地球信息科学学报

地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (2): 147-160.doi: 10.12082/dqxxkx.2020.200045

• 地球信息科学理论与方法 •    下一篇

基于多源时空大数据的区际迁徙人群多层次空间分布估算模型——以COVID-19疫情期间自武汉迁出人群为例

刘张1,2, 千家乐1,2, 杜云艳1,2,*(), 王楠1,2, 易嘉伟1,2, 孙晔然3,4, 马廷1,2, 裴韬1,2, 周成虎1,2   

  1. 1. 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
    2. 中国科学院大学,北京 100049
    3. 中山大学地理科学与规划学院,广州 510275
    4. 斯旺西大学地理系，斯旺西 SA28PP, 英国
  • 收稿日期:2020-01-28 修回日期:2020-02-19 出版日期:2020-02-25 发布日期:2020-04-13
  • 通讯作者: 杜云艳 E-mail:duyy@lreis.ac.cn
  • 作者简介:刘 张（1991— ）,男,湖北荆州人,博士生,主要从事动态人群估算、时空异常模式挖掘以及自然灾害事件人群活动响应等研究。E-mail: liuzhang@lreis.ac.cn
  • 基金资助:
    国家重点研发计划资助项目(2017YFB0503605);国家重点研发计划资助项目(2017YFC1503003)

Multi-level Spatial Distribution Estimation Model of the Inter-regional Migrant Population Using Multi-source Spatio-temporal Big Data: A Case Study of Migrants from Wuhan during the Spread of COVID-19

LIU Zhang1,2, QIAN Jiale1,2, DU Yunyan1,2,*(), WANG Nan1,2, YI Jiawei1,2, SUN Yeran3,4, MA Ting1,2, PEI Tao1,2, ZHOU Chenghu1,2   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
    4. Department of Geography, Swansea University, Swansea SA28PP, United Kingdom
  • Received:2020-01-28 Revised:2020-02-19 Online:2020-02-25 Published:2020-04-13
  • Contact: DU Yunyan E-mail:duyy@lreis.ac.cn
  • Supported by:
    National Key Research and Development Program of China(2017YFB0503605);National Key Research and Development Program of China(2017YFC1503003)

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摘要：

已有研究很少关注区际迁徙人群在不同尺度上空间分布的动态估算问题。COVID-19疫情爆发以来,坚决防止疫情扩散成为社会最紧迫的事情。在2020年1月23日武汉“封城”前夕,已有500多 万人离开了武汉,快速准确地推算这部分人群的去向,可以为防止疫情扩散和制定防疫决策提供科学依据。本文以此为例,基于开源腾讯位置请求大数据、百度迁徙大数据、土地覆盖数据等多源地理时空大数据,提出一种区际迁徙人群多层次空间分布动态估算模型,用于推算2020年除夕 （2020年1月24日）之前从武汉流入湖北省内各地的人群数量及其分布特征。结果显示：① 春节时段湖北省各地级市农村地 区人群增加数量占人群变化总量的比例平均达124.7%,从武汉市迁入各地级市的人群中至少51.3%流入农村地区;② 区县尺 度人群变化总量的空间分布呈现3个圈层结构：第一圈层为疫情核心区,包括武汉及其周边地区,以人群流出为主;第二圈层为 重点关注区,包括黄冈、黄石、仙桃、天门、潜江、随州、襄阳,以及孝感、荆门、荆州和咸宁的部分地区,以人群总量和农村地区人 群数量大幅增加为主;第三圈层为次级关注区,包括湖北西部宜昌、恩施、神农架和荆门部分地区,以人群小幅流入为主。最后,建议湖北省内,尤其是位于第二圈层内的区县,应高度关注农村地区人群的疫情防控。此研究成果在2~3天完成,显示大数据是可以快速地响应重大公共安全事件,为决策的制定提供一定支持的。

关键词: COVID-19, 疫情防控, 动态人群估算, 人群流动, 手机定位大数据, 百度迁徙大数据, 湖北, 武汉, 农村

Abstract:

Previous researches have paid little attention to the multi-level spatial distribution dynamic estimation of the inter-regional migrant population. Preventing the spread of COVID-19 is the most urgent need for society now. Before the closure of Wuhan on Jan 23, 2020, more than 5 million people had left Wuhan to other regions. A better understanding of the destinations of those people will assist in the decision making and prevention of the coronavirus spread. However, few studies have focused on the dynamic estimation of multi-level spatial distribution of inter-regional migrant populations. In this study, by using multi-source spatiotemporal big data, including Tencent location request data, Baidu migration data, and land cover data, we proposed a dynamic estimation model of multi-level spatial distribution of inter-regional migrant population, and further characterized the spatial distribution of the population migrating from Wuhan to other regions of Hubei Province. The results showed that: (1) During the Spring Festival, the average ratio between the number of population increase in the rural areas and the total population change was 124.7% in the prefecture-level cities in Hubei Province. At least 51.3% of the population moving from Wuhan to prefecture-level cities has flowed into rural areas; (2) the spatial distribution of migrants among cities and counties in Hubei Province exhibits a 3-ring structure. The 1st ring is core area of disease, ncludes Wuhan and its surrounding areas, which are mainly characterized by population outflows. The 2nd ring is primary focus area, includes Huanggang, Huangshi, Xiantao, Tianmen, Qianjiang, Suizhou, Xiangyang and parts of Xiaogan, Jingzhou, Jingmen, Xianning, where the total population and the population in rural areas increased significantly during the Spring Festival. The 3rd ring is the secondary focus area, includes Yichang, Enshi, Shennongjia, and parts of Jingzhou and Jingmen, which are located in the western part of Hubei Province and are mainly characterized by a small inflow of population. We suggest higher attention to those rural areas of the counties located in the 2nd ring to better control and prevent the coronavirus spread. The research was completed in 2-3 days, showing that big data can quickly respond to major public safety events and provide support for decision-making.

Key words: COVID-19, epidemic prevention and control, dynamic population estimate, population mobility, mobile phone positioning big data, population migration big data, Hubei, Wuhan, rural areas