Mobile Device Location Data Research Articles

Quantifying human mobility behaviour changes during the COVID-19 outbreak in the United States

Since the first case of the novel coronavirus disease (COVID-19) was confirmed in Wuhan, China, social distancing has been promoted worldwide, including in the United States, as a major community mitigation strategy. However, our understanding remains limited in how people would react to such control measures, as well as how people would resume their normal behaviours when those orders were relaxed. We utilize an integrated dataset of real-time mobile device location data involving 100 million devices in the contiguous United States (plus Alaska and Hawaii) from February 2, 2020 to May 30, 2020. Built upon the common human mobility metrics, we construct a Social Distancing Index (SDI) to evaluate people’s mobility pattern changes along with the spread of COVID-19 at different geographic levels. We find that both government orders and local outbreak severity significantly contribute to the strength of social distancing. As people tend to practice less social distancing immediately after they observe a sign of local mitigation, we identify several states and counties with higher risks of continuous community transmission and a second outbreak. Our proposed index could help policymakers and researchers monitor people’s real-time mobility behaviours, understand the influence of government orders, and evaluate the risk of local outbreaks.

Human mobility trends during the early stage of the COVID-19 pandemic in the United States.

In March of this year, COVID-19 was declared a pandemic, and it continues to threaten public health. This global health crisis imposes limitations on daily movements, which have deteriorated every sector in our society. Understanding public reactions to the virus and the non-pharmaceutical interventions should be of great help to fight COVID-19 in a strategic way. We aim to provide tangible evidence of the human mobility trends by comparing the day-by-day variations across the U.S. from January 2020 to early April 2020. Large-scale public mobility at an aggregated level is observed by leveraging mobile device location data and the measures related to social distancing. Our study captures spatial and temporal heterogeneity as well as the sociodemographic variations and teleworking trends regarding the pandemic propagation and the non-pharmaceutical mobility interventions. All metrics adapted capture decreased public movements after the national emergency declaration. The population staying home has increased in all states before the stay-at-home mandates implemented and becomes more stable after the order with a smaller range of fluctuation. The public had been taking active responses, voluntarily staying home more, to the in-state confirmed cases while the stay-at-home orders stabilize the variations. As the estimated teleworking rates also continue to incline throughout the study period, the teleworking trend can be another driving factor for the growing stay-at-home population. We confirm that there exists overall mobility heterogeneity between the income or population density groups. The study suggests that public mobility trends are in line with the government message urging to stay home. We anticipate our data-driven analysis offers integrated perspectives and serves as evidence to raise public awareness and, consequently, reinforce the importance of social distancing while assisting policymakers.

A Cross-Domain Approach to Analyzing the Short-Run Impact of COVID-19 on the US Electricity Sector.

The novel coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with the US becoming the epicenter of COVID-19 cases since late March. As the US begins to gradually resume economic activity, it is imperative for policymakers and power system operators to take a scientific approach to understanding and predicting the impact on the electricity sector. Here, we release a first-of-its-kind cross-domain open-access data hub, integrating data from across all existing US wholesale electricity markets with COVID-19 case, weather, mobile device location, and satellite imaging data. Leveraging cross-domain insights from public health and mobility data, we rigorously uncover a significant reduction in electricity consumption that is strongly correlated with the number of COVID-19 cases, degree of social distancing, and level of commercial activity.

Potential for inter-state spread of Covid-19 from Arizona, USA: analysis of mobile device location and commercial flight data.

A significant rise of SARS-CoV-2 transmission in Arizona in June 2020 prompted the need to evaluate potential dispersion to other regions in the United States. We evaluate the potential for domestic dissemination of SARS-CoV-2 from Arizona using mobile device-location and scheduled flights data.

The Impact of the Coronavirus Lockdown on Domestic Violence

We use 911 call records and mobile device location data to study the impact of the coronavirus lockdown on domestic violence. The percent of people at home sharply increased at all hours, and nearly doubled during regular working hours, from 45 to 85 percent. Domestic violence increased 12 percent on average and 20 percent during working hours. Using neighborhood-level identifiers, we show that the rate of first-time abuse likely increased even more: 16 percent on average and 23 percent during working hours. Our results contribute to an urgent need to quantify the physical and psychological burdens of prolonged lockdown polices.

Quantifying the Social Distancing Privilege Gap: A Longitudinal Study of Smartphone Movement

Background: In response to the coronavirus pandemic, social distancing became a widely deployed countermeasure in March 2020. We examined whether healthier and wealthier places more successfully implemented social distancing. Methods: Mobile device location data were used to quantify declines in movement by county (n=2,633) in the United States of America, comparing April 15-17 (n=65,544,268 traces) to baseline of February 17 - March 7. Negative binomial regression was used to estimate gradients of privilege across eleven healthcare and economic indicators, adjusting for rurality and stay-at-home mandates. External validation used separate venue-specific data from Google Location Services. Findings: Counties without stay-at-home orders showed a mobility decline of -52·3% (95% CI: -50·3%, -54·3%), slightly less than the decline in mandated areas (-60·8%; 95% CI: -60·0%, -61·6%). Strong linear gradients in privilege were observed. After adjusting for rurality and stay-at-home orders, counties in the highest quintile of social distancing mobility restriction had: 52% less uninsured, 47% more primary care providers, 29% more exercise space, 27% less food insecurity, 26% less child poverty, 17% higher incomes, 14% less overcrowding, 9·6% more racial segregation, 8·2% less youth, 7·4% more elderly, and 6·2% less influenza vaccination, compared to least social distancing areas. Interpretation: Healthier and wealthier counties displayed a social distancing privilege gap, measured via smartphone mobility change. Structural inequities in this key countermeasure will influence immunity, and disease incidence and mortality. Funding: None Declaration of Interests: Authors declare no competing interests. Ethics Approval Statement: The aggregated public county-level data analyzed in this study were not considered human subjects research and exempted from further ethics review.

Big Data Analytics: The New Boundaries of Retail Location Decision Making

Over recent years, the rapid growth of big data and associated analytical tools has provided unparalleled opportunities for retailers to enhance their location decision support activities. To date, there is a lack of research that looks at how retail firms are leveraging such innovation in data and technology. Based on an online survey conducted with Canadian retail location decision makers, this article examines the current state and evolution in (1) the type and scale of location decisions that retail firms undertake; (2) the availability and use of technology and geospatial data within the decision-making process; and (3) the range of location research methods that are employed. The findings highlight that there has been a widespread increase in the availability and use of technology and geospatial data within the decision-making process. The range of analytical approaches has also expanded to include methods that work with new data sources, such as social media and mobile device location data. The adoption and development of big data approaches is also challenged, however, by factors such as information hoarding, a lack of understanding and buy-in from senior management, and a lack of skilled analysts who can manage and synthesize the big data.

Semi‐Supervised Learning in Inferring Mobile Device Locations

With the development of mobility technology, location information has become collectible by various positioning technologies. Different positioning technologies have their advantages and limitations. In particular, location accuracy varies among different technologies. In this paper, we propose semi‐supervised learning in inferring low‐accuracy location data density from high‐accuracy location data density. We focus on the enormous amount of low‐accuracy cell tower triangulation (CTT) calculated mobile device location data and the small amount of high‐accuracy assisted global positioning system (AGPS) pinpointed location data. The CTT and AGPS mobile device location data are collected for each cell tower that serves the devices, and then, the actual distribution is learned from both CTT and AGPS data by semi‐supervised learning, and the likelihood for low‐accuracy CTT location can be used as an accuracy indicator. The proposed method takes the advantage of the existing extensively collected location data and augments it by a machine‐learning algorithm. The method does not only complement the downside of one technology with the other technology but also considers the computing efficiency by extracting features that can segment the large data naturally, which is the essential in divide‐and‐conquer distributed big data processing. This big data approach improves the location accuracy statistically without the added complexity and cost of upgrading or replacing mobile networks or devices. Moreover, also, the proposed method focuses on the location density alignment, which avoids tracking individual user devices and preserves user privacy. Copyright © 2014 John Wiley & Sons, Ltd.

First Amendment Concerns in Governmental Acquisition and Analysis of Mobile Device Location Data

First Amendment Concerns in Governmental Acquisition and Analysis of Mobile Device Location Data