Human Mobility and Social Inequality: Limitations of Mobility Data and Future Directions

Human mobility is a fundamental determinant of urban spatial and social organization, profoundly influencing patterns of social interaction, integration, and inequality. However, prevailing research is constrained by mobility datasets that are often non-representative, reliant on static spatial proxies, and incapable of distinguishing physical co-presence from meaningful social interaction. These limitations impede a mechanistic understanding of how mobility drives core urban social phenomena such as segregation, disparity, and inequity. This perspective critically examines these empirical and theoretical blind spots, framing them around the interconnected dynamics of social mixing, segregation, disparity, inequality, and inequity. We then delineate a research agenda to transcend these limitations, focused on (1) leveraging AI and data fusion to overcome representativeness and validation bottlenecks; (2) incorporating longitudinal dynamics through deep learning models; (3) developing contextualized models of social interactions that move beyond simple co-presence; and (4) harnessing generative models to synthesize realistic mobility flows in data-scarce contexts. We argue that advancements in computational social science are essential to forge a more accurate, dynamic, and equitable understanding of human mobility’s role in shaping social inequality.