InclusiViz: Visual Analytics of Human Mobility Data for Understanding and Mitigating Urban Segregation

TL;DR

InclusiViz is a visual analytics system that combines deep learning and explainable AI to analyze urban segregation across residential and activity spaces, aiding targeted interventions.

Contribution

The paper introduces InclusiViz, a novel system integrating deep learning, explainable AI, and advanced visualizations for multi-level analysis of urban segregation using human mobility data.

Findings

Deep learning model accurately predicts mobility patterns across social groups.

Visualizations enable exploration of segregation at multiple scales.

Expert feedback confirms system's usefulness for urban planning.

Abstract

Urban segregation refers to the physical and social division of people, often driving inequalities within cities and exacerbating socioeconomic and racial tensions. While most studies focus on residential spaces, they often neglect segregation across "activity spaces" where people work, socialize, and engage in leisure. Human mobility data offers new opportunities to analyze broader segregation patterns, encompassing both residential and activity spaces, but challenges existing methods in capturing the complexity and local nuances of urban segregation. This work introduces InclusiViz, a novel visual analytics system for multi-level analysis of urban segregation, facilitating the development of targeted, data-driven interventions. Specifically, we developed a deep learning model to predict mobility patterns across social groups using environmental features, augmented with explainable AI to reveal how these features influence segregation. The system integrates innovative visualizations that allow users to explore segregation patterns from broad overviews to fine-grained detail and evaluate urban planning interventions with real-time feedback. We conducted a quantitative evaluation to validate the model's accuracy and efficiency. Two case studies and expert interviews with social scientists and urban analysts demonstrated the system's effectiveness, highlighting its potential to guide urban planning toward more inclusive cities.