Developing a fractal model for spatial mapping of crime hotspots
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Developing a fractal model for spatial mapping of crime hotspots Mohsen Kalantari 1,2 & Somaye Ghezelbash 1 & Reza Ghezelbash 3 & Bamshad Yaghmaei 4
# Springer Nature B.V. 2019
Abstract Determining the precise area of urban crime hotspots, where the crime rate is significantly different from the surrounding area, is one of the essential applications of crime analysis. Besides, the classification of the spatial distribution of crimes can make spatial analysis more applicable to the police. In this regard, the present study examined the spatial distribution of five types of burglary in Zanjan city, NW Iran. Kernel density estimation (KDE) was initially applied to identify the crime distribution in the raster format. Because of producing a continuous-value surficial signature, the KDE model is not able to determine the exact borders of different crime density. To address this issue, the concentration-area (C-A) fractal model is proposed in this study as a frequency–spatial method for class designation of crime density. Thus, based on the interpolated maps derived from KDE, the C-A log–log plots consisting of the values of the gridded density maps of burglary crimes versus their occupied area were generated. Then, different threshold values and their corresponding criminal classes were delineated and mapped according to the drawn log–log plots. To evaluate the performance of the proposed fractal model, two GIS-based models, namely natural breaks classification (NBC) and equal interval classification (EIC), were generated over the KDE values. Then, a success rate curve was plotted as a validation method for quantitative evaluation of generated models based on the crime events. The obtained results revealed the benefit of the C-A fractal model in distinguishing different discretized classes of crime density over two other models. This can help to select the appropriate strategy to control and prevent crime occurrences in every crime density class. Keywords KDE . C-A fractal . Spatial analysis . Crime hotspots . Clustering . Success-rate curve
Introduction Criminal activities tend to concentrate in certain places due to the interaction between victims and offenders and the strength of guardianship (Cohen and Felson 1979). One of the most * Mohsen Kalantari [email protected] Extended author information available on the last page of the article
M. Kalantari et al.
common and innovative uses of crime mapping is to collect numerous crime incidents into hotspot maps. Following the innovative work by the Chicago school (Shaw and McKay 1942), the development of Geographical Information Systems (GIS), and the popularity of crime mapping, crime pattern analysis has focused on hotspot analysis techniques (Nasar and Fisher 1993; Ratcliffe and McCullagh 1998; Sherman 1995; Weisburd and Green 1995), which are developed to identify high-crime-density areas (Eck et al. 2005). Hotspot analysis helps the police to identify high-crime areas, types of crime committed, and the best way to respond. Using hotspots to identify the
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