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With the onset of COVID-19 and the resulting shelter in place guidelines combined with remote working practices, human mobility in 2020 has been dramatically impacted. Existing studies typically examine whether mobility in specific localities increases or decreases at specific points in time and relate these changes to certain pandemic and policy events. However, a more comprehensive analysis of mobility change over time is needed. In this paper, we study mobility change in the US through a five-step process using mobility footprint data. (Step 1) Propose the Delta Time Spent in Public Places (ΔTSPP) as a measure to quantify daily changes in mobility for each US county from 2019-2020. (Step 2) Conduct Principal Component Analysis (PCA) to reduce the ΔTSPP time series of each county to lower-dimensional latent components of change in mobility. (Step 3) Conduct clustering analysis to find counties that exhibit similar latent components. (Step 4) Investigate local and global spatial autocorrelation for each component. (Step 5) Conduct correlation analysis to investigate how various population characteristics and behavior correlate with mobility patterns. Results show that by describing each county as a linear combination of the three latent components, we can explain 59% of the variation in mobility trends across all US counties. Specifically, change in mobility in 2020 for US counties can be explained as a combination of three latent components: 1) long-term reduction in mobility, 2) no change in mobility, and 3) short-term reduction in mobility. Furthermore, we find that US counties that are geographically close are more likely to exhibit a similar change in mobility. Finally, we observe significant correlations between the three latent components of mobility change and various population characteristics, including political leaning, population, COVID-19 cases and deaths, and unemployment. We find that our analysis provides a comprehensive understanding of mobility change in response to the COVID-19 pandemic. 

Foot traffic is a business term to describe the number of customers that enter a point of interest (POI). This work aims to predict future foot traffic: the number of people from each census block group (CBG) that will visit each POI of a study region with potential applications in marketing and advertising. Existing techniques for spatiotemporal prediction of foot traffic use location-based social network data that suffer from sparsity, capturing only a handful of visits per day. This study utilizes highly granular foot traffic data from SafeGraph, a data company that collects mobility data regarding hundreds of millions of visits per day in the United States alone. Using this data, we explore solutions to predict weekly foot traffic data at the POI level. We propose a collaborative filtering approach using tensor factorization on the (POIs x CBGs x Weeks) data tensor. This approach provides us with a de-noised estimation of visits in previous weeks for all POI-CBG pairs. Using this tensor, we explore various time series prediction models: weekly rolling average, weighted weekly rolling average, univariate linear regression, polynomial regression, and long short-term memory (LSTM) recurrent neural networks. Our results show that of all the prediction models, the collaborative filtering step consistently improves prediction results. We also found that a simple weighted average consistently performed better than the more sophisticated approaches. Given this abundance of foot traffic data, this result shows that we can improve the spatiotemporal prediction of foot traffic data by harnessing collaborative filtering.