基于现有社会空间理论、数据科学发展与时空行为大数据实践的最新进展，本文从治理目标与功能方案的视角出发，提出时空行为大数据驱动的流动人群精准健康治理分析框架。通过对两个最新实践的比较分析，发现时空行为大数据通过简化数据分析层次和提升数据信息效率来捕捉人群行为中存在的时间累计效应、行为动态效应以及群体互动效应，有助于实现对于人群健康的实时追踪与预测，从而把控健康风险的复杂性、动态性和预测不确定性。同时，时空行为大数据的利用为健康治理与疾病预防控制体系向精细化、动态化转型提供了可能。

Based on the latest progress of existing social space theory, data science development and Time-Space-Behavior big data practice, this article proposed framework for healthcare governance of floating populations from the perspectives of problems and solutions. Through a systematic comparison and analysis of two cases, the study found that Time-Space-Behavior big data captures the cumulative effects of time and behavioral dynamics in crowd behaviors by simplifying the data analysis level and improving the efficiency of data information. Thereby reducing the complexity, dynamics and prediction uncertainty of health risks, and helping to achieve real-time tracking and prediction of population health. At the same time, the use of Time-Space-Behavior big data helps to transform the healthcare governance and disease prevention and control system to be a refined and dynamic one.