GIS and Crime Analysis

Spatial analysis of crime has gained increasing attention during the past thirty years, coupled with the growth of geographic information systems (GIS). Most crime analysis tasks are either carried out in a GIS environment or supported by a GIS. GIS is typically used as a tool for data management, data processing, data visualization, and data analysis for crime studies. Crime analysis normally involves the following elements: uncovering spatio-temporal patterns of crime distribution, such as crime hotspots; explaining these patterns and discerning major contributing factors based on multivariate regression modeling; predicting future crime patterns using machine learning and other predictive methods; developing crime prevention approaches based on historical and future crime patterns; and evaluating 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. It should be noted that crime analysis is inherently multidisciplinary, including but not limited to geography, criminology, computer science, statistics, urban planning, and sociology. Therefore, an effective crime analyst should be well trained in multiple disciplinary approaches. Any crime analysis that leads to real-world impact must rely on sound theories and effective methodologies. Many of the theories covered in this article are related to geography, criminology, and sociology. The methods are mostly influenced by GIS, spatial statistics, and artificial intelligence. Crime analysis also involves multiple stakeholders, including at least government agencies, universities, and private companies. Universities conduct basic and applied research, private companies convert the research to products, and government agencies provide funding for research and implement crime prevention strategies. In addition, crime analysis needs to pay close attention to potential issues related to ethics, privacy, confidentiality, and discrimination.