Journal of Geo-information Science ›› 2021, Vol. 23 ›› Issue (1): 43-57.doi: 10.12082/dqxxkx.2021.200247

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Overview and Prospect for Spatial-Temporal Prediction of Crime

GU Haisuo1,*, CHEN Peng1(), LI Huibo2   

  1. 1. School of Information Network Security, People's Public Security University of China, Beijing 102600 China
    2. National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data (PSRPC), Beijing 100043, China
  • Received:2020-05-19 Revised:2020-06-29 Online:2021-01-25 Published:2021-03-25
  • Contact: GU Haisuo,CHEN Peng E-mail:chenpeng@ppsuc.edu.cn
  • Supported by:
    National Natural Science Foundation of Beijing(9192022);National Engineering Laboratory Director Fund for Social Security Risk Perception and Prevention and Control of Large Data Applications

Abstract:

As the core technology of predictive policing, Spatial-Temporal (ST) prediction of crime has developed rapidly from around 2000 to the present. We introduce the basic theory of ST prediction of crime at the beginning. We regard the ST prediction method of crime as a process combining corresponding models to predict the ST distribution of crimes in the future and deconstruct it into relationships between three objects: case, ST backcloth, and individual behavior. Then, based on the input factors of prediction models, we sum up three current main methods, including ① the prediction method based on the information of cases' ST location, ② the prediction method based on the backcloth and the information of cases' ST location, and ③ the prediction method based on individual behavior, the backcloth, and the information of cases' ST location. We further summarize the mechanisms of different methods in detail respectively. In addition, we compare and analyze each method based on their applicable scenarios and predictive capacities. Finally, with the development of big data technology, we present solutions to improve current prediction methods, that are to construct a data-fusion system, refine data granularity, and integrate new types of data. For model optimization, we need to improve the ability of integrating heterogeneous data from multiple sources and balancing the interpretability and predictive ability of models.

Key words: crime prediction, crime spatio-temporal risk, big data, predictive policing, predictive methods, environmental criminology