Location Of Mobile Device Research Articles

A big-data driven approach to analyzing and modeling human mobility trend under non-pharmaceutical interventions during COVID-19 pandemic

During the unprecedented coronavirus disease 2019 (COVID-19) challenge, non-pharmaceutical interventions became a widely adopted strategy to limit physical movements and interactions to mitigate virus transmissions. For situational awareness and decision-support, quickly available yet accurate big-data analytics about human mobility and social distancing is invaluable to agencies and decision-makers. This paper presents a big-data-driven analytical framework that ingests terabytes of data on a daily basis and quantitatively assesses the human mobility trend during COVID-19. Using mobile device location data of over 150 million monthly active samples in the United States (U.S.), the study successfully measures human mobility with three main metrics at the county level: daily average number of trips per person; daily average person-miles traveled; and daily percentage of residents staying home. A set of generalized additive mixed models is employed to disentangle the policy effect on human mobility from other confounding effects including virus effect, socio-demographic effect, weather effect, industry effect, and spatiotemporal autocorrelation. Results reveal the policy plays a limited, time-decreasing, and region-specific effect on human movement. The stay-at-home orders only contribute to a 3.5%-7.9% decrease in human mobility, while the reopening guidelines lead to a 1.6%-5.2% mobility increase. Results also indicate a reasonable spatial heterogeneity among the U.S. counties, wherein the number of confirmed COVID-19 cases, income levels, industry structure, age and racial distribution play important roles. The data informatics generated by the framework are made available to the public for a timely understanding of mobility trends and policy effects, as well as for time-sensitive decision support to further contain the spread of the virus.

Mobile device location data reveal human mobility response to state-level stay-at-home orders during the COVID-19 pandemic in the USA.

One approach to delaying the spread of the novel coronavirus (COVID-19) is to reduce human travel by imposing travel restriction policies. Understanding the actual human mobility response to such policies remains a challenge owing to the lack of an observed and large-scale dataset describing human mobility during the pandemic. This study uses an integrated dataset, consisting of anonymized and privacy-protected location data from over 150 million monthly active samples in the USA, COVID-19 case data and census population information, to uncover mobility changes during COVID-19 and under the stay-at-home state orders in the USA. The study successfully quantifies human mobility responses with three important metrics: daily average number of trips per person; daily average person-miles travelled; and daily percentage of residents staying at home. The data analytics reveal a spontaneous mobility reduction that occurred regardless of government actions and a ‘floor’ phenomenon, where human mobility reached a lower bound and stopped decreasing soon after each state announced the stay-at-home order. A set of longitudinal models is then developed and confirms that the states' stay-at-home policies have only led to about a 5% reduction in average daily human mobility. Lessons learned from the data analytics and longitudinal models offer valuable insights for government actions in preparation for another COVID-19 surge or another virus outbreak in the future.

Mobile device location data reveal human mobility response to state-level stay-at-home orders during the COVID-19 pandemic in the USA: Mobile device location data reveal human mobility response to state-level stay-at-home orders during the COVID-19 pandemic in the USA

Mobile device location data reveal human mobility response to state-level stay-at-home orders during the COVID-19 pandemic in the USA: Mobile device location data reveal human mobility response to state-level stay-at-home orders during the COVID-19 pandemic in the USA

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.

Offloading Time Optimization via Markov Decision Process in Mobile-Edge Computing

Computation offloading from a mobile device to the edge server is an emerging paradigm to reduce completion latency of intensive computations in mobile-edge computing (MEC). In order to satisfy the delay-sensitive computing tasks, offloading time, including task uploading time, task execution time, and results downloading time is adopted as the computational performance metrics for offloading nodes that perform offloaded computing tasks for mobile devices. Therefore, how to minimize the offloading time by selecting an optimal offloading node in MEC is of research importance. This work first investigates a MEC system consisting of mobile devices and heterogeneous edge severs that support various radio access technologies. Then, based on the available bandwidth of heterogeneous edge severs and the location of mobile devices, an optimal offloading node selection strategy is formulated as a Markov decision process (MDP), and solved by employing the value iteration algorithm (VIA). Finally, extensive numerical results demonstrate the effectiveness of the proposed strategy over classic strategies in terms of offloading time.

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.

Mapping Crop Types in Southeast India with Smartphone Crowdsourcing and Deep Learning

High resolution satellite imagery and modern machine learning methods hold the potential to fill existing data gaps in where crops are grown around the world at a sub-field level. However, high resolution crop type maps have remained challenging to create in developing regions due to a lack of ground truth labels for model development. In this work, we explore the use of crowdsourced data, Sentinel-2 and DigitalGlobe imagery, and convolutional neural networks (CNNs) for crop type mapping in India. Plantix, a free app that uses image recognition to help farmers diagnose crop diseases, logged 9 million geolocated photos from 2017–2019 in India, 2 million of which are in the states of Andhra Pradesh and Telangana in India. Crop type labels based on farmer-submitted images were added by domain experts and deep CNNs. The resulting dataset of crop type at coordinates is high in volume, but also high in noise due to location inaccuracies, submissions from out-of-field, and labeling errors. We employed a number of steps to clean the dataset, which included training a CNN on very high resolution DigitalGlobe imagery to filter for points that are within a crop field. With this cleaned dataset, we extracted Sentinel time series at each point and trained another CNN to predict the crop type at each pixel. When evaluated on the highest quality subset of crowdsourced data, the CNN distinguishes rice, cotton, and “other” crops with 74% accuracy in a 3-way classification and outperforms a random forest trained on harmonic regression features. Furthermore, model performance remains stable when low quality points are introduced into the training set. Our results illustrate the potential of non-traditional, high-volume/high-noise datasets for crop type mapping, some improvements that neural networks can achieve over random forests, and the robustness of such methods against moderate levels of training set noise. Lastly, we caution that obstacles like the lack of good Sentinel-2 cloud mask, imperfect mobile device location accuracy, and preservation of privacy while improving data access will need to be addressed before crowdsourcing can widely and reliably be used to map crops in smallholder systems.

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.

Multiresolution spatial generalized linear mixed model for integrating multi-fidelity spatial count data without common identifiers between data sources

A motivating example for this paper is a large human location information system that collects two types of information on mobile device locations: 1) large amounts of low-accuracy cell tower triangulation (CTT) calculated location data and 2) small amounts of high-accuracy assisted global positioning system (AGPS) pinpointed location data. Integrating the CTT and AGPS data and extracting more complete and accurate location information is important to achieve better estimation of the true spatial density. However, the problem is challenging because there is no direct link between the CTT and APGS data. In this paper, we propose a multiresolution spatial generalized linear mixed model to integrate low-accuracy and high-accuracy spatial count data given no direct link between two data sources. The relationship between the high-accuracy data and low-accuracy data is estimated at a low-resolution level, where the relationship between the two types can be better captured, and then the estimated relationship is propagated to the high-resolution level. Using the high-accuracy data, the location information of the low-accuracy data is flexibly adjusted via spatial random effects that are modeled using a Gaussian process. The proposed method is validated using simulated and real data examples.

The effects of physical distancing on population mobility during the COVID-19 pandemic in the UK

The effects of physical distancing on population mobility during the COVID-19 pandemic in the UK

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.

Modelling Offset Regions around Static and Mobile Locations on a Discrete Global Grid System: An IoT Case Study

With the huge volume of location-based point data being generated by Internet of Things (IoT) devices and subsequent rising interest from the Digital Earth community, a need has emerged for spatial operations that are compatible with Digital Earth frameworks, the foundation of which are Discrete Global Grid Systems (DGGSs). Offsetting is a fundamental spatial operation that allows us to determine the region within a given distance of an IoT device location, which is important for visualizing or querying nearby location-based data. Thus, in this paper, we present methods of modelling an offset region around the point location of an IoT device (both static and mobile) that is quantized into a cell of a DGGS. Notably, these methods illustrate how the underlying indexing structure of a DGGS can be utilized to determine the cells in an offset region at different spatial resolutions. For a static IoT device location, we describe a single resolution approach as well as a multiresolution approach that allows us to efficiently determine the cells in an offset region at finer (or coarser) resolutions. For mobile IoT device locations, we describe methods to efficiently determine the cells in successive offset regions at fine and coarse resolutions. Lastly, we present a variety of results that demonstrate the effectiveness of the proposed methods.

A platform for power management based on indoor localization in smart buildings using long short‐term neural networks

AbstractLocation‐based services (LBSs) have drastically changed the way smart cities and smart buildings operate, giving a new dimension to the life of citizens. LBS have various applications ranging from power management, marketing, vehicle to everything communication to social networking, and many other applications. The concept of LBS relies on the estimation of a mobile device location either inside a city or a building. In this work, we present our three‐layer collaborative LBS platform for various types of users and buildings. The first of these layers is the hardware layer consisting of the hardware used for location estimation inside smart buildings. On top of the hardware layers exists the indoor localization layer, at which we implement our indoor localization algorithms using long short‐term neural networks for location estimation. Finally, the collaborative LBS system lies on top of other layers. The novelty of our approach stems from providing a complete layered architecture for LBS usage for power management. The proposed system offers collaborative features and services, that is, input to power management system that optimizes power usage in smart buildings based on user locations.

Enhancement of the Dynamic Computation-Offloading Service Selection Framework in Mobile Cloud Environment

In the era of cloud computing, any mobile device can augment its capabilities by using Cloud computation service. There are different services provided by different cloud service providers. The mobile device has to access the cloud service with minimum response time. So many a times, instead of a distant cloud, nearest cloudlet is chosen to access the service. But according to the mobility of the user, choosing the right service provider is a herculean task. Hence this paper suggests a framework to choose a cloudlet service provider in a multi-user computation offloading environment and accommodate the service that is adaptive based on the movement of the mobile device. This paper defines a framework which comprises of basically two components. The foremost one is Fuzzy KNN component which classifies the mobile device based on the access range of the device with a nearby cloudlet. The later component provides a dynamic service depending on the changes in the mobile device location. The framework exploits Fuzzy K nearest neighbour (KNN) and Hidden Markov Model to enhance the Dynamic computation-offloading service selection (EDCOSS) framework. The EDCOSS framework is analysed and tested in a simulation environment to verify the efficiency of the framework in terms of convergence of the algorithm towards computation cost with respect to different number of clients and communication channels.

Deep Convolutional Neural Networks for Indoor Localization with CSI Images

With the increasing demand of location-based services, Wi-Fi based localization has attracted great interest because it provides ubiquitous access in indoor environments. In this paper, we propose CiFi, deep convolutional neural networks (DCNN) for indoor localization with commodity 5GHz WiFi. Leveraging a modified device driver, we extract phase data of channel state information (CSI), which is used to estimate the angle of arrival (AoA). We then create estimated AoA images as input to a DCNN, to train the weights in the offline phase. The location of mobile device is predicted based using the trained DCNN and new CSI AoA images. We implement the proposed CiFi system with commodity Wi-Fi devices in the 5GHz band and verify its performance with extensive experiments in two representative indoor environments.

Rapid eradication assessment (REA): a tool for pest absence confirmation

Abstract ContextEradication of invasive species is necessary to protect and assist the recovery of native species and ecosystems. Knowing when to declare an eradication has been successful after ongoing non-detections is a challenge. AimsThe rapid eradication assessment (REA) model is a powerful simulation framework to determine, given model parameters and a fixed level of monitoring effort, the level of confidence in declaring the success of pest eradication. The aim of the present study was to extend the current functionality of the REA model for broader applicability. MethodsThe REA model was advanced so that it was able to account for (1) usage of multiple static device types with different probabilities of detection, (2) incursion detection at a known location and (3) usage of mobile detection devices, which are increasingly being used in conservation. Key resultsAn invasive rat incursion response on Great Mercury Island in New Zealand is used as a comprehensive example to demonstrate the distribution of estimated probability of pest absence among the cases using the current REA model and the extensions presented here. ConclusionsAlthough Great Mercury Island already had a sparse but extensive island-wide network of static biosecurity surveillance devices, and deployed additional static devices around the area of incursion, the greatest improvement in the estimated probability of pest absence following a rat incursion was from additionally using a trained rodent-detection dog. ImplicationsThe added functionality in the REA model and demonstration of its use on a real-world scenario will allow more realistic application by wildlife managers.

MIGRATE: Mobile Device Virtualisation Through State Transfer

Delegation of processing tasks to the network has moved from cloud-based schemes to edge computing solutions where nearby servers process requests in a timely manner. Virtualisation technologies have recently given data cloud and network providers the required flexibility to offer such on-demand resources. However, the maintenance of close computing resources presents a challenge when the served devices are on the move. In this case, if processing continuity is desired, a transference of processing resources and task state should be committed to maintain the service to end devices. The solution here presented, MIGRATE, proposes the concept of virtual mobile devices (vMDs) implemented as Virtual Functions (VxF) and acting as virtual representatives of physical processing devices. vMDs are instantiated at the edge of the access network, following a Multi-Access Edge Computing (MEC) approach, and move across different virtualisation domains. MIGRATE provides seamless and efficient transference of these software entities to follow the real location of mobile devices and continue supporting their physical counterparts. Software Defined Networks and Management and Operation functions are exploited to “migrate” vMDs to new virtualisation domains by forwarding data flows to the former domain until the new one is prepared, while a distributed data base avoids the transference of data. The solution has been deployed in a reference vehicular scenario at the Institute of Telecommunications Aveiro premises within the 5GINFIRE European project. In particular, the system has been evaluated under different virtualisation domains to study the operation of the migration approach in a vehicular monitoring scenario. The results validate the system from the application viewpoint with a Web monitoring tool, and the migration of the digital twin provided as VxF is analysed attending to the modification of data flows, indicating a seamless transition between virtualisation domains in a timely manner.

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.

Preface

Abstract. We are now living in a mobile information era, which is fundamentally changing science and society. Location Based Services (LBS), which deliver information depending on the location of the (mobile) device and user, play a key role in this mobile information era. In recent years, lots of progress has been achieved in the research field of LBS, due to the increasingly maturity of the underpinning communication technologies and mobile devices. LBS have become more and more popular in not only citywide outdoor environments, but also shopping malls, museums, and many other indoor environments. They have been applied for emergency services, tourism services, intelligent transport services, social networking, gaming, assistive services, etc.Since its initiation by Georg Gartner from TU Wien (Austria) in 2002, the LBS conference series has become one of the most important scientific events decided to LBS. The conferences have been held in Vienna (2002, 2004, 2005), Hong Kong (2007), Salzburg (2008), Nottingham (2009), Guangzhou (2010), Vienna (2011), Munich (2012), Shanghai (2013), Vienna (2014), Augsburg (2015), Vienna (2016), and Zurich (2018). Starting from 2015, the LBS conferences have become the annual event of the Commission on Location-Based Services of the International Cartographic Association (ICA) (http://lbs.icaci.org/). In November 2019, the 15th LBS conference (LBS 2019) will be hosted by TU Wien in Vienna, Austria.This book contains a selection of peer-reviewed full papers submitted to LBS 2019. All the chapters have been accepted after a rigor double-blind reviewing process. The book provides a general picture of recent research activities related to the domain of LBS. Such activities emerged in the last years, especially concerning issues of outdoor/indoor positioning, smart environment, spatial modeling, personalization and context-awareness, cartographic communication, novel user interfaces, crowd sourcing, social media, mobility analytics, usability and privacy.We would like to thank all the authors for their excellent work and all referees for their critical and constructive reviews. We hope you enjoy reading these papers, and look forward to your participation in the future LBS conferences.