Geographic Information Science & Technology GIS&T and Law Enforcement

Geospatial Intelligence for Law Enforcement

Discussion post: First, do some minimal web research and find a source/site concerning geospatial intelligence for law enforcement that you would like to share with your classmates. Provide a brief summary and the URL. Then, reflect on this question: Is the application of GIS&T to law enforcement and criminal justice really an application of geospatial intelligence? Why or why not (and if not how could it be)?

Discussion response:

On the one hand, Geographic Information Science & Technology (GIS&T) includes the full range of spatial data handling, analysis, modeling and visualization techniques (Shuler, 2023). From this perspective, many of the ways law enforcement leverages GIS for mapping crime patterns, visualizing hotspots, optimizing patrol routes and resource allocation could indeed be considered applications of geospatial intelligence. In this regard, McCarthy (2024) reports that, “Remote sensing and geographic information science and technology (GIST) [include] development of remote-sensing image classification algorithms, as well as GIS-based modeling and spatial-analysis techniques” (para. 4). [see https://geography.tamu.edu/research/ gist/index.html]

On the other hand, though, remote sensing research deals with deriving terrestrial parameters from aerial or satellite sensor data, GIS is more concerned with handling, processing, and presenting location-based information in ways that generate insights and support spatial decision-making workflows. The two disciplines have distinct yet complementary areas of focus within the broader geospatial sciences (McCarthy, 2024). Likewise, when considering whether this constitutes true geospatial intelligence, in many cases GIS&T does not fully live up to that label. Although law enforcement’s use of GIS and location data is certainly valuable, true intelligence connotes a deeper analytical capability that transcend simply visualizing data to extract insights, make predictions, and inform decision-making. Many law enforcement GIS applications today are still fairly descriptive and retrospective. To evolve into geospatial intelligence, these systems would need predictive analytics, pattern recognition, multi-source data fusion, and potentially even AI/machine learning components to derive more actionable foresight from the geospatial data. Mapping incidents is useful, of course, but elevating it to intelligence that is useful to law enforcement authorities requires more automated analysis to stay ahead of criminal activities.