Crowd cluster data in the USA for analysis of human response to COVID-19 events and policies.
Crowd cluster data in the USA for analysis of human response to COVID-19 events and policies.
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DOI：
10.1038/s41597-023-02176-1
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发表时间：
2023-05-10
期刊：
影响因子：
9.8
通讯作者：
Barbarash, D.
中科院分区：
文献类型：
--
作者：
Swaminathan, B.;Kang, J.;Vaidya, K.;Srinivasan, A.;Kumar, P.;Byna, S.;Barbarash, D.
我们提供佛罗里达州和加利福尼亚州按邮政编码划分的不同类型兴趣点 (POI) 人群的日常社交接触强度数据。该数据是通过以 10 米的空间分辨率聚合人们互动的精细细节而获得的，然后将其标准化为社会联系指数。我们还按 POI 类型提供集群大小的分布以及在集群中花费的平均时间。这些数据将帮助研究人员对人类互动模式进行精细的、保护隐私的分析，以了解 COVID-19 流行病传播和缓解的驱动因素。当前的流动性数据集要么提供粗略的社会距离指标，例如县或省一级的回转半径，要么提供更精细的交通量，这两者都不是人与人之间接触的直接衡量标准。我们使用来自选择加入的手机应用程序的匿名、去识别化和隐私增强的基于位置的服务 (LBS) 数据，并适当重新加权以纠正地理异质性，并识别非敏感公共区域的人群，以估计精细的接触情况。
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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影响因子：
4.3
作者：
Brouwer AF;Weir MH;Eisenberg MC;Meza R;Eisenberg JNS
通讯作者：
Eisenberg JNS
DOI：
10.15585/mmwr.mm6943e1
发表时间：
2020-10-30
期刊：
MMWR. Morbidity and mortality weekly report
影响因子：
--
作者：
Pringle JC;Leikauskas J;Ransom-Kelley S;Webster B;Santos S;Fox H;Marcoux S;Kelso P;Kwit N
通讯作者：
Kwit N
影响因子：
9.8
作者：
Wilkinson MD;Dumontier M;Aalbersberg IJ;Appleton G;Axton M;Baak A;Blomberg N;Boiten JW;da Silva Santos LB;Bourne PE;Bouwman J;Brookes AJ;Clark T;Crosas M;Dillo I;Dumon O;Edmunds S;Evelo CT;Finkers R;Gonzalez-Beltran A;Gray AJ;Groth P;Goble C;Grethe JS;Heringa J;'t Hoen PA;Hooft R;Kuhn T;Kok R;Kok J;Lusher SJ;Martone ME;Mons A;Packer AL;Persson B;Rocca-Serra P;Roos M;van Schaik R;Sansone SA;Schultes E;Sengstag T;Slater T;Strawn G;Swertz MA;Thompson M;van der Lei J;van Mulligen E;Velterop J;Waagmeester A;Wittenburg P;Wolstencroft K;Zhao J;Mons B
通讯作者：
Mons B
影响因子：
1.8
作者：
Schubert, Erich;Sander, Jorg;Xu, Xiaowei
通讯作者：
Xu, Xiaowei
影响因子：
64.8
作者：
Chang, Serina;Pierson, Emma;Leskovec, Jure
通讯作者：
Leskovec, Jure