Uncover the nature of overlapping community in cities

TL;DR

This study uses a graph-based deep learning approach on mobile phone data to uncover and quantify the overlapping structures of urban communities, revealing significant correlations with socioeconomic factors.

Contribution

It introduces the first framework to analyze the overlapping nature of urban communities using geospatial and deep learning methods.

Findings

95.7% of urban functional complexity is due to community overlaps during weekdays

Revealed correlations between community overlaps and income levels

Uncovered links between community overlaps and racial segregation patterns

Abstract

Urban spaces, though often perceived as discrete communities, are shared by various functional and social groups. Our study introduces a graph-based physics-aware deep learning framework, illuminating the intricate overlapping nature inherent in urban communities. Through analysis of individual mobile phone positioning data at Twin Cities metro area (TCMA) in Minnesota, USA, our findings reveal that 95.7 % of urban functional complexity stems from the overlapping structure of communities during weekdays. Significantly, our research not only quantifies these overlaps but also reveals their compelling correlations with income and racial indicators, unraveling the complex segregation patterns in U.S. cities. As the first to elucidate the overlapping nature of urban communities, this work offers a unique geospatial perspective on looking at urban structures, highlighting the nuanced interplay of socioeconomic dynamics within cities.