ArXiv Preprint
This paper analyzes the impact of COVID-19 related lockdowns in the Atlanta,
Georgia metropolitan area by examining commuter patterns in three periods:
prior to, during, and after the pandemic lockdown. A cellular phone location
dataset is utilized in a novel pipeline to infer the home and work locations of
thousands of users from the Density-based Spatial Clustering of Applications
with Noise (DBSCAN) algorithm. The coordinates derived from the clustering are
put through a reverse geocoding process from which word embeddings are
extracted in order to categorize the industry of each work place based on the
workplace name and Point of Interest (POI) mapping. Frequencies of commute from
home locations to work locations are analyzed in and across all three time
periods. Public health and economic factors are discussed to explain potential
reasons for the observed changes in commuter patterns.
Tejas Santanam, Anthony Trasatti, Hanyu Zhang, Connor Riley, Pascal Van Hentenryck, Ramayya Krishnan
2023-02-27
