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The spatial dependency of crime increase dispersion

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Abstract

A number of analytical techniques (such as the Gini coefficient and the Lorenz curve) can identify unequal distributions in crime frequency among sub-areas within a study region; however, these tools are often aspatial and say nothing about the relationships between spatial units. Using dispersion analysis, a technique that measures the relative dispersion of a crime increase across a region allows for the identification of particular spatial units that are sufficiently influential to drive up the overall jurisdictional crime rate. In this article, a combination of the order of areal units from a dispersion analysis with a measure of the local level of spatial association is used to develop a tool that can identify clustered areas of emerging crime problems. The identification of these second-order spatial processes may be beneficial to police departments and crime prevention practitioners who are interested in the identification of statistically significant clusters of emerging crime hotspots. The process is demonstrated with an example of robbery rates in police sectors of Philadelphia, PA.

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Notes

  1. Chilvers (1998) explains how this can be done for a population-corrected rate, but frequency counts are used in this article for simplicity and demonstration purposes.

  2. A stand-alone software program that calculates the ODI and NCDI is available as a free download from the author's website at www.jratcliffe.net.

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Ratcliffe, J. The spatial dependency of crime increase dispersion. Secur J 23, 18–36 (2010). https://doi.org/10.1057/sj.2009.16

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