Seasonality of Property Crimes in a Neighborhood-Scale of Beijing, China

  • Zhaolong Zeng
  • Miaomiao Hou
  • Zheng Tang
  • Huanggang Wu
  • Xiaofeng HuEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)


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.


Seasonality Property crimes Neighborhood-scale Routine activities theory Temperature 



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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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Zhaolong Zeng
    • 1
  • Miaomiao Hou
    • 1
  • Zheng Tang
    • 1
  • Huanggang Wu
    • 2
  • Xiaofeng Hu
    • 1
    Email author
  1. 1.School of Information Technology and Network SecurityPeople’s Public Security University of ChinaBeijingChina
  2. 2.School of International Police StudiesPeople’s Public Security University of ChinaBeijingChina

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