Journal of Geo-information Science ›› 2019, Vol. 21 ›› Issue (11): 1655-1668.doi: 10.12082/dqxxkx.2019.190358

    Next Articles

Hotspot Prediction of Public Property Crime based on Spatial Differentiation of Crime and Built Environment

LIU Lin1,2,3,4,*(), JI Jiakai1,2, SONG Guangwen3, LIAO Weiwei1,2, YU Hongjie1,2, LIU Wenjuan1,2   

  1. 1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
    2. Guangdong Provincial Engineering Research Center for Public Security and Disaster, Guangzhou 510275, China
    3. Center of Geographic Information Analysis for Public Security, School of Geographic Sciences, Guangzhou 510006, China
    4. Department of Geography, University of Cincinnati, Cincinnati OH 45221-0131, Ohio, USA
  • Received:2019-07-05 Revised:2019-10-08 Online:2019-11-25 Published:2019-12-11
  • Contact: LIU Lin E-mail:lin.liu@uc.edu
  • Supported by:
    National Key R&D Program of China(No.2018YFB0505500);National Key R&D Program of China(No.2018YFB0505503);Key Program of National Natural Science Foundation of China(No.41531178);Key Project of Science and Technology Program of Guangzhou City, China(No.201804020016);Research Team Program of Natural Science Foundation of Guangdong Province, China(No.2014A030312010)

Abstract:

Machine learning is the mainstream method for crime hotspot prediction. As a popular machine learning algorithm, the random forest algorithm is widely used in the construction of crime hotspot prediction models because of its ability of handling sparse data, and reliable predictive capability and accuracy. A number of studies use multi-source data representing the geographical environment and built environment to train and construct crime hotspot prediction models. Some are theory-driven, while others more data-driven. Most crime prediction models are global models, by constructing a single model for the entire study area. These models do not fully consider the spatial variations of crime and the built environment, as well as the varying relationship between crimes and the built environment. This paper aims to fill in this gap, using public property crime as an example to demonstrate that crime prediction models can be improved by incorporating the aforementioned spatial variations and spatially varying relationship. Firstly, according to the distribution of historical crime events and the distribution of past crime hotspots, the research area was divided into four subareas: stable high-heat grids, high-heat grids, even-hot grids, and non-hot grids. Then, according to the social disorganization theory, routine activity theory, and crime pattern theory, the three covariates including the urban village, the road network, and POI (catering, entertainment and shopping malls as crime attractors and generators) were used as the covariates representing the surrounding built environment. The random forest prediction model also used historical crime data for training and validation. Different models were created for the whole study area and each of the four subareas. The results of 26 bi-week crime hotspot prediction experiments in 2017 were compared, showing that, after adding the three covariates representing the built environment, the prediction accuracy of the entire study area, stable high-heat grids, and high-heat grids were all improved. More importantly, the subarea models were substantially more accurate than the whole model. These findings strongly endorse that incorporating spatial differentiation of crime and the built environment plays a critical role in improving the performance of the prediction models. The majority of the hotspots coincide with commercial facilities that serve as crime generators or attractors. Thus, crime prevention and control should target urban villages and the areas where road densities are high. Further, the differences in subarea based models also suggest any crime fighting strategies should be adjusted to fit each local subarea, to achieve the greatest efficiency.

Key words: crime hotspot prediction, public property crime, random forest, built environment, spatial differentiation, stable high-heat grids, police strategy