Abstract
Using a 6-year dataset of theft and burglary crime incidents in the Tai Yang Gong (TYG) area (with relatively high crime rates and diversified crime patterns) under a police station’s jurisdiction, we study the seasonality of property crimes in a neighborhood-scale of Beijing, China. First, root mean square error (RMSE) of temperature and the count of crime incidents is adopted to evaluate the crime seasonality. Second, we explore the spatial hotspots of both theft and burglary crimes in the TYG area by kernel density estimation method, and further analyze the data from different hotspots to examine whether variability of seasonality exists across space. The results show that the seasonality of theft is less significant than that of burglary based on the data from the whole TYG area. The results also indicate that the seasonality of property crimes has spatial variability. Specifically, in the residential areas with relocated communities which are the spatial hotspots in the paper’s discussion, the seasonality of burglary is less significant compared with the whole area; similarly, the seasonality of theft in the shopping malls with large supermarkets, another type of spatial hotspots, is less significant than that in the whole TYG area. In a neighborhood-scale, variability on offender’s motivation, target’s presence and guardians across space results in the differences of crime occurrence within different areas, and different areas have different crime patterns such as crime generator and crime attractor, which may not significantly be controlled by the general mechanism of crime seasonality.
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The authors appreciate support for this paper by the National Natural Science Foundation of China (Grant No. 71704183) and Basic Scientific Research Project of People’s Public Security University of China (Grant No. 2018JKF228).
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Zeng, Z., Hou, M., Tang, Z., Wu, H., Hu, X. (2020). Seasonality of Property Crimes in a Neighborhood-Scale of Beijing, China. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_121
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