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Journal article
Crowd cluster data in the USA for analysis of human response to COVID-19 events and policies
Scientific data, Vol.10, p.267
05/10/2023
PMID: 37164983
Web of Science ID: WOS:000985826000002
Abstract
We provide data on daily social contact intensity of clusters of people at different types of Points of Interest (POI) by zip code in Florida and California. This data is obtained by aggregating fine-scaled details of interactions of people at the spatial resolution of 10 m, which is then normalized as a social contact index. We also provide the distribution of cluster sizes and average time spent in a cluster by POI type. This data will help researchers perform fine-scaled, privacy-preserving analysis of human interaction patterns to understand the drivers of the COVID-19 epidemic spread and mitigation. Current mobility datasets either provide coarse-level metrics of social distancing, such as radius of gyration at the county or province level, or traffic at a finer scale, neither of which is a direct measure of contacts between people. We use anonymized, de-identified, and privacy-enhanced location-based services (LBS) data from opted-in cell phone apps, suitably reweighted to correct for geographic heterogeneities, and identify clusters of people at non-sensitive public areas to estimate fine-scaled contacts.
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- Title
- Crowd cluster data in the USA for analysis of human response to COVID-19 events and policies
- Publication Details
- Scientific data, Vol.10, p.267
- Resource Type
- Journal article
- Publisher
- Nature Publishing Group
- Grant note
- R15 LM013382 / NLM NIH HHS
- Copyright
- © the author(s)
- Identifiers
- WOS:000985826000002; 99381591127106600
- Academic Unit
- Computer Science; Hal Marcus College of Science and Engineering
- Language
- English