| 引用本文：梁海伦, 陶磊, 王虎峰.时空行为大数据何以驱动流动人群的健康治理提升——基于两个实践案例的比较[J].中国卫生政策研究,2022,15(5):15-23 |
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| 时空行为大数据何以驱动流动人群的健康治理提升——基于两个实践案例的比较 |
| 投稿时间：2022-02-21 修订日期：2022-05-22 PDF全文浏览 HTML全文浏览 |
| 梁海伦1, 陶磊2, 王虎峰1 |
1. 中国人民大学公共管理学院医改研究中心 北京 100872;
2. 香港城市大学公共政策学系 香港 999077 |
| 摘要: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。 |
| 关键词:健康治理 流动人群 时空行为 大数据 |
| 基金项目:国家自然科学基金(71804183)；北京市科学技术协会青年人才托举工程(BYESS2022043);中国人民大学公共健康与疾病预防控制交叉学科重大创新平台建设成果(2021PDPC) |
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| How does the Time-Space-Behavior big data drive the improvement of the health governance of the floating population: A case study based on two practices |
| LIANG Hai-lun1, TAO Lei2, WANG Hu-feng1 |
1. Health Reform and Development Center, School of Public Administration and Policy, Renmin University of China, Beijing 100872, China;
2. Department of Public Policy, City University of Hong Kong, Hong Kong 999077, China |
| Abstract: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. |
| Key words:Health governance Floating population Time-space-behavior Big data |
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