Redefining Urban Clusters: Combining Subjective Perceptions and Objective Data to Map Inequality

In recent decades, inequalities in economic, health, and education sectors have intensified spatial clustering of populations and resources, further reinforcing disparities within urban environment. Identifying these geographic boundaries is crucial for developing targeted policies to address inequality effectively. While traditional approaches to studying urban segregation rely primarily on socioeconomic indicators, this research introduces a novel methodology that combines subjective perceptions of the urban environment and objective characteristics of urban areas—such as land use and infrastructure—to identify distinct spatial clusters within Glasgow, a city with a varied socioeconomic landscape. Using MIT Place Pulse dataset of crowd-sourced streetscape perceptions, we developed a deep learning model to predict perception scores for new areas. These perception scores, along with image embeddings and land use information, enabled the geographic clustering of areas based on perceived and functional similarities. Our analysis reveals that perception-based boundaries often diverge from traditional census dissemination areas, suggesting that administrative boundaries may not fully capture the lived experiences of urban space. This research advances our understanding of urban inequality by demonstrating how perceived environmental qualities interact with physical infrastructure to shape distinct urban zones, providing policymakers with new tools for targeted intervention strategies.