2021
DOI: 10.1038/s41467-021-23937-9
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Effect of COVID-19 response policies on walking behavior in US cities

Abstract: The COVID-19 pandemic is causing mass disruption to our daily lives. We integrate mobility data from mobile devices and area-level data to study the walking patterns of 1.62 million anonymous users in 10 metropolitan areas in the United States. The data covers the period from mid-February 2020 (pre-lockdown) to late June 2020 (easing of lockdown restrictions). We detect when users were walking, distance walked and time of the walk, and classify each walk as recreational or utilitarian. Our results reveal drama… Show more

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Cited by 121 publications
(102 citation statements)
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“…Interestingly, after the con nement period, the frequency of community ambulation increased but only by a small amount. In agreement with a recent study that compiled mobile device data from 1.62 million anonymous users in 10 metropolitan areas in the United States 27 , results from our questionnaire suggest that while community ambulation increased with the easing of lockdown restrictions, people still show signs of reluctance to leave their homes.…”
Section: Discussionsupporting
confidence: 92%
“…Interestingly, after the con nement period, the frequency of community ambulation increased but only by a small amount. In agreement with a recent study that compiled mobile device data from 1.62 million anonymous users in 10 metropolitan areas in the United States 27 , results from our questionnaire suggest that while community ambulation increased with the easing of lockdown restrictions, people still show signs of reluctance to leave their homes.…”
Section: Discussionsupporting
confidence: 92%
“… 4 Deterioration of other health behaviors has also been observed during the pandemic. For example, using cell phone data from over 1.5 million people from ten large US cities, Hunter at al 26 identified a large reduction in both walking frequency and walking distance during the first three months of the pandemic. Such changes are impactful because they compound the negative impact of the pandemic on overall health and may persist beyond the pandemic.…”
Section: Discussionmentioning
confidence: 99%
“…Despite the limited data, there are some examples that assist in the acquisition of big data, such as the data collected by Apple [ 28 ], although its level of data disaggregation does not allow for the analysis of changes at a detailed level (neighbourhood, census district, or street). Using mobile phones as a source, Hunter et al [ 29 ] analysed the change in the behaviour of the population of the United States with respect to walking, assessing the variations in time and distance before and after the pandemic. However, the use of this source of data has its limitations.…”
Section: Literature Reviewmentioning
confidence: 99%
“…First, the source used provided a great volume of data, which have to be filtered to obtain data that correspond to pedestrian mobility according to the criteria. Second, the data from mobile phones obviously do not include information on people who walk without their mobile phone [ 29 ] or who carry it without being connected. One method of overcoming these problems is to use data generated by fixed sensors, such as pedestrian counters.…”
Section: Literature Reviewmentioning
confidence: 99%