Big data insight on global mobility during the Covid-19 pandemic lockdown

Sadowski, Adam; Galar, Zbigniew; Walasek, Robert; Zimon, Grzegorz; Engelseth, Per

doi:10.1186/s40537-021-00474-2

Cited by 26 publications

(21 citation statements)

References 54 publications

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“…Without human movement, infectious particles would be less likely to be transferred from one person/location to another. Our ndings are consistent with those of previous studies that found an increase in residential mobility and a decline in mobility in non-residential places during COVID-19 lockdown periods (31)(32)(33)(34). There was a decrease in residential mobility following the lifting of restrictions.…”

Section: Discussionsupporting

confidence: 92%

Validity of Air Quality as a Measure of Human Mobility in Uganda. The COVID-19 Context.

Galiwango

Bainomugisha

Kivunike

et al. 2022

Preprint

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Background Mobility patterns are valuable in identifying transmission patterns for infectious diseases and in deriving contact matrices that are used to parametrize mathematical models. Aggregated location data from mobile phones have been the main means of measuring human mobility on a population level. However, these data come with several limitations related to individual privacy, access and restriction of the GPS location by the user that limit their use. Methods We explored the viability of using ground monitored air quality data as an alternative to aggregated location data from mobile phones, as a measure of human mobility in two cities in Uganda. We determined associations between air quality and human mobility; and the effect of mobility restrictions on mobility and air quality using Pearson correlation (R), multivariate regression and visualized these relationships using scatter plots. Results Daily mean levels for PM2.5 in both cities were consistently higher than the WHO guideline limit, with a mean of 77.0µg/m3 (Range = 22.0–309) for Kampala and 60.0µg/m3 (Range = 18.2–331) for Wakiso. PM10 levels had a mean of 84.6µg/m3 (Range = 25.0–318) in Kampala and 67.9µg/m3 (Range = 21.0–340) in Wakiso. PM2.5 was negatively correlated with the government response stringency index for Kampala (R = -0.31, p < 0.001) and Wakiso (R = -0.21, p < 0.001). In Kampala, PM2.5 was positively associated with movement in grocery and pharmacy (R = 0.24, p < 0.001), parks (R = 0.25, p < 0.001), retail and recreation (R = 0.24, p < 0.001), transit stations (R = 0.3, p < 0.001) and work places (R = 0.2, p < 0.001); and negatively correlated with movement in residential places (R = -0.3, p < 0.001). Only associations between PM2.5 and movement in workplaces and residential places were statistically significant in Wakiso (R = 0.14, p < 0.001 and R = -0.19, p = 0.003 respectively). Conclusions These findings suggest that air quality data are linked to human mobility data and could thus be used to monitor human movement patterns. This work represents a pioneer study to empirically and quantitatively assess the value of air quality data as a surrogate for human mobility in Uganda.

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Section: Discussionsupporting

confidence: 92%

Validity of Air Quality as a Measure of Human Mobility in Uganda. The COVID-19 Context.

Galiwango

Bainomugisha

Kivunike

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…The application stores the human mobility data of 1 million users and disaster warning information corresponding to earthquakes and typhoons since 2014. Other examples include Google Location History data to examine rare, long-distance, and international trips (Ruktanonchai et al, 2018); Google Mobility data for analysis during the COVID-19 pandemic (Sadowski et al, 2021); and Baidu Maps to analyze the changes in congestion patterns in Shanghai (Jiang et al, 2021).…”

Section: Human Mobility Data and Analytical Approachesmentioning

confidence: 99%

“…Various researchers have quantified the effects of lockdown on human mobility in Wuhan (Kraemer et al, 2020), Tokyo (Yabe et al, 2020b), Italy (Bonaccorsi et al, 2020), and other countries (Kishore et al, 2021a; Sadowski et al, 2021). Kishore et al (2021a) used Facebook data to analyze how human mobility changes affect the spread of COVID-19 in several countries, including India, France, Spain, Bangladesh, and the United States.…”

Section: Human Mobility Data and Analysis Applications In Disaster Ri...mentioning

confidence: 99%

“…Their study showed that rapid implementation and public messaging are necessary to reduce social disorder and export cases from an infection epicenter after lockdown announcements. In addition, to identify the most effective activities or locations for human mobility restrictions during COVID-19, Sadowski et al (2021) analyzed mobility in 137 countries and 50 U.S. states and found that retail and recreation areas, transit stations, and workplaces are the areas where lockdowns can most significantly reduce the number of cases. This finding shows that governments can utilize HMDA to identify activities to prioritize.…”

Section: Combatting Infectious Diseases and The Covid-19 Pandemicmentioning

confidence: 99%

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Human mobility data and analysis for urban resilience: A systematic review

Haraguchi

Nishino

Kodaka

et al. 2022

Environment and Planning B: Urban Analytics and City Science

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The impacts of disasters are increasing due to climate change and unplanned urbanization. Big and open data offer considerable potential for analyzing and predicting human mobility during disaster events, including the COVID-19 pandemic, leading to better disaster risk reduction (DRR) planning. However, the value of human mobility data and analysis (HMDA) in urban resilience research is poorly understood. This review highlights key opportunities for and challenges hindering the use of HMDA in DRR in urban planning and risk science, as well as insights from practitioners. A gap in research on HMDA for data-driven DRR planning was identified. By examining human mobility studies and their respective analytical and planning tools, this paper offers deeper insights into the challenges that must be addressed to improve the development of effective data-driven DRR planning, from data collection to implementation. In future work on HMDA, (i) the human mobility of vulnerable populations should be targeted, (ii) research should focus on disaster mitigation and prevention, (iii) analytical methods for evidence-based disaster planning should be developed, (iv) different types of data should be integrated into analyses to overcome methodological challenges, and (v) a decision-making framework should be developed for evidence-based urban planning through transdisciplinary knowledge co-production.

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“…Third, our survey data hold some salient features that are absent from typical datasets used in the transportation literature. Our dataset is advantageous over large-scale datasets generated by machines, such as mobile phones, which have been used in studies such as Lee et al, 2020 , Sadowski et al, 2021 , since our data can capture important attributes of households and individuals. Moreover, our dataset is comprised of high-frequency panel data able to collect both location information and household/individual attributes at multiple times.…”

Section: Introductionmentioning

confidence: 99%