Measuring Crime Concentration Across Cities of Varying Sizes: Complications Based on the Spatial and Temporal Scale Employed
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We argue that assessing the level of crime concentration across cities has four challenges: (1) how much variability should we expect to observe; (2) whether concentration should be measured across different types of macro units of different sizes; (3) a statistical challenge for measuring crime concentration; (4) the temporal assumption employed when measuring high crime locations.
We use data for 42 cities in southern California with at least 40,000 population to assess the level of crime concentration in them for five different Part 1 crimes and total Part 1 crimes over 2005–2012. We demonstrate that the traditional measure of crime concentration is confounded by crimes that may simply spatially locate due to random chance. We also use two measures employing different temporal assumptions: a historically adjusted crime concentration measure, and a temporally adjusted crime concentration measure (a novel approximate solution that is simple for researchers to implement).
There is much variability in crime concentration over cities in the top 5 % of street segments. The standard deviation across cities over years for the temporally adjusted crime concentration measure is between 10 and 20 % across crime types (with the average range typically being about 15–90 %). The historically adjusted concentration has similar variability and typically ranges from about 35 to 100 %.
The study provides evidence of variability in the level of crime concentration across cities, but also raises important questions about the temporal scale when measuring this concentration. The results open an exciting new area of research exploring why levels of crime concentration may vary over cities? Either micro- or macro- theories may help researchers in exploring this new direction.
KeywordsNeighborhoods Crime Aggregation Imputation
This research is supported in part by NIJ Grant 2012-R2-CX-0010.
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