University of California, Berkeley, USA.
University of West Florida, Pensacola, USA.
Sci Data. 2023 May 10;10(1):267. doi: 10.1038/s41597-023-02176-1.
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.
我们提供了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。这些数据是通过聚合人群在 10 米空间分辨率的交互的细粒度细节获得的，然后将其归一化为社交接触指数。我们还提供了按 POI 类型划分的簇大小分布和平均在簇中花费的时间。这些数据将帮助研究人员对人类互动模式进行细粒度的、保护隐私的分析，以了解 COVID-19 疫情传播和缓解的驱动因素。当前的移动性数据集要么提供社交距离的粗粒度指标，例如县或省一级的旋转半径，要么提供更细粒度的交通流量，这些都不是人与人之间接触的直接衡量标准。我们使用来自已选择加入的手机应用程序的匿名、去识别和增强隐私的基于位置的服务（LBS）数据，并进行适当的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簇，以估计细粒度的接触。
