The Ethics of Location Tracking During the COVID-19 Pandemic and Beyond: Building Awareness and Consensus

This article highlights the relatively limited but growing discussion surrounding ethical guidelines for the use of location tracking technology. After a review of recent literature related to location data and geoprivacy, this article is divided into two sections: The first highlights views of public officials and location tracking experts over the potential misuse of location data, especially in the context of the COVID-19 pandemic. The data come from available transcripts of the Location Tech Task Force organized in 2020 by the American Geographical Society as part of its EthicalGEO initiative. The second section documents various institutional approaches to elevate the dialogue and inform governance of location-based data and technology, including the development of the Locus Charter, an emerging international framework on the ethical use of location data. In conclusion, we urge the professional and academic geographic communities to engage with the elaboration and dissemination of ethical frameworks to guide the use and management of data from location tracking technology. (English) [ FROM AUTHOR] La reciente erudición geográfica feminista ha urgido a los geógrafos a distanciarse de los enfoques androcéntricos y eurocéntricos, y a abrir la disciplina a perspectivas diversas. En tanto que numerosos estudios se han enfocado a diversificar y descolonizar la geografía por medio de prácticas de reclutamiento, tutoría y producción de conocimiento, solo muy pocos han analizado cómo se traduce la diversidad en las prácticas de enseñanza, en particular en contextos donde la diversidad está relativamente bien establecida entre el personal. Basado en una encuesta por cuestionario entre el personal docente, en un análisis del contenido de los programas de los cursos y un análisis cuantitativo de los datos de los empleados del departamento, este artículo explora hasta qué punto la diversidad dentro del departamento conduce a la diversidad en las prácticas de la enseñanza. Desarrollando un marco de los espacios de la diversidad, analizamos tres espacios que potencialmente permiten practicar la diversidad en la enseñanza: El espacio académico del departamento promueve la libre elección de los tópicos de investigación y enseñanza, y las condiciones flexibles del trabajo;el espacio del departamento permite a los individuos asumir compromisos en la configuración de la enseñanza geográfica;y el espacio del conocimiento promueve la diversidad como un ideal. Sin embargo, encontramos que practicar la diversidad en geografía implica enfrentar los retos de las estructuras universitarias tradicionales y neoliberales y de las jerarquías formales y percibidas. Aún más, existe una necesidad de prácticas concretas sobre diversidad a niveles individuales e institucionales para llevar activamente las diversas perspectivas al salón de clase. (Spanish) [ FROM AUTHOR] 女权地理学的最新研究, 敦促地理学者远离以男性和欧洲为核心的方法, 接受不同的观点。许多研究都侧重通过招聘、指导和知识生产, 去实现地理学的多样化和去殖民化。只有少数研究分析了多样性如何转化为教学实践（尤其是在教职员工多样性相对稳定的情况下）。基于教师问卷调查、课程大纲内容分析以及对地理系员工数据的定量分析, 本文探讨了地理系的多样性在多大程度上导致教学实践的多样性。我们建立了一个多样性的空间框架, 分析了可能实现教学多样性的三个空间："学术空间"促进对研究课题、课程题目和灵活工作条件的自由选择, "地理系空间"使个人能够参与地理教学的建设, "知识空间"促进理想的多样性。然而, 传统的和新自由主义的大学体系以及严格的等级制度, 是实现地理多样性的挑战。此外, 还需要在个人和体制层面采取切实的多样性实践, 积极地将不同观点带入课堂。 (Chinese) [ FROM AUTHOR] Copyright of Professional Geographer is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Spatial and social disparities in the decline of activities during the COVID-19 lockdown in Greater London.

We use data on human mobility obtained from mobile applications to explore the activity patterns in the neighbourhoods of Greater London as they emerged from the first wave of COVID-19 lockdown restrictions during summer 2020 and analyse how the lockdown guidelines have exposed the socio-spatial fragmentation between urban communities. The location data are spatially aggregated to 1 km2 grids and cross-checked against publicly available mobility metrics (e.g. Google COVID-19 Community Report, Apple Mobility Trends Report). They are then linked to geodemographic classifications to compare the average decline of activities in the areas with different sociodemographic characteristics. We found that the activities in the deprived areas dominated by minority groups declined less compared to the Greater London average, leaving those communities more exposed to the virus. Meanwhile, the activity levels declined more in affluent areas dominated by white-collar jobs. Furthermore, due to the closure of non-essential stores, activities declined more in premium shopping destinations and less in suburban high streets.

Modeling COVID-19 with big mobility data: Surveillance and reaffirming the people in the data

To better understand the COVID-19 pandemic, public health researchers turned to "big mobility data”—location data collected from mobile devices by companies engaged in surveillance capitalism. Publishing formerly private big mobility datasets, firms trumpeted their efforts to "fight” COVID-19 and researchers highlighted the potential of big mobility data to improve infectious disease models tracking the pandemic. However, these collaborations are defined by asymmetries in information, access, and power. The release of data is characterized by a lack of obligation on the part of the data provider towards public health goals, particularly those committed to a community-based, participatory model. There is a lack of appropriate reciprocities between data company, data subject, researcher, and community. People are de-centered, surveillance is de-linked from action while the agendas of public health and surveillance capitalism grow closer. This article argues that the current use of big mobility data in the COVID-19 pandemic represents a poor approach with respect to community and person-centered frameworks. © The Author(s) 2023.

Changes in Electricity Use following COVID-19 Stay-at-home Behavior

This article uses hourly electricity consumption data from the PJM Interconnection in the United States and stay-at-home metrics from cell phone location data to study the effect of the COVID-19 pandemic on electricity consumption using a difference-in-predicted-differences strategy. I show that while in the first months of the COVID-19 pandemic total electricity consumption declined by 2.7–3.8% relative to a predicted counterfactual, in June through August 2020 electricity consumption was 2.1–3.5% higher than the predicted counterfactual. Time spent at home reduces electricity consumption, and a reduction in time at home after May lead to increased electricity consumption in the summer months. In addition, higher temperatures had an increased effect on electricity consumption in 2020 relative to previous years. Nationwide monthly data on electricity consumption by load class reveals that commercial and industrial consumption was below its expected baseline from March-December 2020, while residential consumption was above its expected baseline, peaking in July. This suggests that increased demand for residential cooling offset declines in commercial and industrial demand for electricity. Estimates of the total effect of the pandemic on electricity consumption from March through December 2020 suggest that early reductions in electricity use were offset by later increases, implying that any expected "silver lining” of decreased emissions from electricity generation may be smaller than previously thought. © 2023 by the IAEE. All rights reserved.

The effect of COVID-19 on customer traffic: A case study of Food and Beverage stores in Erie County, New York

The declaration of the COVID-19 pandemic and the resulting lockdowns brought focus on the importance of the retail sector for community well-being. The restrictive government policies that were put into place to curb the spread of COVID-19 virus added pressure on retailers to adapt to the subsequent changes in consumption. This research, using a case study of Erie County in the State of New York (NY), investigates these changes in visitation patterns for a commercial service sector that was deemed 'essential' - food and beverage. This study uses mobile location data to identify variations in shopping patterns for independent and chain stores. The study finds that by comparing the pre-pandemic to pandemic, there were changes to visitation patterns over time and between retail types. While the study highlights the potential to use mobile data to study shifts in consumption behaviours, the paper also reveals several challenges in using such data. © 2022 IEEE Computer Society. All rights reserved.

COVID-19 and income profile: How communities in the United States responded to mobility restrictions in the pandemic's early stages.

Mobility interventions in communities play a critical role in containing a pandemic at an early stage. The real-world practice of social distancing can enlighten policymakers and help them implement more efficient and effective control measures. A lack of such research using real-world observations initiates this article. We analyzed the social distancing performance of 66,149 census tracts from 3,142 counties in the United States with a specific focus on income profile. Six daily mobility metrics, including a social distancing index, stay-at-home percentage, miles traveled per person, trip rate, work trip rate, and non-work trip rate, were produced for each census tract using the location data from over 100 million anonymous devices on a monthly basis. Each mobility metric was further tabulated by three perspectives of social distancing performance: "best performance," "effort," and "consistency." We found that for all 18 indicators, high-income communities demonstrated better social distancing performance. Such disparities between communities of different income levels are presented in detail in this article. The comparisons across scenarios also raise other concerns for low-income communities, such as employment status, working conditions, and accessibility to basic needs. This article lays out a series of facts extracted from real-world data and offers compelling perspectives for future discussions.

Optimizing the Cleaning and Disinfection Scheme for Dockless Shared Bikes

Shared bikes can help cities achieve carbon neutrality goals. Cleaning and disinfection are vital procedures of the maintenance of shared bikes, especially during the COVID-19 pandemic because shared bikes could be a transmission intermediary of viruses. This study proposes an optimization model of the cleaning and disinfection scheme of the dockless shared bikes. The disinfection is assumed to be performed at night, when the usage is lowest. By regarding the disinfection staff as traveling salesmen, the model is formulated as an extension of the Multidepot Multiple Traveling Salesman Problem (MDMTSP). The objective function is to minimize the total cost;which consists of the cost associated with the working time and per-capita cost of the disinfection staff. A heuristic algorithm combining k -means clustering and genetic algorithm (K-GA) is adopted to find the lower bound solution. Then, the K-GA-adjustment algorithm has been adopted to find the solutions that satisfy the constraints. To reduce the computing time needed, an approximate function for the lower bound of the optimal number of disinfection staff is obtained by constructing a Continuous Approximation (CA) model. A case study based on real location data of shared bikes in Chengdu, China, is performed to show how the maintenance department could adopt the optimization framework to design an efficient scheme to clean and disinfect the shared bikes. [ABSTRACT FROM AUTHOR] Copyright of Journal of Advanced Transportation is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Individual-based epidemiological model of COVID19 using location data

Because human movement spreads infection, and mobility is a good proxy for other social distancing measures, human mobility has been an important factor in the COVID19 epidemic. Therefore, the control of human mobility is one of the countermeasures used to suppress an epidemic.As a notable feature, COVID19 has had multiple waves (subepidemics). Understanding the causes of the start and end of each wave has important implications for a policy evaluation and the timely implementation of countermeasures. Some of the waves have been correlated with the changes in mobility, and some can be attributed to the emergence of new variants. However, the start and end of some of the waves are difficult to explain through known factors.To evaluate the effect of human mobility, we built a stochastic model incorporating individual movements of 500,000 people obtained from anonymized, user-approved location data of smartphones throughout Japan. Instead of using aggregate values of human mobility, our model tracks the movements of individuals and predicts the infection of all persons within the entire country. Although the model only has a single static parameter, it successfully reproduced the occurrence of three waves of the number of confirmed cases within the study period of March 01 to December 31, 2020 in Japan. It was previously difficult to explain the end of the second wave and the start of the third wave in the study period by human mobility alone. Our results suggest the importance of tracking individual movements instead of relaying the aggregate values of human mobility. © 2022 IEEE.

Aggregate Learning for Mixed Frequency Data

Large and acute economic shocks such as the 2007-2009 financial crisis and the current COVID-19 infections rapidly change the economic environment. In such a situation, real-time analysis of regional heterogeneity of economic conditions using alternative data is essential. We take advantage of spatio-temporal granularity of alternative data and propose a Mixed-Frequency Aggregate Learning (MF-AGL) model that predicts economic indicators for the smaller areas in real-time. We apply the model for the real-world problem;prediction of the number of job applicants which is closely related to the unemployment rates. We find that the proposed model predicts (i) the regional heterogeneity of the labor market condition and (ii) the rapidly changing economic status. The model can be applied to various tasks, especially economic analysis. © 2022 IEEE.

The relationship between Twitter sentiment and mobility during the COVID-19 pandemic

The COVID-19 pandemic has affected public behavior in a variety of ways. Concerns about the spread of a hitherto unknown virus drove numerous changes in public behavior, including a greater tendency to self-isolate at home. In this study, we assigned numerical scores to key sentiments expressed in COVID-19-related posts on major social media platform Twitter to measure changes in public sentiment during the pandemic. We also examined the relationship between mobility in various locations around Japan and scores for sentiments such as dislike and fear. Our research provided evidence of a tendency for mobility to decline (i.e. for more people to self-isolate at home) roughly one month after scores for negative public sentiment regarding COVID-19 increased. Mobility is closely connected with a variety of economic activities, mainly in service industries. This suggests that the sentiment in Twitter postings on COVID-19 that we discuss in this study is a leading indicator of changes in mobility (the extent to which people self-isolate at home), demonstrating the effectiveness of Twitter data in forecasting short-term changes in economic activity during the pandemic. © 2022 IEEE.

An Evidence Study of Long-term Impacts on Mobility Patterns Brought by COVID-19

The outbreak of COVID-19 at the end of 2019 has posed an enormous threat to people's physical and psychological health, especially those who are infected during the epidemic. Understanding how the infected people behaved during the pandemic and whether long-term effects are exerted even after they were cured is essential for guiding them to conduct a more comprehensive recovery. Large scale crowd-sourced data provides a chance to investigate their behavior patterns. In this paper, we explore the possible differences in mobility patterns between the infected and the uninfected, relying on a large volume of crowd -sourced location data contributed by smartphone users consisting of 11,414 infected cases and 12,793 uninfected people between Jun. 1, 2019 and Dec 31, 2020 in Wuhan, China. We characterize mobility distinctions of the two groups by introducing five mobility indicators that accurately capture spatio-temporal patterns of human mobility. We reveal that the infected kept higher mobility level during the pandemic. Moreover, the COVID-19 caused lower recovery efficiency on mobility of the infected, including later recovery time, lower speed and worse status. © 2022 IEEE.

Quantifying the impact of COVID-19 on travel behavior in different socio-economic segments.

The COVID-19 pandemic has resulted in substantial negative impacts on social equity. To investigate transport inequities in communities with varying medical resources and COVID controlling measures during the COVID pandemic and to develop transport-related policies for the post-COVID-19 world, it is necessary to evaluate how the pandemic has affected travel behavior patterns in different socio-economic segments (SES). We first analyze the travel behavior change percentage due to COVID, e.g., increased working from home (WFH), decreased in-person shopping trips, decreased public transit trips, and canceled overnight trips of individuals with varying age, gender, education levels, and household income, based on the most recent US Household Pulse Survey census data during Aug 2020 âˆ¼ Dec 2021. We then quantify the impact of COVID-19 on travel behavior of different socio-economic segments, using integrated mobile device location data in the USA over the period 1 Jan 2020-20 Apr 2021. Fixed-effect panel regression models are proposed to statistically estimate the impact of COVID monitoring measures and medical resources on travel behavior such as nonwork/work trips, travel miles, out-of-state trips, and the incidence of WFH for low SES and high SES. We find that as exposure to COVID increases, the number of trips, traveling miles, and overnight trips started to bounce back to pre-COVID levels, while the incidence of WFH remained relatively stable and did not tend to return to pre-COVID level. We find that the increase in new COVID cases has a significant impact on the number of work trips in the low SES but has little impact on the number of work trips in the high SES. We find that the fewer medical resources there are, the fewer mobility behavior changes that individuals in the low SES will undertake. The findings have implications for understanding the heterogeneous mobility response of individuals in different SES to various COVID waves and thus provide insights into the equitable transport governance and resiliency of the transport system in the "post-COVID" era.

The Real Estate: Lane Brown: What If the Rent Surge Is Based on a Lie? Investigating a COVID-era mystery

But a spokesperson told me ina statement, "Rent prices are driven primarilyby supply and demand, and in NewYork, vacancy is very low, causing rents torise." But this is only one factor in NewYork's rent-affordability crisis, and mothballingthese units probably isn't what sentthe price of market-rate Manhattan one-bedroomsspiraling toward infinity. Some argue--including the plaintiffs ofover a dozen class-action lawsuits filed in thewake of ProPublica's story--that RealPage'ssoftware allows individual landlords to keeptheir hands clean while indirectly colludingto inflate prices. The Real Estate: Lane Brown: What If the Rent Surge Is Based on a Lie?. [Extracted from the article] Copyright of New York is the property of New York Media and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Urban exodus? Understanding human mobility in Britain during the COVID-19 pandemic using Meta-Facebook data.

Existing empirical work has focused on assessing the effectiveness of nonpharmaceutical interventions on human mobility to contain the spread of COVID-19. Less is known about the ways in which the COVID-19 pandemic has reshaped the spatial patterns of population movement within countries. Anecdotal evidence of an urban exodus from large cities to rural areas emerged during early phases of the pandemic across western societies. Yet, these claims have not been empirically assessed. Traditional data sources, such as censuses offer coarse temporal frequency to analyse population movement over infrequent time intervals. Drawing on a data set of 21 million observations from Meta-Facebook users, we aim to analyse the extent and evolution of changes in the spatial patterns of population movement across the rural-urban continuum in Britain over an 18-month period from March 2020 to August 2021. Our findings show an overall and sustained decline in population movement during periods of high stringency measures, with the most densely populated areas reporting the largest reductions. During these periods, we also find evidence of higher-than-average mobility from high-density population areas to low-density areas, lending some support to claims of large-scale population movements from large cities. Yet, we show that these trends were temporary. Overall mobility levels trended back to precoronavirus levels after the easing of nonpharmaceutical interventions. Following these interventions, we found a reduction in movement to low-density areas and a rise in mobility to high-density agglomerations. Overall, these findings reveal that while COVID-19 generated shock waves leading to temporary changes in the patterns of population movement in Britain, the resulting vibrations have not significantly reshaped the prevalent structures in the national pattern of population movement. As of 2021, internal population movements sit at an intermediate level between those observed pre- and early phases of the pandemic.

A Study on Guaranteed Privacy using One-time Identification Code on Digital Health Keeper

Recently, epidemiological investigation technology for identifying infected persons based on smart phone location data has been used to prevent threats by quickly finding close contacts who may be in the early stage of infection. In addition, in order to prevent the spread of COVID-19, the technology is used for rehabilitation through video call-based EEG, ECG, and EMG sensor-based treatment support, thereby preventing close contacts with infected people and protecting medical staff and patients. In most cases, there exists security and privacy concerns. This paper studies pseudonymization to protect security and personal privacy. The core of the technology proposal to enhance security and privacy in the loT and sensing-based medical technology environment is to approach the NFC network tagging-based OTAC authentication technology from a completely different perspective. This paper suggests a new service direction that can be used in the development of a system that protects personal security and personal information. The proposed technology is valuable to security and privacy. © 2022 IEEE.

On the Implementation of Contact Tracing via GPS

The covid-19 pandemic has impacted the world. One of the mitigation technique to limit the spread of the virus is contact tracing. Contact tracing techniques applies to any infectious disease. Digital contact tracing via mobile phone using GPS coordinates was investigated. Implementation decision such as the location service's configuration mode has an impact on the accuracy of location data captured as well as the battery usage. The limitations and issues associated to the implementation of mobile contact tracing applications are identified. © 2022 IEEE.

Location Data: A Delphi Study of its Ethical Implications for the News Industry

Location data is used in many aspects of journalism today. For example, journalists can identify the number of COVID-19 cases by neighborhood, map those, and then share hot spots with their audiences. They can break down political allegiances, block by block, and predict elections. They can tag location data on reported events and filter those to provide a neighborhood-specific newsletter, and so on. But how is this location data acquired and attended to? This study examines how location data is being used by journalists and the ethical concerns that can arise with such data. Recent location data breaches with third-party technology companies have raised questions about how this kind of data is collected, stored, maintained, and shared on a wider scale. This study used the Delphi method of progressive interviews with a panel of subject matter experts and consisted of three rounds of discussions with U.S. journalists and journalism ethicists. The findings identify several themes of how location data poses new storytelling opportunities and significant ethical issues related to privacy, transparency, and validity. Implications for future uses of location data in journalism are discussed, including placing such data into contexts both in the profession and in the academy.

Exploring the role of spatial configuration and human behavior on the spread of the epidemic: A study of factors that affect Covid-19 spreading in the city

This research explores how exterior public space - defined through the configuration of the city - and human behavior affect the spread of disease. In order to understand the virus spreading mechanism and influencing factors of the epidemic which accompany residents' movement, this study attempts to reproduce the process of virus spreading in city areas through computer simulation. The simulation can be divided into residents' movement simulation and the virus spreading simulation. First, the Agent-based model (ABM) can effectively simulate the behavior of the individual and crowd;real location data - uploaded by residents via mobile phone applications - is used as a behavioral driving force for the agent's movement. Second, a mathematical model of infectious diseases is constructed based on SIR (SEIR) Compartmental models in epidemiology. Finally, by analyzing the simulation results of the agent's movement in the city and the virus spreading under different conditions, the influence of multiple factors of city configuration and human behavior on the virus spreading process is explored, and the effectiveness of countermeasures such as social distancing and lockdown are further demonstrated. © Association for Computer Aided Design in Architecture Annual Conference, ACADIA 2021.

A Secure and Efficient Blockchain-Based Data Sharing Scheme for Location Data

Location data containing trajectory information is of great value, but its unrestricted sharing also brings problems such as data abuse, privacy threat and lack of audit evidence. Especially when it comes to researchers or governments to collect location data to stem the spread of COVID-19, the authorization of the data owner is often missing. In this paper, we propose a data sharing scheme for location data based on a high-performance consortium blockchain combined with proxy re-encryption, which can guarantee location data sharing under user authorization while ensure the efficiency and security in the process of data sharing. The performance analysis and simulation results illustrate that the proposed scheme can meet the performance requirements in practical scenarios, guarantee the privacy of personal location data while making the data useful for stemming the spread of COVID-19. © 2022 ACM.

Note: Home Location Detection from Mobile Phone Data: Evidence from Togo

Algorithms for home location inference from mobile phone data are frequently used to make high-stakes policy decisions, particularly when traditional sources of location data are unreliable or out of date. This paper documents analysis we performed in support of the government of Togo during the COVID-19 pandemic, using location information from mobile phone data to direct emergency humanitarian aid to individuals in specific geographic regions. This analysis, based on mobile phone records from millions of Togolese subscribers, highlights three main results. First, we show that a simple algorithm based on call frequencies performs reasonably well in identifying home locations, and may be suitable in contexts where machine learning methods are not feasible. Second, when machine learning algorithms can be trained with reliable and representative data, we find that they generally out-perform simpler frequency-based approaches. Third, we document considerable heterogeneity in the accuracy of home location inference algorithms across population subgroups, and discuss strategies to ensure that vulnerable mobile phone subscribers are not disadvantaged by home location inference algorithms. © 2022 Owner/Author.