Abstract
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.
Highlights
To understand the geography of crime it is necessary to determine the differences in the spatial distribution of the criminal phenomenon
As the time variation analysis of crime more closely follows the idea presented by Local Indicator of Mobility Association (LIMA), we focus on how to numerically describe the changes in the spatial distribution patterns between different periods
This paper used the concept of exchange mobility with the LIMA method to analyze spatial distribution in relation to the theft rate exchange mobility of theft in Wuhan, China
Summary
To understand the geography of crime it is necessary to determine the differences in the spatial distribution of the criminal phenomenon. Well-known methods exist for incorporating spatial autocorrelation or detecting hot spots, such as eigenvector spatial filtering (ESF) [8,9,10,11,12] and space-time scanning statistics [13,14,15,16,17,18,19] Both these methods are static approaches that detect the differences between geographical units over a specific time range, failing to account for the fact that such a distribution usually changes over time and neglecting temporal and spatial evolutionary patterns. Breetzke and Cohn [21] evaluated monthly differences in assault levels stratified by neighborhood and found assault to be seasonal, with higher incidence rates in summer In other words, they generalized the periodicity of crime distribution. It is difficult to determine how to numerically describe the changes in the spatial distribution pattern between different periods, and this is often ignored
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