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
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
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:
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
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:
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。
刘张, 千家乐, 杜云艳, 王楠, 易嘉伟, 孙晔然, 马廷, 裴韬, 周成虎. 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[J]. 地球信息科学学报, 2020, 22(2): 147-160.DOI:10.12082/dqxxkx.2020.200045
LIU Zhang, QIAN Jiale, DU Yunyan, WANG Nan, YI Jiawei, SUN Yeran, MA Ting, PEI Tao, ZHOU Chenghu. 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[J]. Journal of Geo-information Science, 2020, 22(2): 147-160.DOI:10.12082/dqxxkx.2020.200045
[1] | 周先旺 . 约500多万人离开了武汉[EB/OL]. http://news.china.com.cn/2020-01/26/content_75650784.htm,2020-01-26. |
[ Zhou X W . About 5 million people left Wuhan[EB/OL]. http://news.china.com.cn/2020-01/26/content_75650784.htm,2020-01-26.] | |
[2] | Wang Y X, Dong L, Liu Y , et al. Migration patterns in China extracted from mobile positioning data[J]. Habitat International, 2019,86:71-80. |
[3] | Hu M . Visualizing the largest annual human migration during the Spring Festival travel season in China[J]. Environment and Planning A: Economy and Space, 2019,51(8):1618-1621. |
[4] | Wei Y, Song W, Xiu C L , et al. The rich-club phenomenon of China's population flow network during the country's spring festival[J]. Applied Geography, 2018,96:77-85. |
[5] | Li J W, Ye Q Q, Deng X K , et al. Spatial-temporal analysis on Spring Festival travel rush in China based on multisource big data[J]. Sustainability, 2016,8(11):1184. |
[6] | Zhu D, Huang Z, Shi L , et al. Inferring spatial interaction patterns from sequential snapshots of spatial distributions[J]. International Journal of Geographical Information Science, 2018,32(4):783-805. |
[7] | Wang X W, Liu C, Mao W L , et al. Tracing the largest seasonal migration on earth[J]. arXiv preprint arXiv: 1411.0983, 2014. |
[8] | Hu X Q, Li H, Bao X G . Urban population mobility patterns in Spring Festival Transportation: Insights from Weibo data[C]. 2017 International Conference on Service Systems and Service Management. IEEE, 2017: 1-6. |
[9] | Xu J, Li A Y, Li D , et al. Difference of urban development in China from the perspective of passenger transport around Spring Festival[J]. Applied Geography, 2017,87:85-96. |
[10] | Leyk S, Gaughan A E, Adamo S B , et al. The spatial allocation of population: A review of large-scale gridded population data products and their fitness for use[J]. Earth System Science Data, 2019,11:3. |
[11] | Wardrop N A, Jochem W C, Bird T J , et al. Spatially disaggregated population estimates in the absence of national population and housing census data[J]. Proceedings of the National Academy of Sciences, 2018,115(14):3529-3537. |
[12] | Yao Y, Liu X P, Li X , et al. Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data[J]. International Journal of Geographical Information Science, 2017,31(6):1220-1244. |
[13] | Patel N N, Stevens F R, Huang Z J , et al. Improving large area population mapping using geotweet densities[J]. Transactions in GIS, 2017,21(2):317-331. |
[14] | Kontokosta C E, Johnson N . Urban phenology: Toward a real-time census of the city using Wi-Fi data[J]. Computers, Environment and Urban Systems, 2017,64:144-153. |
[15] | Kubíček P, Konečný M, Stachoň Z , et al. Population distribution modelling at fine spatio-temporal scale based on mobile phone data[J]. International Journal of Digital Earth, 2019,12(11):1319-1340. |
[16] | Ma Y J, Xu W, Zhao X J , et al. Modeling the hourly distribution of population at a high spatiotemporal resolution using subway smart card data: A case study in the central area of Beijing[J]. ISPRS International Journal of Geo-information, 2017,6(5):128. |
[17] | Tsou M H, Zhang H, Nara A , et al. Estimating hourly population distribution change at high spatiotemporal resolution in urban areas using geo-tagged tweets, land use data, dasymetric maps[J]. arXiv preprint arXiv: 1810.06554, 2018. |
[18] | Deville P, Linard C, Martin S , et al. Dynamic population mapping using mobile phone data[J]. Proceedings of the National Academy of Sciences, 2014,111(45):15888-15893. |
[19] | Liu Z, Ma T, Du Y , et al. Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records[J]. Transactions in GIS, 2018,22(2):494-513. |
[20] | Khodabandelou G, Gauthier V, Fiore M , et al. Estimation of static and dynamic urban populations with mobile network metadata[J]. IEEE Transactions on Mobile Computing, 2018,18(9):2034-2047. |
[21] | Feng J, Li Y, Xu F L , et al. A Bimodal Model to Estimate Dynamic Metropolitan Population by Mobile Phone Data[J]. Sensors, 2018,18(10):3431. |
[22] | Khodabandelou G, Gauthier V, El-Yacoubi M , et al. Population estimation from mobile network traffic metadata[C]. 2016 IEEE 17th international symposium on a world of wireless, mobile and multimedia networks (WOWMOM). IEEE, 2016: 1-9. |
[23] | Zong Z F, Feng J, Liu K C , et al. DeepDPM: Dynamic Population Mapping via Deep Neural Network[C]. Proceedings of the AAAI Conference on Artificial Intelligence, 2019,33:1294-1301. |
[24] | Chen J, Pei T, Shaw S L , et al. Fine-grained prediction of urban population using mobile phone location data[J]. International Journal of Geographical Information Science, 2018,32(9):1770-1786. |
[25] | Chen Y H, Zhang R J, Ge Y , et al. Downscaling census data for gridded population mapping with geographically weighted area-to-point regression Kriging[J]. IEEE Access, 2019,7:149132-149141. |
[26] | Song Y Z, Long Y, Wu P , et al. Are all cities with similar urban form or not? Redefining cities with ubiquitous points of interest and evaluating them with indicators at city and block levels in China[J]. International Journal of Geographical Information Science, 2018,32(12):2447-2476. |
[27] | Gong P, Li X C, Zhang W . 40-Year (1978-2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing[J]. Science Bulletin, 2019,64(11):756-763. |
[28] | Ma T, Lu R, Zhao N , et al. An estimate of rural exodus in China using location-aware data[J]. PLoS one, 2018,13(7) e0201458. |
[29] | 宫礼 . 人民网评:疫情防控万万不可忽视农村[EB/OL]. http://opinion.people.com.cn/n1/2020/0124/c1003-31561897.html,2020-01-24. |
[ Gong L . People's Online Review: The epidemic prevention and control must not be ignored in rural areas[EB/OL]. http://opinion.people.com.cn/n1/2020/0124/c1003-31561897.html, 2020-01-24.] |
[1] | 谢聪慧, 吴世新, 张晨, 孙文涛, 何海芳, 裴韬, 罗格平. 基于谱系聚类的全球各国新冠疫情时间序列特征分析[J]. 地球信息科学学报, 2021, 23(2): 236-245. |
[2] | 巫细波, 赖长强, 葛志专. 政府严控期我国地级市COVID-19疫情的时空集聚、演变及自相关效应研究[J]. 地球信息科学学报, 2021, 23(2): 246-258. |
[3] | 毕佳, 王贤敏, 胡跃译, 罗孟涵, 张俊华, 胡凤昌, 丁子洋. 一种基于改进SEIR模型的突发公共卫生事件风险动态评估与预测方法——以欧洲十国COVID-19为例[J]. 地球信息科学学报, 2021, 23(2): 259-273. |
[4] | 韦原原, 江南, 陈云海, 李响, 杨振凯. 顾及时空对象空间相互作用的疫情风险评估建模与应用[J]. 地球信息科学学报, 2021, 23(2): 274-283. |
[5] | 方云皓, 顾康康. 基于多元数据的中国地理空间疫情风险评估探索——以2020年1月1日至4月11日COVID-19疫情数据为例[J]. 地球信息科学学报, 2021, 23(2): 284-296. |
[6] | 曹中浩, 张健钦, 杨木, 贾礼朋, 邓少存. 基于GIS新冠智能体仿真模型及应用——以广州市为例[J]. 地球信息科学学报, 2021, 23(2): 297-306. |
[7] | 杜毅贤, 徐家鹏, 钟琳颖, 侯盈旭, 沈婕. 网络舆情态势及情感多维特征分析与可视化——以COVID-19疫情为例[J]. 地球信息科学学报, 2021, 23(2): 318-330. |
[8] | 刘权毅, 詹庆明, 刘稳, 杨晨. 基于铁路客流的湖北省城市网络关联与空间组织结构特征[J]. 地球信息科学学报, 2020, 22(5): 1008-1022. |
[9] | 贾涛, 杨仕浩, 李欣, 鄢鹏高, 喻雪松, 罗希, 陈凯. 武汉居民建筑物碳排放反演计算和时空分析[J]. 地球信息科学学报, 2020, 22(5): 1063-1072. |
[10] | 柯新利, 肖邦勇, 郑伟伟, 马艳春, 李红艳. 城镇-农业-生态空间划定的多情景模拟[J]. 地球信息科学学报, 2020, 22(3): 580-591. |
[11] | 刘稳, 詹庆明, 刘权毅, 司瑶, 黄启雷, 樊智宇. 地理国情监测成果与规划用地数据的关联转换方法[J]. 地球信息科学学报, 2020, 22(2): 161-174. |
[12] | 赵轩, 彭建东, 樊智宇, 杨晨, 杨红. “双评价”视角下基于FLUS模型的武汉大都市区土地利用模拟和城镇开发边界划定研究[J]. 地球信息科学学报, 2020, 22(11): 2212-2226. |
[13] | 蔡博文,王树根,王磊,邵振峰. 基于深度学习模型的城市高分辨率遥感影像 不透水面提取[J]. 地球信息科学学报, 2019, 21(9): 1420-1429. |
[14] | 姚尧, 任书良, 王君毅, 关庆锋. 卷积神经网络和随机森林的城市房价微观尺度制图方法[J]. 地球信息科学学报, 2019, 21(2): 168-177. |
[15] | 樊智宇, 詹庆明, 刘慧民, 杨晨, 夏宇. 武汉市夏季城市热岛与不透水面增温强度时空分布[J]. 地球信息科学学报, 2019, 21(2): 226-235. |
|