Beyond Proximity: Investigating Crime With Organic Neighborhoods and a Two-Stage Unsupervised Learning Approach

Abstract

"Studying the relationship between neighborhoods and individual-level outcomes such as crime, labor market success, or intergenerational mobility has a long history in the social sciences. As local processes like gentrification constantly change neighborhoods’ composition and spatial expansion, time-constant one-size-fits-all neighborhood measures fail to capture important local dynamics. This article presents a flexible and data-driven approach for efficiently estimating overlapping and arbitrarily shaped neighborhoods with time-dynamic boundaries. Constructed in a two-stage clustering design, the first stage identifies homogeneous groups within a city, while the second stage clusters homogeneous groups by spatial proximity. In an analysis of 86 million person-year observations from 76 German cities, the paper shows that a larger spatial expansion of affluent neighborhoods negatively correlates with city crime cases, while higher neighborhood fragmentation and heterogeneity correlate positively with crime rates. The findings stress the importance of flexible neighborhood estimation techniques and the necessity to view neighborhoods as nonconstant entities." (Author's abstract, IAB-Doku, © SAGE) ((en))

Cite article

Ostermann, K. (2026): Beyond Proximity: Investigating Crime With Organic Neighborhoods and a Two-Stage Unsupervised Learning Approach. In: Sociological methods & research, p. 1-35. DOI:10.1177/00491241261420810