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CrimeStat 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 new version is 3.0 (CrimeStat III).
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 ArcViewAE, MapInfoAE, Atlas*GISTM, SurferAE for Windows, and ArcView Spatial Analyst(c).
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. New in CrimeStat III is 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, Moran's I spatial autocorrelation index, or directional mean. New in CrimeStat III is a convex hull routine and a Moran correlogram that calculates Moran's I for different distance separations.
Distance analysis I - statistics for describing properties of distances between incidents including nearest neighbor analysis, linear nearest neighbor analysis, and Ripley's K statistic. New in CrimeStat III is 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, and Anselin's local Moran statistics. The STAC and K-means hot spots can be output as ellipses or convex hulls.
Interpolation - 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).
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. New in CrimeStat III is 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.
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 calcuating 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.
| Quarter/Year | File Downloads | Unique Visitors | Page Views | Session Count |
|---|---|---|---|---|
| Apr.-June 2008 | 55,726 | 7,212 | 75,302 | 13,829 |
| Jan.-Mar. 2008 | 49,580 | 6,725 | 68,329 | 12,309 |
| Oct.-Dec. 2007 | 53,883 | 6,336 | 72,217 | 10,920 |
| July-Sept. 2007 | 37,857 | 5,544 | 53,119 | 10,258 |
| Apr.-June 2007 | 114,638 | 6,122 | 133,107 | 11,744 |
| Jan.-Mar. 2007 | 41,167 | 6,379 | 58,912 | 10,365 |
| Oct.-Dec. 2006** | 41,039 | 3,846 | 49,129 | 6,282 |
| September 2006 | 9,130 | 1,229 | 15,332 | 1,793 |
| August 2006 | 8,016 | 1,088 | 9,908 | 1,494 |
| July 2006 | 8,789 | 1,110 | 10,856 | 1,748 |
| June 2006 | 9,133 | 1,075 | 11,194 | 1,662 |
| May 2006 | 13,816 | 1,266 | 16,126 | 1,900 |
| April 2006 | 15,776 | 1,413 | 18,233 | 2,090 |
| March 2006 | 13,516 | 1,438 | 16,479 | 2,251 |
| February 2006 | 12,939 | 1,475 | 15,367 | 1,944 |
| January 2006 | 12,337 | 1,380 | 14,708 | 1,840 |
| December 2005 | 11,284 | 1,761 | 16,062 | 2,652 |
| November 2005 | 13,390 | 1,566 | 15,787 | 2,034 |
| October 2005 | 13,207 | 1,542 | 15,339 | 1,939 |
| September 2005 | 14,750 | 1,447 | 16,826 | 1,761 |
| August 2005 | 13,093 | 1,100 | 14,872 | 1,475 |
| July 2005 | 7,283 | 933 | 8,958 | 1,250 |
| June 2005 | 9,675 | 987 | 11,559 | 1,247 |
| May 2005* | 12,536 | 1,450 | 16,385 | 1,954 |
| April 2005 | 2,554 | 431 | - | - |
| March 2005 | 2,491 | 381 | - | - |
| February 2005 | 2,310 | 366 | - | - |
| January 2005 | 2,053 | 338 | - | - |
| December 2004 | 1,629 | 282 | - | - |
| November 2004 | 2,112 | 358 | - | - |
| October 2004 | 2,272 | 376 | - | - |
| September 2004 | 1,894 | 323 | - | - |
| August 2004 | 2,114 | 348 | - | - |
| July 2004 | 2,378 | 405 | - | - |
| June 2004 | 2,558 | 390 | - | - |
| May 2004 | 2,926 | 461 | - | - |
*Information on page views and session count was unavailable prior to May 2005. In early May 2005, we had a number of changes:
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.