Abstract
Patterns in crime vary quite substantially at different scales of aggregation, in part because data tend to be organized around standardized, artificially defined units of measurement such as the census tract, the city boundary, or larger administrative or political boundaries. The boundaries that separate units of data often obscure the detailed spatial patterns and muddy analysis. These aggregation units have an historic place in crime analysis, but increasing computational power now makes it possible to start with very small units of analysis and to build larger units based on theoretically defined parameters. This chapter argues for a crime analysis that begins with a small spatial unit, in this case individual parcels of land, and builds larger units that reflect natural neighborhoods. Data are limited in these small units at this point in time, but the value of starting with very small units is substantial. An algorithm based on analysis of land unit to unit similarity using fuzzy topology is presented. British Columbia (BC) data are utilized to demonstrate how crime patterns follow the fuzzy edges of certain neighborhoods, diffuse into permeable neighborhoods, and concentrate at selected high activity nodes and along some major streets. Crime patterns that concentrate on major streets, at major shopping centers and along the edges of neighborhoods would be obscured, at best, and perhaps missed altogether if analysis began with larger spatial units such as census tracts or politically defined neighborhood areas.
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Brantingham, P.L., Brantingham, P.J., Vajihollahi, M., Wuschke, K. (2009). Crime Analysis at Multiple Scales of Aggregation: A Topological Approach. In: Weisburd, D., Bernasco, W., Bruinsma, G.J. (eds) Putting Crime in its Place. Springer, New York, NY. https://doi.org/10.1007/978-0-387-09688-9_4
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DOI: https://doi.org/10.1007/978-0-387-09688-9_4
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