Detecting Neighborhood Gentrification at Scale via Street-level Visual Data
This provides a scalable tool for urban researchers and policymakers to supplement existing gentrification indicators, though it is incremental as it builds on prior detection methods.
The paper tackles the problem of detecting neighborhood gentrification by proposing a novel approach that uses historical street-level visual data to analyze physical appearance at scale, showing effectiveness through comparisons with existing measures and case studies.
Neighborhood gentrification plays a significant role in shaping the social and economic well-being of both individuals and communities at large. While some efforts have been made to detect gentrification in cities, existing approaches rely mainly on estimated measures from survey data, require substantial work of human labeling, and are limited in characterizing the neighborhood as a whole. We propose a novel approach to detecting neighborhood gentrification at a large-scale based on the physical appearance of neighborhoods by incorporating historical street-level visual data. We show the effectiveness of the proposed method by comparing results from our approach with gentrification measures from previous literature and case studies. Our approach has the potential to supplement existing indicators of gentrification and become a valid resource for urban researchers and policy makers.