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Journey to Crime

Analyzing an Offender’s Journey to Crime: A Criminal Movement Model (CriMM)

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Abstract

In the current study we develop a Criminal Movement Model (CriMM) to investigate the relationship between simulated travel routes of offenders along the physical road network and the actual locations of their crimes in the same geographic space. With knowledge of offenders’ home locations and the locations of major attractors, we are able to model the routes that offenders are likely to take when travelling from their home to an attractor by employing variations of Dijkstra’s shortest path algorithm. With these routes plotted, we then compare them to the locations of crimes committed by the same offenders. This model was applied to five attractor locations within the Greater Vancouver Regional District (GVRD) in the province of British Columbia, Canada. Information about offenders in these cities was obtained from five years worth of real police data. After performing a small-scale analysis for each offender to investigate how far off their...

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Evaluating The Usefulness Of Functional Distance Measures When Calibrating Journey-to-Crime Distance Decay Functions

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Abstract

This research evaluates the usefulness of applying functional distance measures to criminal geographic profiles using mathematically calibrated distance decay models. Both the travel- path (i.e., shortest distance) and temporally optimized (i.e., quickest travel time) functional distance measures were calculated based on the impedance attributes stored within a linearly referenced transportation data layer of several parishes in Louisiana. Two different journey-to- crime distance decay functions (i.e., negative exponential, and truncated negative exponential) were mathematically calibrated for ‘‘best fit’’, based on the distribution of distances between homicide crime locations and offenders residences. Using the calibrated distance decay functions, geographic profiles were created for a localized serial killer from Baton Rouge, Louisiana. A probability score was calculated for every point within the study area to indicate the likelihood that it contained the serial offenders residence. A comparison between the predicted (highest probability score) and the actual residence of the serial offender determined the predictive value and procedural validity of functional distance metrics.

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