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

• NCJ Number: 242989
• Author(s): Ned Levine
• Date Published: July 2013
• Length: 44 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 fifth 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, "Mode Split Modeling," discusses the third step in the crime travel demand model, i.e., "mode split,", which involves separating ("splitting") the predicted crime trip from each origin zone to each destination zone into distinct travel modes, e.g., walking, bicycling, driving, train, bus. This is the "weakest link" in the analysis, since there is little available information on travel mode by offenders. This requires a dependence on the intuitive adaptation of existing theory of travel-mode choice to crime data. The chapter first presents the theoretical background underlying the mode-split module. This is followed by a discussion of the model, illustrating its use with data from Baltimore County, MD. A discussion of the utility of travel and mode choice addresses discrete choice analysis, multinomial logit function, generalized relative utility function, measuring travel costs, and the aggregate and individual utility functions. The two sets of tools for estimating mode split in CrimeStat are reviewed. If individual-level data can be obtained on travel modes, then the multinomial logit model in the Discrete Choice module can be used. If such data are not available, which is the usual case, then an approximation to a utility function can be made. The approach is to estimate a relative accessibility function and then apply that function to the predicted trip distribution. The chapter concludes with a review of the usefulness and limitations of the mode split modeling of crime trips and the methodology used. Although this step in crime travel demand modeling requires building more systematic databases that document travel modes, the possibilities are important for crime analysts and researchers. 15 figures and 26 references