Mobile device location data reveal human mobility response to state-level stay-at-home orders during the COVID-19 pandemic in the USA

Chenfeng Xiong, Songhua Hu, Mofeng Yang, Hannah Younes, Weiyu Luo, Sepehr Ghader and Lei Zhang

This study uses an integrated dataset, consisting of anonymized and privacy-protected location data from over 150 million monthly active samples in the USA, COVID-19 case data and census population information, to uncover mobility changes during COVID-19 and under the stay-at-home state orders in the USA. The study successfully quantifies human mobility responses with three important metrics: daily average number of trips per person; daily average personmiles travelled; and daily percentage of residents staying at home. The data analytics reveal a spontaneous mobility reduction that occurred regardless of government actions and a ‘floor’ phenomenon, where human mobility reached a lower bound and stopped decreasing soon after each state announced the stay-at-home order. A set of longitudinal models is then developed and confirms that the states’ stay-at-home policies have only led to about a 5% reduction in average daily human mobility.

Code structure

• Data used for model building is located at the folder data, which is computed via State_Features.py.
• Three R scripts are used to fit the GAM models. Our model predicts the daily average number of trips and daily average PMT across all states.
• Plot_Fig56.py is used to model results plot.

Results

Estimated daily person-miles travelled increase/reduction at the national level and for each state