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Description
A two-way relationship exists between human mobility and COVID-19 cases. While existing studies analyzed this relationship often at country and county levels using the available human mobility and COVID-19 data, fewer studies have focused on the connection at the neighborhood level, partially due to the limited data availability and data integration challenges at the local scale. Nevertheless, to better support decisions, it is essential to understand the connection at a finer geographic and temporal scale, which can provide insights into how human mobility and their behavior and interaction affect disease transmission from the bottom up. This research aims to examine the spatial and temporal dimensions of human mobility and how human movement influences the diffusion of COVID-19 in San Diego, CA. The human mobility data used in this research is the daily number of mobile devices aggregated from the census block group level to the San Diego Subregional Areas (SRA) level, using the Social Distancing Metrics of the SafeGraph data. The COVID-19 confirmed case data is the number of COVID-19 cases provided by the San Diego Health and Human Services Agency, aggregated to SRAs from the zip code level using the Dasymetric Mapping by HDMA. Dynamic Time Warping (DTW) measures the similarity between human mobility and the COVID-19 time series at the SRA level. The slopes of the time series are then calculated and compared with the DTW values to validate the increase-increase relationship between human mobility and COVID-19 cases. This study found that the relationship between human mobility and COVID-19 in San Diego depends on time and place. The positive correlation between human mobility and COVID-19 cases happened the most during the winter and holiday season of 2020. The flows of people coming to San Diego County (inflow and netflow) impact the Covid-19 case increase more than those going out of the County (outflow). The human mobility inflow affects the South region more, while the North Central is affected the most by the mobility netflow. Spatial association among SRAs does exist. The revealed patterns are meaningful to local-scale policymaking.
