The DOI matches its Crossref record.
Mapping county-level mobility pattern changes in the United States in response to COVID-19
Abstract
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 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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Mobility network models of COVID-19 explain inequities and inform reopening
Chang, S., Pierson, E., Koh, P. W., Gerardin, J., Redbird, B., Grusky, D., & Leskovec, J. (2021) · Nature · doi:10.1038/s41586-020-2923-3 · Cited by 1,533
Whaley, C. M., Pera, M. F., Cantor, J., Chang, J., Velasco, J., Hagg, H. K., Sood, N., & Bravata, D. M. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.24984 · Cited by 426
Pullano, G., Valdano, E., Scarpa, N., Rubrichi, S., & Colizza, V. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30243-0 · Cited by 365
Twitter reveals human mobility dynamics during the COVID-19 pandemic
Huang, X., Li, Z., Jiang, Y., Li, X., & Porter, D. (2020) · PLOS ONE · doi:10.1371/journal.pone.0241957 · Cited by 242
Heterogeneous interventions reduce the spread of COVID-19 in simulations on real mobility data
Wang, H., Ghosh, A., Ding, J., Sarkar, R., & Gao, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-87034-z · Cited by 28
Metadata
- Authors
- Gao, S., Rao, J., Kang, Y., Liang, Y., & Kruse, J.
- Venue
- SIGSPATIAL Special
- Published
- 2020-06-05
- Volume
- 12
- Issue
- 1
- Pages
- 16-26
- ISSN
- 1946-7729 (ISSN-L)
- OpenAlex
- W3203434175
- Cited by
- 226
- References
- 0
- FWCI
- 40.25
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Mapping county-level mobility pattern changes in the United States in response to COVID-19. https://app.cochat.ai/papers/10.1145/3404820.3404824
- The paper
- 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. https://doi.org/10.1145/3404820.3404824
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
