Street View Sociability: Interpretable Analysis of Urban Social Behavior Across 15 Cities
Kieran Elrod, Katherine Flanigan, Mario Berg\'es

TL;DR
This study demonstrates that street view images can be analyzed using large language models to infer urban social behaviors, aligning with urban planning theories and offering a scalable method for studying city sociability.
Contribution
Introduces a novel approach using multimodal language models to interpret social interactions from street view imagery based on established urban sociability theories.
Findings
Sky view index correlates with all sociability types.
Green view index predicts enduring sociability.
Place attachment links positively with fleeting sociability.
Abstract
Designing socially active streets has long been a goal of urban planning, yet existing quantitative research largely measures pedestrian volume rather than the quality of social interactions. We hypothesize that street view imagery -- an inexpensive data source with global coverage -- contains latent social information that can be extracted and interpreted through established social science theory. As a proof of concept, we analyzed 2,998 street view images from 15 cities using a multimodal large language model guided by Mehta's taxonomy of passive, fleeting, and enduring sociability -- one illustrative example of a theory grounded in urban design that could be substituted or complemented by other sociological frameworks. We then used linear regression models, controlling for factors like weather, time of day, and pedestrian counts, to test whether the inferred sociability measures…
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Taxonomy
TopicsUrban, Neighborhood, and Segregation Studies · Crime Patterns and Interventions · Urban Design and Spatial Analysis
