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.

翻译：本文通过考察疫情封锁前、中、后三个时期的通勤模式，分析了佐治亚州亚特兰大都会区因新冠肺炎相关封锁措施产生的影响。研究采用手机定位数据集，通过创新性处理流程，利用基于密度的带噪声空间聚类算法（DBSCAN）推断数千名用户的家庭与工作地点。从聚类获得的坐标经过反向地理编码处理后，提取词嵌入向量，进而根据工作场所名称与兴趣点（POI）映射关系对各类工作场所所属行业进行分类。研究分析了三个时期内及跨时期的家庭至工作地点的通勤频率，并结合公共卫生与经济因素探讨了通勤模式变化的潜在成因。