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.
Highlights
The detection of hot spots is a starting point to policy-relevant understanding of crime and to making predictions about potential crimes
Crime hot spots are often generated based on the discrete data of the criminal events
Others may choose to stop committing crimes, change where they commit crimes, or change the way they go about committing crime in response to the police intervention
Summary
The detection of hot spots is a starting point to policy-relevant understanding of crime and to making predictions about potential crimes. A hot spot might disappear, with some criminal activities moving to another area These changes will make the original data set obsolete. This paper models the displacement and diffusion of residential burglary based on the near-repeat theory. It considers the spatial and temporal proximity relation between data, and considers the rise of risk value due to similarity in environmental characteristics, though geographic entities with similar environmental characteristics are physically far from each other. The targets likely to be burgled but not identified by traditional analysis methods are determined, that is, burglary crime pattern in the entire area is developed using the spatial and environmental data.
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