Mapping hourly population dynamics using remotely sensed and geospatial data: a case study in Beijing, China. Issue 5 (4th July 2021)

• Record Type: Journal Article
• Authors: Zhao, Xia; Zhou, Yuyu; Chen, Wei; Li, Xi; Li, Xuecao; Li, Deren
• Is Part Of: GIScience & remote sensing. Volume 58:Issue 5(2021)
• Page Start: 717
• Page End: 732
• Publication Date: 2021-07-04
• Subjects: Population -- multi-source -- social media -- functional zones -- activity pattern; Geodesy -- Periodicals; Cartography -- Periodicals; Aerial photogrammetry -- Periodicals; Remote sensing -- Periodicals; 526.05
• DOI: 10.1080/15481603.2021.1935128
• Languages: English
• ISSNs: 1548-1603

Abstract

ABSTRACT: High spatiotemporal population data are critical for a wide range of applications (e.g. urban planning and management, risk assessment, and epidemic control). However, such data are still not widely available due to the limited knowledge of complex human activities. Here we proposed a spatiotemporal downscaling framework for estimating hourly population dynamics in Beijing by integrating remote sensing and social sensing data. First, we generated two baseline maps of population during sleep and work times using a dasymetric method. Second, we generated urban functional zones using a random forest model and derived human activity patterns from social sensing data. Finally, we estimated the hourly population dynamics at a 500-meter resolution using a temporal downscaling method. Results show the significant spatial difference of the population over time, especially between working hours (9:00 − 18:00) and sleeping hours (after 0:00). The spatial pattern of population is more homogenous within the sixth ring area in Beijing during work time compared to sleep time when there are more clusters of high population. The comparison of spatiotemporal patterns with the referenced real-time heat maps from Baidu indicates that our population data are reliable. The framework presented in this paper is transferable in other regions. The resulting dataset of hourly population dynamics is of great help for governments of emergency responses as well as for studies about human risks to environmental issues.