If you use this dataset and code in your research or applications, please refer to this source:
Gao, S., Rao, J., Kang, Y., Liang, Y., & Kruse, J. (2020). Mapping county-level mobility pattern changes in the United States in response to COVID-19. SIGSPATIAL Special. 12(1), 16-26. DOI: 10.1145/3404820.3404824
@article{gao2020mapping,
title={Mapping county-level mobility pattern changes in the United States in response to COVID-19},
author={Gao, Song and Rao, Jinmeng and Kang, Yuhao and Liang, Yunlei and Kruse, Jake},
journal={SIGSpatial Special},
volume={12},
number={1},
pages={16--26},
year={2020},
publisher={ACM New York, NY, USA}
}
Tracking Mobility Changes Interactive Web Portal: https://geods.geography.wisc.edu/covid19/physical-distancing
Median Travel Distance Index: https://github.com/descarteslabs/DL-COVID-19
Median Stay-At-Home (Dwelling) Time: Safegraph Social Distancing Metrics
To contain the COVID-19 epidemic, one of the non-pharmacological epidemic control measures is reducing the transmission rate of SARS-COV-2 in the population through social distancing. An interactive web-based mapping platform that provides timely quantitative information on how people in different counties and states reacted to the social distancing guidelines was developed by the GeoDS Lab @UW-Madison with the support of the National Science Foundation RAPID program. The web dashboard portal integrates geographic information systems (GIS) and daily updated human mobility statistical patterns (median travel distance and stay-at-home dwell time) derived from large-scale anonymized and aggregated smartphone location big data at the county-level in the United States, and aims to increase risk awareness of the public, support data-driven public health and governmental decision-making, and help enhance community responses to the COVID-19 pandemic.