page_title: CrimeStat
section_title: Statistical routines
hierarchical_section_title: CrimeStat / Functionality / Statistical routines
caption_attribution_description: English: CrimeStat Dual Kernel
context_page_description: CrimeStat is a crime mapping software program. CrimeStat is Windows-based program that conducts spatial and statistical analysis and is designed to interface with a geographic information system. The program is developed by Ned Levine & Associates under the direction of Ned Levine, with funding by the National Institute of Justice, an agency of the United States Department of Justice. The program and manual are distributed for free by NIJ.
  CrimeStat performs spatial analysis on objects located in a GIS. The objects can be points, zones or lines. The program can analyze the distribution of the objects, identify hot spots, indicate spatial autocorrelation, monitor the interaction of events in space and time, and model travel behavior.
  There is a regression module for non-linear spatial modeling. Some of its tools are specific to crime analysis. Others can by applied in many fields. There are 55 statistical routines in the program.
context_section_description: The spatial description routines include:
  Spatial distribution statistics (mean center, standard deviation ellipse, center of minimum distance, median center, directional mean, convex hull)
  Spatial autocorrelation statistics for zonal data (Moran's I, Getis's G, Getis-Ord Global G, Moran correlogram, Geary correlogram, Getis-Ord correlogram),
  Distance-based statistics among points (nearest neighbor analysis, Ripley's "K", the allocation and summation of Primary File points to Secondary File points, and various distance calculation matrices), and
  Cluster analysis statistics for points, zones or lines.  CrimeStat has a range of routines available for cluster identification:
  Monte Carlo simulations can be run on many routines to estimate credible intervals.
  The spatial modeling routines include:
  Single kernel density interpolation for examining variation over a region of a single variable
  Dual kernel density interpolation of two variables (e.g., a set of events in relation to a population ‘at risk’)
  Head Bang routine for smoothing zonal data
  ”Interpolated Head Bang surface that interpolates the Head Bang estimates to a grid
  ”Knox and Mantel indexes that identify the interaction between space and time in events
  Correlated Walk Analysis, based on random walk theory, for modeling the sequential behavior of a serial offender in space and time and makes a prediction about the next event
  Journey-to-crime analysis for modeling the likely origin of a serial offender based on the location of prior events committed by the offender (geographic profiling)
  Bayesian Journey-to-crime which is an empirical Bayes method that integrates the Journey-to-crime estimate with information on the residence location of other serial offenders who committed crimes in the same places to produce an updated estimate.  The diagnostic routine compares this estimate with its components in predicting the residence location for multiple serial offenders
  Bayesian Journey-to-crime estimation which applies the Bayesian Journey-to-crime method to estimate the location of one serial offender
  Spatial regression.  The models include ordinary least squares, Poisson regression and various other generalized linear models for count data. In addition there are Markov chain Monte Carlo routines for fitting Poisson-Gamma and Poisson-Lognormal models, including where these have a conditional spatial autoregression (CAR or SAR) adjustment.
  The Crime Travel Demand module models crime travel over a metropolitan area.  It is an application of travel demand modeling to crime or other rare events. The purpose is to calibrate the travel behavior of a large number of offenders in committing crimes as a basis for modeling alternative interventions by law enforcement
caption: Auto theft risk in Baltimore County produced by CrimeStat dual kernel density routine.
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