Abstract
Crime Geography and spatial analysis of crime has gained great momentum lately, coupled with the advancement of geographic information science (GIScience) and big data in human mobility. According to (Liu in Oxford Bibliographies in Geogr 2021), crime geography and crime analysis normally cover spatio-temporal crime pattern detection, crime explanation, crime prediction, crime prevention and crime intervention assessment. The acronym of DEPPA captures these five elements. Pattern detection uncovers spatio-temporal patterns of crime distribution, such as crime hotspots. Crime explanation aims to discern major contributing factors based on multivariate regression modeling and machine learning. Crime prediction forecasts future crime patterns using machine learning and other predictive methods. Crime prevention devises targeted intervention strategies such as hot spot policing, based on historical and future crime patterns. Assessment examines the effectiveness of crime prevention, to find out if crime is reduced in the targeted area and whether the nearby areas are affected by the intervention. This chapter summarizes some of the latest progresses and challenges of crime geography and crime analysis along the issues of the unit of analysis and spatial scale, comparison analysis, new data and new variables, crime prevention and assessment, and the spatio-temporal mismatch problem.
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References
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Liu, L. (2022). Progresses and Challenges of Crime Geography and Crime Analysis. In: Li, B., Shi, X., Zhu, AX., Wang, C., Lin, H. (eds) New Thinking in GIScience. Springer, Singapore. https://doi.org/10.1007/978-981-19-3816-0_37
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DOI: https://doi.org/10.1007/978-981-19-3816-0_37
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