This is the seventh of eight chapters on "Crime Travel Demand Modeling" from the user manual for CrimeStat IV, a spatial statistics package that can analyze crime incident location data.
This chapter presents a case study of the crime travel demand model for robbery in Chicago, IL. It illustrates the application of the model to a compact city with substantial transit services. The study found that for robberies that occurred between 8:00 p.m. and 5:59 a.m. for the year 1997, travel patterns were a good predictor of travel distances from a robber's point of origin to the place of the robbery, intra-zonal robberies, and network load for 1998. On the other hand, the 1997 travel patterns only weakly predicted specific links between traffic analysis zones. For 1998 incidents, a trip distribution model (using Poisson regression of the zonal count of robbers' homes and incident locations and an impedance function) modeled the overnight travel links between home and crime. Substituting a lognormal impedance function that better matched the observed overnight robbery pattern resulted in predictions that were nearly as good as the 1997 actual travel patterns. A combination of these predictions with analysis of travel patterns over several years might eventually result in an excellent zonal prediction of crime travel patterns. The study concludes that the crime travel demand model of CrimeStat IV, along with a geographic information system can identify "hot" street segments. These are the segments most likely to be on the travel routes of offenders and most useful for intervention to prevent crime. For researchers, a crime travel demand model is an effective tool for addressing long-term structural questions. The study methodology is described in detail based on the use of CrimeStat's stages of crime travel demand modeling. Limitations to this modeling are also addressed. 2 tables, 8 figures, and 7 references
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