突发公共卫生事件下的人口流动模式变化识别  

Identifying Human Mobility Patterns Changes During Public Health Emergencies

作　　者：钟雷洋 周颖 高松 夏吉喆 李珍 李晓明 乐阳 李清泉 ZHONG Leiyang;ZHOU Ying;GAO Song;XIA Jizhe;LI Zhen;LI Xiaoming;YUE Yang;LI Qingquan(Key Laboratory for Geo-Environmental Monitoring of Great Bay Area,MNR,Shenzhen 518060,China;Shenzhen Key Laboratory of Spatial Smart Sensing and Services,Shenzhen 518060,China;Guangdong Key Laboratory for Urban Informatics,Shenzhen 518060,China;Research Institute for Smart Cites,Shenzhen University,Shenzhen 518060,China;College of Public Health,Shenzhen University,Shenzhen 518060,China;Geospatial Data Science Lab,Department of Geography,University of Wisconsin-Madison,Madison WI,53706)

机构地区：[1]自然资源部大湾区地理环境监测重点实验室,广东深圳518060 [2]深圳市空间信息智能感知与服务深圳市重点实验室,广东深圳518060 [3]广东省城市空间信息工程重点实验室,广东深圳518060 [4]深圳大学智慧城市研究院,广东深圳518060 [5]深圳大学公共卫生学院,广东深圳518060 [6]美国威斯康星大学麦迪逊分校地理系地理空间数据科学实验室,威斯康星州麦迪逊53706

出　　处：《武汉大学学报（信息科学版）》2024年第7期1237-1249,共13页Geomatics and Information Science of Wuhan University

基　　金：粤自然资合[2023]25号;国家重点研发计划(2018YFB2100704);国家自然科学基金(42171400,7181101150,41971341);广东省自然科学基金(2021A1515011324,2019A1515010748)。

摘　　要：根据中国与美国两大湾区的高时空分辨率人口移动大数据,首先构建移动变化指数、距离变化指数和波动变化指数3个评价指标,剖析突发公共卫生事件下中国粤港澳大湾区与美国旧金山湾区的人口移动差异特征。然后,利用奇异值分解算法识别出两大湾区的人口流动内在结构和流动模式。分析发现:(1)在人口流动管控维度上,粤港澳大湾区整体表现优于旧金山湾区,大流行期间人口移动量下降幅度更大,每日移动量波动更加平稳,平均出行距离更短。(2)粤港澳大湾区人口流动模式主要受春节假期与公共卫生政策双重影响,从中识别出日常出行模式、返乡出行模式和返程复工模式。旧金山湾区人口流动模式呈现强规律性(包括日常出行模式、工作日出行模式与周末出行模式),公共卫生政策对其影响并不深刻。量化突发公共卫生事件管控措施下中美两大湾区人口流动指标与模式的定量改变,对评估防疫措施有效性和确定有针对性的防疫干预措施至关重要,还可为未来各类突发性公共卫生事件的防控措施制定提供重要指标与经验参考。Objectives:This study aims to evaluate the changes in human mobility during a public health emergency.Methods:Employing detailed spatiotemporal big data on human mobility from China and the USA,we constructed three mobility indices:Mobility change index,distance change index,and volatility change index.We investigated the mobility characteristics within China's Guangdong-Hong Kong-Macao Greater Bay Area(GBA)and the United States'San Francisco Bay Area(SBA)during the epidemic.The singular value decomposition(SVD)algorithm was applied to identified underlying structures and patterns of mobility in these regions.Results:The results show that:(1)The GBA outperformed the SBA in terms of human mobility control with a greater decline in movement volumes,smoother volatility in daily movement and shorter average travel distances during the pandemic.(2)Human mobility patterns in the GBA were influenced by both the Chinese Spring Festival holiday and public health policies,from which the daily travel patterns,returning home travel characteristics and returning-to-work characteristics were identified.Human mobility patterns in the SBA show strong regularity(including daily travel characteristics,weekday travel characteristics and weekend travel characteristics),and public health policies do not have profound impacts.Conclusions:We quantify the changes in human mobility patterns under different epidemic control measures in two Bay Areas of China and the United States,which is essential to assess and identify intervention effectiveness.It also provides important evidences and references for various infectious disease control in the future.

关 键 词：公共卫生事件 人口流动 时空特征 模式识别 粤港澳大湾区 旧金山湾区 

分 类 号：P208[天文地球—地图制图学与地理信息工程]