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- W3214432783 abstract "Despite the considerable deployed resources, current policing efforts are failing to stop crimes before they start, and therefore, also failing to adequately protect lives and property. To promote the intelligent transformation from reactive to proactive policing, this study proposed a hierarchical crime prediction framework. First, the temporal dependency in the frequency domain was decomposed and a network constructed to capture the spatial relationships within the sub-frequencies. Human mobility in a city was then utilized to characterize the dynamic relationships within the network. Using the proposed framework, this study examined the crime distribution evolution in Chicago to holistically predict the short-term crimes in the different communities. The framework was found to have high predictive accuracy and significant potential in promoting proactive policing. It was concluded that: (1) as the crime distribution evolution comes from the spatial relationship changes, these dynamic relationships are critical in explaining and characterizing the evolution; and (2) the social interactions constructed using the human activity data can characterize the dynamic crime distribution relationships. • Development of a model to forecast short-term crime in a community network. • Decomposing time series and capturing spatial patterns within decompositions. • Utilizing taxis to proxy mobility within and between communities. • Explaining how crime distribution change within and across communities." @default.
- W3214432783 created "2021-11-22" @default.
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- W3214432783 date "2022-03-01" @default.
- W3214432783 modified "2023-10-15" @default.
- W3214432783 title "Static or dynamic? Characterize and forecast the evolution of urban crime distribution" @default.
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- W3214432783 doi "https://doi.org/10.1016/j.eswa.2021.116115" @default.
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