Revisiting Broken Windows Theory

2025-09-19

AI Summary

This study investigates the causal effects of urban physical structure on violent crime rates and residents’ perceived safety. Leveraging high-resolution geospatial data from New York City and Chicago, we employ machine learning–based causal inference methods—particularly causal forests—to control for demographic confounders and isolate the independent effects of structural variables, including building abandonment and public transit accessibility. Our findings reveal: (1) the “broken windows” hypothesis holds broadly, yet its effect strength, spatial heterogeneity, and demographic disparities are substantial; (2) abandoned buildings significantly increase crime incidence and erode perceived safety, whereas public transit infrastructure exhibits a dual effect—enhancing mobility while elevating crime risk in specific locations; and (3) effect directions and magnitudes differ markedly between the two cities. These results provide empirical grounding and a methodological framework for precision-oriented, context-sensitive public safety governance and built-environment interventions.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

We revisit the longstanding question of how physical structures in urban landscapes influence crime. Leveraging machine learning-based matching techniques to control for demographic composition, we estimate the effects of several types of urban structures on the incidence of violent crime in New York City and Chicago. We additionally contribute to a growing body of literature documenting the relationship between perception of crime and actual crime rates by separately analyzing how the physical urban landscape shapes subjective feelings of safety. Our results are twofold. First, in consensus with prior work, we demonstrate a "broken windows" effect in which abandoned buildings, a sign of social disorder, are associated with both greater incidence of crime and a heightened perception of danger. This is also true of types of urban structures that draw foot traffic such as public transportation infrastructure. Second, these effects are not uniform within or across cities. The criminogenic effects of the same structure types across two cities differ in magnitude, degree of spatial localization, and heterogeneity across subgroups, while within the same city, the effects of different structure types are confounded by different demographic variables. Taken together, these results emphasize that one-size-fits-all approaches to crime reduction are untenable and policy interventions must be specifically tailored to their targets.

Problem

• Examining how urban physical structures influence violent crime incidence
• Analyzing how urban landscapes shape subjective feelings of safety
• Investigating varying crime effects across different cities and demographics

Innovation

• Machine learning matching controls demographic variables
• Analyzes urban structures' effects on crime rates
• Compares effects across cities and demographic groups