Anonymized Location Data Research Articles

Effects of meteorology and human‐mobility on UK's air quality during COVID‐19

Efforts to prevent the spread of the coronavirus disease 2019 (COVID‐19) pandemic have had profound positive and negative impacts on social and environmental indicators worldwide. For the first time, a scenario of a partial economic shutdown could be measured, and large tech companies published wide‐coverage mobility reports to quantify the impacts on social change with anonymized location data. During the COVID‐19 pandemic, the UK government has employed some of the strictest lockdown periods in the world, causing an immediate halt to travel and business activities. From these repeated lockdown periods, we have gained a snapshot of life without excessive human‐made pollution; this has allowed us to interrogate the interaction between meteorology and air quality with minimal anthropogenic input. Our findings show a warmer 2020 increased the UK's ozone levels by 9%, while reductions in human‐mobility reduced UK‐wide nitrogen dioxide levels by 25% in 2020, which have remained low during the first months of 2021 despite curtailing/ending of restrictions; and a decrease in particulate matter created by meteorological and human drivers. Regionally, London records the highest NO2 and O3 changes, −31% and 35%, respectively, linked to mobility reductions and meteorology.

Anonymized location data reveals trends in legal Cannabis use in communities with increased mental health risks at the start of the COVID-19 pandemic

Background The coronavirus disease 2019 (COVID-19) pandemic has led to increases in felt negative affect for many. This is concerning as individuals at increased risk for mental health issues are often more likely to use substances to cope with stressors. Objectives The aim of the current study is to examine whether communities reporting an increased risk for developing mental health issues showed differential patterns of legal cannabis use as the pandemic began. A secondary goal is to examine the feasibility of using anonymized location data to uncover community consumption patterns of potential concern. Methods Anonymized location data from approximately 10% of devices in the United States provided a count of the number of visitors to 3,335 cannabis retail locations (medical and recreational) each day from December 1st 2019 through April 2020. Visitor counts were merged with the average number of mentally unhealthy days (aMUDs) reported in the Federal Information Processing Standard (FIPS) county the retailer was located along with FIPS county population and poverty rate estimates. A Poisson spline regression predicting visitors by day, aMUDs, as well as their interaction was performed, entering population and poverty rate as covariates. Results As the pandemic began communities reporting a greater aMUDs showed greater visitation to cannabis retailers. Conclusions These results suggest that the COVID-19 pandemic may have led to increased legal cannabis use in at risk communities. They also highlight the value anonymized location data can provide policymakers and practitioners in uncovering community level trends as they confront an increasingly uncertain landscape.

Timing of State and Territorial COVID-19 Stay-at-Home Orders and Changes in Population Movement — United States, March 1–May 31, 2020

SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), is thought to spread from person to person primarily by the respiratory route and mainly through close contact (1). Community mitigation strategies can lower the risk for disease transmission by limiting or preventing person-to-person interactions (2). U.S. states and territories began implementing various community mitigation policies in March 2020. One widely implemented strategy was the issuance of orders requiring persons to stay home, resulting in decreased population movement in some jurisdictions (3). Each state or territory has authority to enact its own laws and policies to protect the public's health, and jurisdictions varied widely in the type and timing of orders issued related to stay-at-home requirements. To identify the broader impact of these stay-at-home orders, using publicly accessible, anonymized location data from mobile devices, CDC and the Georgia Tech Research Institute analyzed changes in population movement relative to stay-at-home orders issued during March 1-May 31, 2020, by all 50 states, the District of Columbia, and five U.S. territories.* During this period, 42 states and territories issued mandatory stay-at-home orders. When counties subject to mandatory state- and territory-issued stay-at-home orders were stratified along rural-urban categories, movement decreased significantly relative to the preorder baseline in all strata. Mandatory stay-at-home orders can help reduce activities associated with the spread of COVID-19, including population movement and close person-to-person contact outside the household.

A dual model/artificial neural network framework for privacy analysis in traffic monitoring systems

Most large scale traffic information systems rely on a combination of fixed sensors (e.g. loop detectors, cameras) and user generated data, the latter in the form of probe traces sent by smartphones or GPS devices onboard vehicles. While this type of data is relatively inexpensive to gather, it can pose multiple privacy risks, even if the location tracks are anonymous. In particular, an issue could be the possibility for an attacker to infer user location tracks from anonymous location data, which affects users privacy. In this article, we propose a new framework for analyzing a variety of privacy problems arising in transportation systems. The state of traffic is modeled by the Lighthill–Whitham–Richards traffic flow model, which is a first order scalar conservation law with a concave flux function. Given a set of traffic flow data, we show that the constraints resulting from this partial differential equation are mixed integer linear inequalities for some decision variable. These constraints allow us to determine the likelihood of two distinct location tracks being generated by the same vehicle. We then use these model-based reidentification metrics to train an artificial neural network classifier. Numerical implementations are performed on experimental data from the Mobile Century experiment, and show that this framework significantly outperforms naive reidentification techniques.

Providing Customised Information Panel Content Based on User Behavioral Patterns

Although mobile applications are commonly using user location and behavior to provide relevant content, public information panels usually lack the ability to adjust the content for a particular user or a group of users. Therefore, we focused on the development of information panels that are able, in combination with a mobile application, to collect anonymous location data about the users, identify key behavioral patterns and provide content that is relevant for the users in the panel vicinity. The key property of our solution is the anonymity of the collected information and privacy in general. The proposed algorithm consists of the data clustering and subsequent analysis. Described solution can be used in any public building or campus that the users visit regularly.

Full Location Privacy Protection Through Restricted Space Cloaking

Anonymous location data may be correlated with restricted spaces like home and office for subject identification. This creates significant privacy risks to users when they disclose their location to applications like location-based services. In this paper, we address this problem with a novel approach named restricted space cloaking. This scheme cloaks a user's location only when the location is inside a restricted space. When in non-restricted spaces, the location is reported as is. We show that this cloaking strategy is capable of full location privacy protection: given a restricted space, the adversary does not know when its owner was there; given a non-restricted location, the adversary does not know who has ever visited there. Such protection is not available from the existing cloaking techniques. In addition to full location privacy protection, the proposed strategy makes it possible for users to cloak their locations without disclosing accurate locations to either a trusted anonymizer or other users. We discuss the implementation challenges and present corresponding solutions. The performance of the proposed solutions is evaluated through simulation.

Human mobility characterization from cellular network data

Anonymous location data from cellular phone networks sheds light on how people move around on a large scale.