Detecting Neighborhood Gentrification at Scale via Street-level Visual Data

TL;DR

This paper introduces a scalable method for detecting neighborhood gentrification using street-level visual data, offering an alternative to traditional survey-based approaches and aiding urban analysis.

Contribution

It presents a novel large-scale approach leveraging visual data to identify gentrification, reducing reliance on manual labeling and survey data.

Findings

Effective detection of gentrification through visual data comparison.

Potential to supplement existing gentrification indicators.

Validated against previous measures and case studies.

Abstract

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.