Exploring the Spatial Relationship Between Crime and Urban Places in Austin

A Geographically Weighted Regression Approach

Bibliographic Data

ID13112950
AuthorsWenji Wang (0009-0002-7805-0270, Columbia University), Yang Song (0000-0003-2734-9838, Texas A&M University, corresponding author), Jie Kong (0000-0002-9405-3204, Columbia University), Z Guo (0000-0001-7900-4853), Guo Zipeng (0009-0006-5806-1692, Texas A&M University), Yunpei Zhang (Texas A&M University), Zheng Zhu (0000-0002-2164-0141, Providence College), Shilin Hu (Providence College)
Year2025
Volume9
Issue9
Pages359-359
Publication date2025-09-08
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueUrban Science (JOURNAL)
Journal identifiersISSN: 2413-8851 • E-ISSN: 2413-8851
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/urbansci9090359
OpenAlexW4414117006
LanguageEN
References cited40

Urban safety is a critical concern for sustainable city development, with crime patterns often linked to localized environmental factors. Understanding the spatial dynamics of safety is critical for informed design and planning of urban environments. This study employs a Geographically Weighted Regression (GWR) approach to investigate how crime in Austin, Texas, correlates with Points of Interest (POIs) such as bars, transit stations, financial businesses, and public spaces, while accounting for localized socio-economic factors. Building on theoretical frameworks like Routine Activity Theory and Crime Pattern Theory, the analysis integrates crime data from the Austin Police Department (APD), POI datasets, and census variables to explore spatially varying relationships often overlooked by traditional global models (e.g., OLS). A novel adaptive geo-grid method refines spatial units by clustering high-density downtown areas into smaller zones and retaining larger grids in suburban regions, ensuring precision without over-fragmentation. Analysis of crime incidents and POI data reveals significant spatial non-stationarity in crime–environment associations. Transportation-related facilities demonstrate strong spatial correlation with crime citywide, particularly forming persistent crime hotspots around transit hubs in areas like Rundberg Lane, South Congress, and East Riverside. Alcohol-related establishments show a strong positive correlation with crime in entertainment districts (coefficient up to 13.5, p 0.05). The GWR model significantly outperforms traditional OLS regression, capturing critical local variations obscured by global models. Downtown Austin emerges as a complex hotspot for urban safety where multiple high-risk POI types overlap. This research advances urban design and planning knowledge by providing empirical evidence that environmental factors’ influence on safety is spatially conditional rather than universally consistent, aligning with Crime Pattern Theory and Routine Activity Theory. The findings support place-specific crime prevention strategies, offering policymakers data-driven insights for developing targeted design strategies for urban zones

Census · Downtown · Geographic information system · Geographically Weighted Regression · Public transport · Spatial analysis · Spatial correlation · Spatial ecology · Spatial relationship · Urban planning · Crime Patterns and Interventions · Land Use and Ecosystem Services · Urban Transport and Accessibility

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