Understanding COVID-19 Effects on Mobility: A Community-Engaged Approach

Abstract

Abstract. Given aggregated mobile device data, the goal is to understand the impact of COVID-19 policy interventions on mobility. This problem is vital due to important societal use cases, such as safely reopening the economy. Challenges include understanding and interpreting questions of interest to policymakers, cross-jurisdictional variability in choice and time of interventions, the large data volume, and unknown sampling bias. The related work has explored the COVID-19 impact on travel distance, time spent at home, and the number of visitors at different points of interest. However, many policymakers are interested in long-duration visits to high-risk business categories and understanding the spatial selection bias to interpret summary reports. We provide an Entity Relationship diagram, system architecture, and implementation to support queries on long-duration visits in addition to fine resolution device count maps to understand spatial bias. We closely collaborated with policymakers to derive the system requirements and evaluate the system components, the summary reports, and visualizations.

Understanding the bias of mobile location data across spatial scales and over time: A comprehensive analysis of SafeGraph data in the United States

“…Sampling bias refers to the discrepancy between a sample and the population from which it was collected. It is a systematic error that cannot be alleviated by simply increasing the size of the sample [25]. In the context of SafeGraph data, 'sample' refers to the panel of devices compiled within the dataset, while the 'population' denotes the entirety of the U.S. population, and the bias can arise from multiple dimensions.…”

Use of SafeGraph visitation patterns for the identification of essential services during COVID-19

“…For example, it revealed short-term perturbations in the number of visits to universities, gas stations, and grocery stores around Hurricane Irma (September 2017) in Florida (Juhász and Hochmair, 2020). SafeGraph data has also been used to determine anomalies in POI visitation counts on selected POI categories (e.g., elementary and secondary schools, restaurants, malls) during the pandemic in Minnesota, which revealed different recovery speeds in reaching pre-pandemic levels for different POI categories (Sharma et al, 2022). Another study used SafeGraph and subway turnstile data to analyze the effect of lockdowns and other restrictions on the spread of COVID-19 across five U.S. cities, finding that a ten percentage point decrease in mobility leads to a fall between 19% and 34% in COVID-19 cases per capita (Glaeser et al, 2022).…”

“…To the best of our knowledge, this is the first study to systematically analyze all POI categories from SafeGraph regarding COVID-19 lockdown impact on mobility. Previous work assessed the effect of COVID-19 on visitor flows from aggregated SafeGraph data at three geographic scales: census tract, county, and state (Kang et al, 2020) or explored the COVID-19 effect on travel distance and number of visitors only on selected POI categories (Sharma et al, 2022).…”