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Title: Spatiotemporal Patterns of Human Mobility and Its Association with Land Use Types during COVID-19 in New York City
The novel coronavirus disease (COVID-19) pandemic has impacted every facet of society. One of the non-pharmacological measures to contain the COVID-19 infection is social distancing. Federal, state, and local governments have placed multiple executive orders for human mobility reduction to slow down the spread of COVID-19. This paper uses geotagged tweets data to reveal the spatiotemporal human mobility patterns during this COVID-19 pandemic in New York City. With New York City open data, human mobility pattern changes were detected by different categories of land use, including residential, parks, transportation facilities, and workplaces. This study further compares human mobility patterns by land use types based on an open social media platform (Twitter) and the human mobility patterns revealed by Google Community Mobility Report cell phone location, indicating that in some applications, open-access social media data can generate similar results to private data. The results of this study can be further used for human mobility analysis and the battle against COVID-19.  more » « less
Award ID(s):
2028791
NSF-PAR ID:
10345839
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
ISPRS International Journal of Geo-Information
Volume:
10
Issue:
5
ISSN:
2220-9964
Page Range / eLocation ID:
344
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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