglaries) and a dual-variable kernel density estimation routine for comparing the density of incidents to the density of an underlying baseline variable (the secondary file), such as burglaries relative to the number of households).
  • Interpolation II — Head Bang routine for smoothing zonal data that can be applied to events (counts) or rates or can be used to create rates. In addition, there is an interpolated Head Bang routine for interpolating the smoothed Head Bang result to grid cells.
  • Space-time analysis — Set of tools for analyzing clustering in time and in space. These include the Knox and Mantel indices, which look for the relationship between time and space, the Correlated Walk Analysis module, which analyzes and predicts the behavior of a serial offender, and a spatial-temporal moving average.
  • Journey to crime analysis — Simple criminal justice method for estimating the likely location of a serial offender given the distribution of incidents and a model of travel distance. The routine allows the user to estimate a travel model with a calibration file and apply it to the serial events. It can be used to identify a likely location given the distribution of 'points' and assumptions about travel behavior. There is a routine for drawing lines between origins and destinations (crime trips).
  • Bayesian journey to crime analysis — Advanced criminal justice method for estimating the likely location of a serial offender given the distribution of incidents, a model of travel distance, and an origin-destination matrix showing the relationship between where crimes were committed and where offenders lived. A diagnostics routine analyzes serial offenders whose residence is known and estimates which of several journey to crime estimates is most accurate. A selected method can be applied to identify a likely residence location of a single serial offender given the distribution of incidents, assumptions about travel behavior and the origin of offenders who committed crimes in the same locations.

Section 5: Spatial Modeling II

  • • Regression modeling I — Module for analyzing the relationship between a dependent variable and one or more independent variables. The CrimeStat regression module includes Normal (Ordinary Least Squares), Poisson-based and Binomial Logit regression models, estimated by Maximum Likelihood (MLE) or Markov Chain Monte Carlo (MCMC) algorithms. The current version includes 30 different models including Normal/OLS, Poisson with Linear Dispersion Correction, Poisson-Gamma, Poisson-Lognormal, and Binomial Logit non-spatial regression models, and Poisson-Gamma-Conditional Autoregressive (CAR), Poisson-Gamma-Simultaneous Autoregressive (SAR), Poisson-Lognormal-CAR/SAR, and Binomial Logit-CAR/SAR spatial regression models. For the MCMC models, an exposure variable can be defined to allow an analysis of risk. The module can handle very large datasets through a Block Sampling approach.
  • Regression modeling II — Module for predicting the dependent variable with the coefficients of one or more independent variables that have been estimated with the Regression I module.
  • Discrete choice modeling I — Module for analyzing the relationship between a discrete (nominal) dependent variable and one or more independent variables. The CrimeStat discrete choice module includes both Multinomial Logit and Conditional Logit models. There is also a utility for creating a dataset appropriate for the Conditional Logit model.
  • Discrete choice modeling II — Module for predicting a discrete dependent variable with the coefficients of one or more independent variables that have been estimated with the Discrete choice I module.
  • Time series forecasting — Time series module that monitors crime or other counts by specific geographical areas (districts) and detects unusual levels of activity in the latest time period. It also forecasts expected counts in the next time period.

Section 6: Crime Travel Demand Modeling

Crime travel demand modeling is an application of travel demand modeling, widely used in transportation planning, to crime analysis. The analysis is done by zones.

First, a crime trip is defined as a link between an offender residence (origin) and a crime location (destination).

Second, the model is run sequentially in four separate stages with multiple routines in several stages:

  • Trip generation — Produces separate models that predict the number of crimes originating in each zone (origins) and the number of crimes ending in each zone (destinations). CrimeStat IV uses multivariate Poisson-based regression models, either MLE or MCMC, to create the prediction. Trips from outside the study area (external trips) can be added to the origin model to ensure that all trips are included. Once the models are created, a balancing procedure ensures that the number of origins equals the number of destinations.
  • Trip distribution — Using the predicted number of crime trips originating in each zone and the predicted number of trips occurring in each zone, the second stage distributes trips from each zone to every other zone using a gravity model. There are routines for calculating the actual (observed) distribution from individual data, for estimating the prediction coefficients, and for applying the predicted coefficients to the predicted origins and destinations. Another routine allows a comparison of the predicted trip distribution with the observed trip distribution.
  • Mode split — The predicted number of trips for each zone-to-zone pair can be split into likely travel modes using an accessibility function that approximates the utility of one mode relative to the others.
  • Network assignment — The predicted trips from each zone to every other zone by travel mode are assigned to a likely route based on the A* shortest path algorithm. The output includes the likely routes taken for each origin-destination zone pair and the total volume of trips on network links. This step requires a travel network, one for each travel mode. There are additional utilities for calculating transit networks from station/stop locations and for testing for one-way streets.

Section 7: Options

  • Parameters can be saved and re-loaded.
  • Tab colors can be changed.
  • Monte Carlo simulation data can be output.
  • If the coordinate system is spherical (lat/lon), then the primary file can be saved as a kml file for display in Google Earth.
  • Excel xlsx and xls files can be converted to dbf files for use in CrimeStat.

CrimeStat is accompanied by sample datasets and a manual that gives the background behind the statistics and examples. The manual also discusses applications of CrimeStat developed by other analysts and researchers. The program and sample data sets are in Windows-based zipped files that can be downloaded. The manual is a set of individual chapters in PDF files. They can be viewed online or downloaded. If downloading the PDF chapters separately, they should be saved into the same directory as the CrimeStat program. If the PDF file names are not renamed, they can be accessed directly from other chapters or from the program's help menu.

CrimeStat Libraries

The CrimeStat Libraries (version 1.1) are component objects that allow for the functions of CrimeStat to be programmed directly into custom software or systems. The CrimeStat Libraries include all of the routines that were developed through version 2.0 of the regular CrimeStat program, including spatial description, hot spot analysis and kernel density interpolation routines. Additional spatial autocorrelation routines have been included. The libraries can input dbf, point shape and ASCII text files and can output to shape file, ASCII text files and kml files.

Copyright

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

Citation

The program must be cited correctly in any publication or report that uses results from the program. The author's suggested citation is:

Ned Levine (2014). CrimeStat: A Spatial Statistics Program for the Analysis of Crime Incident Locations (v 4.01). Ned Levine & Associates, Houston, Texas, and the National Institute of Justice, Washington, D.C. August.

Unless otherwise stated, individual chapters were authored by Ned Levine.

Contact Information
Questions relating to the software, sample data, or manual should be directed to:
Dr. Ned Levine
Ned Levine and Associates
crimestat@nedlevine.com

Date Modified: November 18, 2013