Too Fine to be Good? Issues of Granularity, Uniformity and Error in Spatial Crime Analysis
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Too Fine to be Good? Issues of Granularity, Uniformity and Error in Spatial Crime Analysis Rafael G. Ramos1 · Bráulio F. A. Silva2 · Keith C. Clarke3 · Marcos Prates4
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Objectives Crime counts are sensitive to granularity choice. There is an increasing interest in analyzing crime at very fine granularities, such as street segments, with one of the reasons being that coarse granularities mask hot spots of crime. However, if granularities are too fine, counts may become unstable and unrepresentative. In this paper, we develop a method for determining a granularity that provides a compromise between these two criteria. Methods Our method starts by estimating internal uniformity and robustness to error for different granularities, then deciding on the granularity offering the best balance between the two. Internal uniformity is measured as the proportion of areal units that pass a test of complete spatial randomness for their internal crime distribution. Robustness to error is measured based on the average of the estimated coefficient of variation for each crime count. Results Our method was tested for burglaries, robberies and homicides in the city of Belo Horizonte, Brazil. Estimated “optimal” granularities were coarser than street segments but finer than neighborhoods. The proportion of units concentrating 50% of all crime was between 11% and 23%. Conclusions By balancing internal uniformity and robustness to error, our method is capable of producing more reliable crime maps. Our methodology shows that finer is not necessarily better in the micro-analysis of crime, and that units coarser than street segments might be better for this type of study. Finally, the observed crime clustering in our study was less intense than the expected from the law of crime concentration. Keywords Criminology of Place · Crime Mapping · Granularity · Scale · Error
* Rafael G. Ramos [email protected] 1
INPE National Institute for Space Research, Av. dos Astronautas, 1.758 ‑ Jardim da Granja, São José Dos Campos, São Paulo 12227‑010, Brazil
2
Department of Sociology, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
3
Department of Geography, University of California Santa Barbara, Santa Barbara, CA, USA
4
Department of Statistics, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
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Vol.:(0123456789)
Journal of Quantitative Criminology
Introduction Crime mapping is a valuable tool: knowing where crime is more frequent leads not only to a more effective and efficient deployment of police forces and other public policies, it can also lead to more accurate models and theories through which the underlying causes can be examined and better understood (Santos 2016; Chainey and Ratcliffe 2013; Wang 2012). The question of where crime takes place, however, implies a definition of “where?” Should crimes be counted and analyzed per police district, or should we use neighborhoods, census tracts, street blocks, or some other
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