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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 < 0.001) but a negligible association in suburban residential areas (coefficient close to 0, 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.

Part of the answer to violent crime prevention is to understand the route that those who have committed violent crimes have traveled in order to find ways to guide others from the road leading to such violence. An investigation of the lifelong personal and environmental factors affecting 37 men who were executed in 1997 focuses on distinctions between men in two categories based on heinousness of violent crime. The study aimed to identify risk factors and events that preceded the violent event and to compare the constellation of variables of the men who committed particularly heinous murders characterized by extreme rage and brutality with those whose crimes and criminal histories were characterized mostly by property crimes without intentional harm to people. Descriptive results suggest differences between the two groups of men related to 19 variables and the emergence of two diverse profiles of risk factors and life experiences.

As partners on the cold case squad, Harris County Texas detectives Harry Fikaris and Roger Wedgeworth attempt to solve the crimes that other detectives couldn't. Unfortunately, DNA testing does not make these officers' jobs any easier.