Optimization of Criminal HotSpots Based on Underlying Crime Controlling Factors Using Geospatial Discriminative Pattern

Wang, Dawei; Ding, Wei; Stepinski, T. F.; Salazar, Josue; Lo, Henry Z.; Morabito, Melissa S.

doi:10.1007/978-3-642-31087-4_57

Cited by 5 publications

References 11 publications

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Spatio-Temporal Crime HotSpot Detection and Prediction: A Systematic Literature Review

et al. 2020

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The primary objective of this study is to accumulate, summarize, and evaluate the state-ofthe-art for spatio-temporal crime hotspot detection and prediction techniques by conducting a systematic literature review (SLR). The authors were unable to find a comprehensive study on crime hotspot detection and prediction while conducting this SLR. Therefore, to the best of author's knowledge, this study is the premier attempt to critically analyze the existing literature along with presenting potential challenges faced by current crime hotspot detection and prediction systems. The SLR is conducted by thoroughly consulting top five scientific databases (such as IEEE, Science Direct, Springer, Scopus, and ACM), and synthesized 49 different studies on crime hotspot detection and prediction after critical review. This study unfolds the following major aspects: 1) the impact of data mining and machine learning approaches, especially clustering techniques in crime hotspot detection; 2) the utility of time series analysis techniques and deep learning techniques in crime trend prediction; 3) the inclusion of spatial and temporal information in crime datasets making the crime prediction systems more accurate and reliable; 4) the potential challenges faced by the state-of-the-art techniques and the future research directions. Moreover, the SLR aims to provide a core foundation for the research on spatio-temporal crime prediction applications while highlighting several challenges related to the accuracy of crime hotspot detection and prediction applications.

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