About CrimeStat ®
CrimeStat III is a spatial statistics program for the analysis of crime incident locations, developed by Ned Levine & Associates under the direction of Ned Levine, PhD, that was funded by grants from the National Institute of Justice (grants 1997-IJ-CX-0040, 1999-IJ-CX-0044, 2002-IJ-CX-0007, and 2005-IJ-CX-K037). The program is Windows-based and interfaces with most desktop GIS programs. The purpose is to provide supplemental statistical tools to aid law enforcement agencies and criminal justice researchers in their crime mapping efforts. CrimeStat is being used by many police departments around the country as well as by criminal justice and other researchers. The latest version is 3.3.
The program inputs incident locations (e.g., robbery locations) in 'dbf', 'shp', ASCII or ODBC-compliant 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.
CrimeStat is organized into five sections:
Primary file - this is a file of incident or point locations with X and Y coordinates. 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 - this is an associated file of incident or point locations with X and Y coordinates. The coordinate system has to 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 the risk-adjusted nearest neighbor clustering routine and the duel kernel interpolation.
Reference file - this is a 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 - This 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 III has the ability to utilize 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.
Spatial distribution - statistics for describing the spatial distribution of incidents, such as the mean center, center of minimum distance, standard deviational ellipse, the convex hull, or directional mean.
Spatial autocorrelation - statistics for describing the amount of spatial autocorrelation between zones, including general 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, 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 routine that assigns the primary points to the secondary points, either on the basis of nearest neighbor 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 distance between either the primary or secondary file and the grid.
'Hot spot' analysis I - routines for conducting 'hot spot' analysis including the mode, the fuzzy mode, hierarchical nearest neighbor clustering, and risk-adjusted nearest neighbor hierarchical clustering. The hierarchical nearest neighbor hot spots can be output as ellipses or convex hulls.
'Hot spot' analysis II - more routines for conducting hot spot analysis including the Spatial and Temporal Analysis of Crime (STAC), K-means clustering, Anselin's local Moran, and the Getis-Ord local G statistics. The STAC and K-means hot spots can be output as ellipses or convex hulls. All of these routines can simulate confidence intervals with a Monte Carlo simulation.
Interpolation I - a 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 (e.g., burglaries relative to the number of households).
Interpolation II - a Head Bang routine for smoothing zonal data that can be applied to events (volumes), 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 - a 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, and 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 - a 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 - an 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 for whom their 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.
Regression modeling - a module for analyzing a relationship between a dependent variable and one or more independent variables. The CrimeStat regression module includes both Ordinary Least Squares and Poisson-based regression models, estimated from Maximum Likelihood (MLE) or Markov Chain Monte Carlo (MCMC) algorithms. The current version includes six different models including OLS, Poisson with Linear Dispersion Correction, Poisson-Gamma and a Poisson-Gamma-Conditional Autoregressive (CAR) spatial regression model. The module can handle very large datasets through a Block Sampling approach. There is also a module for applying estimated coefficients to a new dataset to make predictions.
Crime Travel Demand Modeling
Crime travel demand modeling is a new module in CrimeStat III. It is an application of travel demand modeling, widely used in transportation planning, to crime analysis. The analysis is done by zones. First, crime 'trips' are defined as a link between an offender residence/origin location and a crime location. The number of crimes originating in each zone is counted as is the number of crimes ending in each zone. Second, the model is run sequentially in four separate stages with multiple routine in each stage:
Trip Generation - Separate models are produced that predict the number of crimes originating in each zone (origins) and the number of crimes ending in each zone (destinations). CrimeStat III uses a multivariate Poisson regression model, with stepwise options, to create the prediction. Trips from outside the study area (external trips) can be added to the origin model to account for travel from outside the region. 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 - Finally, 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.
Parameters can be saved and re-loaded.
Tab colors can be changed.
Monte Carlo simulation data can be output.
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 the program's help menu.
The CrimeStat Libraries (version 1.0) 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, shape, and Ascii text files and can output to shape file, MIF/MID files, ASCII text files, and KML files.
|Quarter/Year||File Downloads||Unique Visitors||Page Views||Session Count|
*Information on page views and session count was unavailable prior to May 2005. In early May 2005, we had a number of changes:
- CrimeStat III was released.
- We changed the software we used to track file downloads.
- We transformed CrimeStat into its own Web site.
The radical change in download activity is partly due to these changes.
**In Fall 2006 this table was changed to reflect quarterly, rather than monthly, figures. The Fall, Winter, Spring, Summer quarters cover October-December, January-March, April-June, July-September, respectively. Quarterly figures are posted early in the following quarter.