Trip Distribution Modeling (CrimeStat IV: A Spatial Statistics Program for the Analysis of Crime Incident Locations, Version 4.0)

• NCJ Number: 242988
• Author(s): Ned Levine; Richard Block; Dan Helms; Phil Canter
• Date Published: July 2013
• Length: 80 pages
• Grant Number(s): 2005-IJ-CX-K037
• Agencies: NIJ-Sponsored
• Publication Type: Technical Assistance; Instructional Material (Programmed)
• Award: The Expansion of the Crimestat Spatial Statistics Program to Incorporate Spatial Regression, Space-Time Clustering, Bayesian Journey to Crime Modeling, Incident Detection Methodology and Sparse Matrix

Annotation

This is the fourth of eight chapters on "Crime Travel Demand Modeling" from the user manual of CrimeStat IV, a spatial statistics package that can analyze crime incident location data.

Abstract

This Chapter, "Crime Trip Distribution," examines the second stage in the travel demand model, i.e., trip distribution, in which the predicted crime origins and the predicted crime destinations are linked to produce crime trips. "Trip distribution" is "a model of the number of trips that occur between each (offender) origin zone and each (offender) destination zone." It uses the predicted number of trips originating in each origin zone (trip production model) and the predicted number of trips ending in each destination zone (trip attraction model). Thus, trip distribution is a model of travel between zones. The modeled trip distribution can then be compared to the actual distribution to determine whether the model produced a reasonable approximation. A discussion of the model's theoretical background notes that the actual distribution of crime trips is complex, stemming from a large number of decisions by offenders, who do not necessarily use the same decision logic. The model, on the other hand, is a simple allocation on the basis of three or sometimes four variables. Still, the simple model can often capture the most important characteristics of the actual distribution. The chapter's discussion of the "gravity" model pertains to the attraction of individuals in social application, crime trips as social interaction, and the negative exponential distance function. The chapter's section on "travel impedance," addresses barriers to travel, such as distance and time, cost, and utility. How these variables impact trip distribution modeling are discussed. The CrimeStat IV trip distribution module is then explained in detail, along with the uses of trip distribution analysis. 3 tables, 22 figures, 35 references, and two attachments on the modeling of DWI trips that end in crashes in Baltimore County, MD, and the targeting of crime on public transport in Greater Manchester, England