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. 2023 Apr;2677(4):168-180.
doi: 10.1177/03611981211043813. Epub 2021 Sep 18.

Interactive COVID-19 Mobility Impact and Social Distancing Analysis Platform

Lei Zhang  1 , Aref Darzi  1 , Sepehr Ghader  1 , Michael L Pack  2 , Chenfeng Xiong  1 , Mofeng Yang  1 , Qianqian Sun  1 , Aliakbar Kabiri  1 , Songhua Hu  1
Affiliations

Interactive COVID-19 Mobility Impact and Social Distancing Analysis Platform

Lei Zhang et al. Transp Res Rec. 2023 Apr.

Abstract

The research team has utilized privacy-protected mobile device location data, integrated with COVID-19 case data and census population data, to produce a COVID-19 impact analysis platform that can inform users about the effects of COVID-19 spread and government orders on mobility and social distancing. The platform is being updated daily, to continuously inform decision-makers about the impacts of COVID-19 on their communities, using an interactive analytical tool. The research team has processed anonymized mobile device location data to identify trips and produced a set of variables, including social distancing index, percentage of people staying at home, visits to work and non-work locations, out-of-town trips, and trip distance. The results are aggregated to county and state levels to protect privacy, and scaled to the entire population of each county and state. The research team is making their data and findings, which are updated daily and go back to January 1, 2020, for benchmarking, available to the public to help public officials make informed decisions. This paper presents a summary of the platform and describes the methodology used to process data and produce the platform metrics.

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Conflict of interest statement

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
The density map of anonymized location data across the nation. Note: brighter shades = higher density of sightings within a day across the nation.
Figure 2.
Figure 2.
Methodology.
Figure 3.
Figure 3.
Recursive algorithm for trip identification methodology.
Figure 4.
Figure 4.
Distance validation of the trip identification algorithm against national household travel survey (NHTS) 2017.
Figure 5.
Figure 5.
Travel time validation of the trip identification algorithm against national household travel survey (NHTS) 2017.
Figure 6.
Figure 6.
Activity clustering methodology.
Figure 7.
Figure 7.
Home/work Census block group (CBG) imputation methodology.
Figure 8.
Figure 8.
Validation of home and work imputation against longitudinal employer-household dynamics (LEHD).
Figure 9.
Figure 9.
The framework of the mode detection model based on wide and deep learning.
Figure 10.
Figure 10.
Demonstration of the multi-modal travel patterns.
Figure 11.
Figure 11.
Platform illustration.
See this image and copyright information in PMC

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