This is the fifth of five chapters on "Spatial Modeling I" from the user manual of CrimeStat IV, a spatial statistics package that can analyze crime incident location data.
This chapter, "Bayesian Journey-to-Crime Modeling" profiles this CrimeStat module (Bayesian Jtc), which includes tools for estimating the likely residence location of a serial offender. It is an extension of the journey-to-crime routine that uses a travel distance function to make an estimate about the likely residence location of a serial offender (See Chapter 13 of the user manual). The Bayesian Jtc routine adds information about specific origins (residence or work locations) of offenders who committed crimes in the same locations to the Jte to update the estimate. First, the chapter explains the theory behind the Bayesian Jtc routine. Second, data requirements are discussed. Third, the routine is illustrated with data from Baltimore County and from Chicago. Fourth, the use of probability filters as extensions is illustrated. Fifth, guidelines for analysis are presented. The chapter notes that although the Bayesian Jtc methodology is an improvement over the current journey-to-crime method and appears to be as good as and more useful than the center of minimum distance, it still has a substantial amount of error, which reflects the inherent mobility of offenders, especially those living in suburbs outside of central cities; they tend to have means of traveling to locations outside of their residential neighborhoods. 32 references
- Thinking, Fast and Slow? Some Field Experiments to Reduce Crime and Dropout in Chicago
- Sustaining the Crime Reduction Impact of Designing Out Crime: Re-Evaluating the Secured by Design Scheme 10 Years On
- Focused Deterrence Strategies and Crime Control: An Updated Systematic Review and Meta-Analysis of the Empirical Evidence