Spatial association of mobility and COVID-19 infection rate in the USA: A county-level study using mobile phone location data

Tokey, Ahmad Ilderim

doi:10.1016/j.jth.2021.101135

Cited by 49 publications

(36 citation statements)

References 53 publications

(71 reference statements)

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“…Khataee et al compared the effect of the social distancing in several countries, using mobility data from Apple iPhones [13]. Bushman et al [14], Gao et al [15], Hu et al [16] and Tokey [17] also analyzed effects of the stay-at-home distancing on the COVID-19 increase rate in the U.S. Lucchini et al studied the mobility changes during the pandemic in four U.S. states [18].…”

Section: Introductionmentioning

confidence: 99%

Awakening City: Traces of the Circadian Rhythm within the Mobile Phone Network Data

Pintér

Felde

2022

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In this study, call detail records (CDR), covering Budapest, Hungary, are processed to analyze the circadian rhythm of the subscribers. An indicator, called wake-up time, is introduced to describe the behavior of a group of subscribers. It is defined as the time when the mobile phone activity of a group rises in the morning. Its counterpart is the time when the activity falls in the evening. Inhabitant and area-based aggregation are also presented. The former is to consider the people who live in an area, while the latter uses the transit activity in an area to describe the behavior of a part of the city. The opening hours of the malls and the nightlife of the party district are used to demonstrate this application as real-life examples. The proposed approach is also used to estimate the working hours of the workplaces. The findings are in a good agreement with the practice in Hungary, and also support the workplace detection method. A negative correlation is found between the wake-up time and mobility indicators (entropy, radius of gyration): on workdays, people wake up earlier and travel more, while on holidays, it is quite the contrary. The wake-up time is evaluated in different socioeconomic classes, using housing prices and mobile phones prices, as well. It is found that lower socioeconomic groups tend to wake up earlier.

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Section: Introductionmentioning

confidence: 99%

Awakening City: Traces of the Circadian Rhythm within the Mobile Phone Network Data

Pintér

Felde

2022

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“…The summary statistics of our dependent (e-scooter availability) and independent variables are provided in Table 1. Unsurprisingly, like bike ridership (Tokey 2020) and overall mobility (Tokey 2021), e-scooters availability and usage is also substantially lower in 2020 than in 2019 due to COVID-19. Our main independent variables describe the social composition of block groups, collected from the American Community Survey 5-year estimate data.…”

Section: Methodsmentioning

confidence: 99%

E-scooter’s Availability and Social Equity in Minneapolis, MN: A Spatial Modeling Approach

Tokey

2021

Findings

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This article examines the social equity of e-scooter availability across different socially disadvantaged groups in Minneapolis, MN (USA). The city's northwestern part with high poverty rates has a higher e-scooter availability rate than its counterparts. Also, this study did not find any significant inequality involved with race or educational attainment. However, while the city performed well on these, areas with a high percentage of commuters, dependent on transit, bike, and walking, have fewer e-scooters available on their streets. As such an area, the local models of this study underscore the need for more e-scooter deployment in areas close to downtown. QuestionsElectric scooter (or e-scooter) is one of the newest forms of shared mobility and a convenient and environmentally efficient mode of transport. In US cities, concerns have been raised about inequities in the geographical distribution and access to e-scooters (Dill and McNeil 2021;Frias-Martinez et al. 2021). In response, some cities have started to impose regulations on e-scooter operating companies to ensure the deployment of a share of their e-scooters in socially disadvantaged areas. For example, Minneapolis, a mid-sized city in the USA and the most populous city in Minnesota, had started prioritizing safety and equity in e-scooter operation in 2020 for the first time after the inception of the e-scooter-share program in 2018. Specifically, the city set a limit of a maximum of 40% of deployed e-scooters that could be around downtown and required a minimum of 30% of e-scooters in areas with concentrated poverty in the northeast, north, and south Minneapolis (Mahmud 2021; City of Minneapolis 2021). The city's assessment is primarily focused on the poverty rate and did not highlight much on other socio-demographic constructs (e.g., lower educational attainment, dependency on public and shared transportation for commuting, lower usage of credit cards and smartphones) that can potentially lead to inequality in accessing scooters.

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“…Based on GPS-based mobility data from US counties, Tokey employed space-time cube, curvefitting and regression to study the evolving relationship between human mobility and COVID daily infected cases over time with alteration in socioeconomic, demographic and geographic policies [86]. Loo et al utilized mobility data from public places, like bars, shopping centres, karaoke/cinemas, mega shopping malls, public libraries, and sports centres, to learn the connectivity among populous zones and their potential to serve as infection hotspots [87].…”

Section: Contact Tracing and Mobility Schedulingmentioning

confidence: 99%

Towards a Unified Pandemic Management Architecture: Survey, Challenges and Future Directions

Roy¹,

Ghosh²,

Uplavikar³

et al. 2022

Preprint

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The pandemic caused by SARS-CoV-2 has left an unprecedented impact on health, economy and society worldwide. Emerging strains are making pandemic management increasingly challenging. There is an urge to collect epidemiological, clinical, and physiological data to make an informed decision on mitigation measures. Advances in the Internet of Things (IoT) and edge computing provide solutions for pandemic management through data collection and intelligent computation. While existing data-driven architectures attempt to automate decision-making, they do not capture the multifaceted interaction among computational models, communication infrastructure, and the generated data. In this paper, we perform a survey of the existing approaches for pandemic management, including online data repositories and contact-tracing applications. We then envision a unified pandemic management architecture that leverages the IoT and edge computing to automate recommendations on vaccine distribution, dynamic lockdown, mobility scheduling and pandemic prediction. We elucidate the flow of data among the layers of the architecture, namely, cloud, edge and end device layers. Moreover, we address the privacy implications, threats, regulations, and existing solutions that may be adapted to optimize the utility of health data with security guarantees. The paper ends with a lowdown on the limitations of the architecture and research directions to enhance its practicality.

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Spatial association of mobility and COVID-19 infection rate in the USA: A county-level study using mobile phone location data

Cited by 49 publications

References 53 publications

Awakening City: Traces of the Circadian Rhythm within the Mobile Phone Network Data

Awakening City: Traces of the Circadian Rhythm within the Mobile Phone Network Data

E-scooter’s Availability and Social Equity in Minneapolis, MN: A Spatial Modeling Approach

Towards a Unified Pandemic Management Architecture: Survey, Challenges and Future Directions

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