Abstract:The relationship between crime and urban environment has always been the focus of crime geography. Like diseases which can transmit and diffuse, crimes may also spread during a certain period of time and to a certain area by the near-repeat effect. Traditional near-repeat analysis focuses on the spatial spread of crimes to adjacent areas, with little regard to the displacement effect. Crime displacement refers to the relocation of criminal events as a result of policing efforts. If this phenomenon is neglected, the near-repeat analysis will tend not to obtain the overall spatial distribution pattern of criminal cases, leading to limited effectiveness of crime control. This paper presents a non-homogeneous diffusion model where crime spreads not only to spatially and temporally adjacent areas, but also to areas with similar environmental characteristics. By virtue of spatial constraints and environmental characteristics, the most vulnerable areas are identified, and this will be helpful for developing policing strategy as well as for sustainable community development.

As a major social issue during urban development, crime is closely related to socioeconomic, geographic, and environmental factors. Traditional crime prediction models reveal the spatiotemporal dynamics of crime risks, but usually ignore the environmental context of the geographic areas where crimes occur. Therefore, it is difficult to enhance the spatial accuracy of crime prediction. We propose the use of anisotropic diffusion to include environmental factors of the evaluated geographic area in the traditional crime prediction model, thereby aiming to predict crime occurrence at a finer scale regarding spatiotemporal aspects and environmental similarity. Under different evaluation criteria, the average prediction accuracy of the proposed method is 28.8%, improving prediction accuracy by 77.5%, as compared to the traditional methods. The proposed method can provide strong policing support in terms of conducting targeted hotspot policing and fostering sustainable community development.

A critical issue in the geography of crime is the quantitative analysis of the spatial distribution of crimes which usually changes over time. In this paper, we use the concept of exchange mobility across different time periods to determine the spatial distribution of the theft rate in the city of Wuhan, China, in 2016. To this end, we use a newly-developed spatial dynamic indicator, the Local Indicator of Mobility Association (LIMA), which can detect differences in the spatial distribution of theft rate rankings over time from a distributional dynamics perspective. Our results provide a scientific reference for the evaluation of the effects of crime prevention efforts and offer a decision-making tool to enhance the application of temporal and spatial analytical methods.