Yu Yue, Wang Yifang, Zhang Yongjun, Qu Huamin, Liu Dongyu
IEEE Trans Vis Comput Graph. 2025 Jun;31(6):3836-3849. doi: 10.1109/TVCG.2025.3567117.
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.
城市隔离指的是人群在物理空间和社会层面的分隔，这常常导致城市内部的不平等现象加剧，同时也激化了社会经济和种族矛盾。虽然大多数研究聚焦于居住空间，但往往忽视了人们工作、社交和休闲的“活动空间”中的隔离情况。人类移动性数据为分析更广泛的隔离模式提供了新机会，涵盖居住空间和活动空间，但在捕捉城市隔离的复杂性和局部细微差别方面对现有方法构成了挑战。这项工作引入了InclusiViz，这是一种用于城市隔离多层次分析的新型可视化分析系统，有助于制定有针对性的、数据驱动的干预措施。具体而言，我们开发了一个深度学习模型，利用环境特征预测不同社会群体的移动模式，并辅以可解释人工智能来揭示这些特征如何影响隔离。该系统集成了创新的可视化功能，允许用户从宏观概览到细粒度细节探索隔离模式，并通过实时反馈评估城市规划干预措施。我们进行了定量评估以验证模型的准确性和效率。两项案例研究以及与社会科学家和城市分析师的专家访谈证明了该系统的有效性，突出了其引导城市规划走向更具包容性城市的潜力。
