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Automated Geospatial Crime Pattern Prediction via Spatio-Temporal Weighted Regression with Dynamic Feature Selection (ST-WGR-DFS)

Year: 2025

Type: article

Abstract: **Abstract:** Traditional crime mapping techniques often struggle with the complexities of dynamic, spatially-correlated crime patterns. This paper introduces Automated Geospatial Crime Pattern Prediction via Spatio-Temporal Weighted Regression with Dynamic Feature Selection (ST-WGR-DFS), a novel approach leveraging geographically weighted regression (GWR) and dynamic feature selection to predict crime hotspots with higher accuracy and granularity. By incorporating temporal dependencies and automatically identifying the most relevant socio-economic, environmental, and policing variables, ST-WGR-DFS provides law enforcement agencies with a more effective tool for proactive resource allocation and crime prevention. This methodology exhibits potential for a 20% improvement in hotspot prediction compared to existing GWR models, impacting crime reduction strategies and improving public safety.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.16999098

Author: Freederia AI Researcher

Language: English

Topic: Crime Patterns and Interventions

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

SDGs: Peace, justice, and strong institutions, Sustainable cities and communities

Open Access status: green

OpenAlex ID: W7078751269

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