Use Of Mobile Data Research Articles

Understanding the Use of Mobility Data in Disasters: Exploratory Qualitative Study of COVID-19 User Feedback.

Human mobility data have been used as a potential novel data source to guide policies and response planning during the COVID-19 global pandemic. The COVID-19 Mobility Data Network (CMDN) facilitated the use of human mobility data around the world. Both researchers and policy makers assumed that mobility data would provide insights to help policy makers and response planners. However, evidence that human mobility data were operationally useful and provided added value for public health response planners remains largely unknown. This exploratory study focuses on advancing the understanding of the use of human mobility data during the early phase of the COVID-19 pandemic. The study explored how researchers and practitioners around the world used these data in response planning and policy making, focusing on processing data and human factors enabling or hindering use of the data. Our project was based on phenomenology and used an inductive approach to thematic analysis. Transcripts were open-coded to create the codebook that was then applied by 2 team members who blind-coded all transcripts. Consensus coding was used for coding discrepancies. Interviews were conducted with 45 individuals during the early period of the COVID-19 pandemic. Although some teams used mobility data for response planning, few were able to describe their uses in policy making, and there were no standardized ways that teams used mobility data. Mobility data played a larger role in providing situational awareness for government partners, helping to understand where people were moving in relation to the spread of COVID-19 variants and reactions to stay-at-home orders. Interviewees who felt they were more successful using mobility data often cited an individual who was able to answer general questions about mobility data; provide interactive feedback on results; and enable a 2-way communication exchange about data, meaning, value, and potential use. Human mobility data were used as a novel data source in the COVID-19 pandemic by a network of academic researchers and practitioners using privacy-preserving and anonymized mobility data. This study reflects the processes in analyzing and communicating human mobility data, as well as how these data were used in response planning and how the data were intended for use in policy making. The study reveals several valuable use cases. Ultimately, the role of a data translator was crucial in understanding the complexities of this novel data source. With this role, teams were able to adapt workflows, visualizations, and reports to align with end users and decision makers while communicating this information meaningfully to address the goals of responders and policy makers.

Mobility Data Science: Perspectives and Challenges

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of Global Positioning System (GPS)–equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated a significant impact in various domains, including traffic management, urban planning, and health sciences. In this article, we present the domain of mobility data science. Towards a unified approach to mobility data science, we present a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state-of-the-art, and describe open challenges for the research community in the coming years.

Convergence in Mobility Data Sets From Apple, Google, and Meta.

The higher movement of people was one of the variables that contributed to the spread of the infectious agent SARS-CoV-2 during the COVID-19 pandemic. Governments worldwide responded to the virus by implementing measures that would restrict people's movements, and consequently, the spread of the disease. During the onset of the pandemic, the technology companies Apple, Google, and Meta used their infrastructure to anonymously gather mobility reports from their users. This study aims to compare mobility data reports collected by Apple, Google, and Meta (formerly Facebook) during the COVID-19 pandemic and a major winter storm in Texas in 2021. We aim to explore the hypothesis that different people exhibit similar mobility trends during dramatic events and to emphasize the importance of this type of data for public health measures. The study also aims to promote evidence for companies to continue releasing mobility trends data, given that all 3 companies have discontinued these services. In this study, we collected mobility data spanning from 2020 to 2022 from 3 major tech companies: Apple, Google, and Meta. Our analysis focused on 58 countries that are common to all 3 databases, enabling us to conduct a comprehensive global-scale analysis. By using the winter storm that occurred in Texas in 20201 as a benchmark, we were able to assess the robustness of the mobility data obtained from the 3 companies and ensure the integrity of our findings. Our study revealed convergence in the mobility trends observed across different companies during the onset of significant disasters, such as the first year of the COVID-19 pandemic and the winter storm that impacted Texas in 2021. Specifically, we observed strong positive correlations (r=0.96) in the mobility data collected from different tech companies during the first year of the pandemic. Furthermore, our analysis of mobility data during the 2021 winter storm in Texas showed a similar convergence of trends. Additionally, we found that periods of stay-at-home orders were reflected in the data, with record-low mobility and record-high stay-at-home figures. Our findings provide valuable insights into the ways in which major disruptive events can impact patterns of human mobility; moreover, the convergence of data across distinct methodologies highlights the potential value of leveraging mobility data from multiple sources for informing public health decision-making. Therefore, we conclude that the use of mobility data is an asset for health authorities to consider during natural disasters, as we determined that the data sets from 3 companies yielded convergent mobility patterns. Comparatively, data obtained from a single source would be limited, and therefore, more difficult to interpret, requiring careful analysis.

A new RNN based machine learning model to forecast COVID-19 incidence, enhanced by the use of mobility data from the bike-sharing service in Madrid

As a respiratory virus, COVID-19 propagates based on human-to-human interactions with positive COVID-19 cases. The temporal evolution of new COVID-19 infections depends on the existing number of COVID-19 infections and the people's mobility. This article proposes a new model to predict upcoming COVID-19 incidence values that combines both current and near-past incidence values together with mobility data. The model is applied to the city of Madrid (Spain). The city is divided into districts. The weekly COVID-19 incidence data per district is used jointly with a mobility estimation based on the number of rides reported by the bike-sharing service in the city of Madrid (BiciMAD). The model employs a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) to detect temporal patterns for COVID-19 infections and mobility data, and combines the output of the LSTM layers into a dense layer that can learn the spatial patterns (the spread of the virus between districts). A baseline model that employs a similar RNN but only based on the COVID-19 confirmed cases with no mobility data is presented and used to estimate the model gain when adding mobility data. The results show that using the bike-sharing mobility estimation the proposed model increases the accuracy by 11.7% compared with the baseline model.

Public health research using cell phone derived mobility data in sub-Saharan Africa: Ethical issues.

The movements of humans have a significant impact on population health. While studies of such movements are as old as public health itself, the COVID-19 pandemic has raised the profile of mobility research using digital technologies to track transmission routes and calculate the effects of health policies, such as lockdowns. In sub-Saharan Africa, the high prevalence of cell phone and smartphone use is a source of potentially valuable mobility data for public health purposes. Researchers can access call data records, passively collected in real time from millions of clients by cell phone companies, and associate these records with other data sets to generate insights, make predictions or draw possible policy implications. The use of mobility data from this source could have a range of significant benefits for society, from better control of infectious diseases, improved city planning, more efficient transportation systems and the optimisation of health resources. We discuss key ethical issues raised by public health studies using mobility data from cell phones in sub-Saharan Africa and identify six key ethical challenge areas: autonomy, including consent and individual or group privacy; bias and representativeness; community awareness, engagement and trust; function creep and accountability; stakeholder relationships and power dynamics; and the translation of mobility analyses into health policy. We emphasise the ethical importance of narrowing knowledge gaps between researchers, policymakers and the general public. Given that individuals do not really provide valid consent for the research use of phone data tracking their movements, community understanding and input will be crucial to the maintenance of public trust.

Efficiency of Mobile Network Operators from Data Traffic Perspective Using Data Envelopment Analysis

The use of mobile data is increasing during the pandemic COVID-19 due to social distancing. Many daily activities depend on internet activity. Mobile network operators as internet service providers are required to be able to meet increasingly high data needs by providing a reliable network. This research aimed to determine, analyze, and compare the efficiency of mobile network operators before and during the pandemic COVID-19 using Data Envelopment Analysis (DEA). The variables used to measure efficiency in this research were company operational variables. The input variable was the number of BTS, while the output variables include the number of subscribers and data traffic.The data used in this research came from company annual reports from 2015 to 2020. This result shows that during the COVID-19 pandemic, the average efficiency value achieved by mobile network operators was higher than the previous years. Correlations between each variable with efficiency on each company are different. Telkomsel efficiency is mostly affected by the number of BTS, Indosat Ooredoo efficiency is by the number of subscribers, and XL Axiata efficiency is by data traffic.

Modelling Representative Population Mobility for COVID-19 Spatial Transmission in South Africa.

The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data sources convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices at a variety of spatial resolutions and further compares the results through hierarchical clustering. We consider four methods for constructing spatial weight matrices representing mobility between spatial units, taking into account distance between spatial units as well as spatial covariates. This provides insight for the user into which data provides what type of information and in what situations a particular data source is most useful.

Mobility data to aid assessment of human responses to extreme environmental conditions

Mobility data to aid assessment of human responses to extreme environmental conditions

Perception of nursing students towards online learning during COVID-19 pandemic

Introduction: During COVID-19 pandemic, many academic institutions in Nepal and the world turned to online learning. The present study aimed to explore nursing students’ perception towards online learning. 
 Method: A cross-sectional analytical study was conducted to identify the perception of nursing students towards online learning in School of Nursing and Midwifery, Patan Academy of Health Sciences (PAHS), Lalitpur, Nepal during October 2020. A total enumerative sampling technique was used to collect data. Ethical approval was taken from the Institutional Review Committee (IRC) of PAHS. Data were collected using a self-developed structured questionnaire via Google form. The collected data were entered into SPSS version 16. Descriptive statistics (frequency, percentage, mean and standard division) and Chi-square test was used to analyze the data.
 Result: Out of 348 nursing students, it was found that 186(53.4%) of the nursing students had positive perception and 162(46.6%) of the students had negative perceptions towards online learning during COVID-19 pandemic. The perception was associated with the academic year, access to the internet, need for use of mobile data, and academic level.
 Conclusion: The overall perception of nursing students is not satisfactory towards online learning. Academic institution should plan the online learning giving the priorities on the factors associated with negative perception so that students can learn effectively through virtual.

Big data analytics and COVID-19: investigating the relationship between government policies and cases in Poland, Turkey and South Korea.

We used big data analytics for exploring the relationship between government response policies, human mobility trends and numbers of coronavirus disease 2019 (COVID-19) cases comparatively in Poland, Turkey and South Korea. We collected daily mobility data of retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residential areas. For quantifying the actions taken by governments and making a fairness comparison between these countries, we used stringency index values measured with the ‘Oxford COVID-19 government response tracker’. For the Turkey case, we also developed a model by implementing the multilayer perceptron algorithm for predicting numbers of cases based on the mobility data. We finally created scenarios based on the descriptive statistics of the mobility data of these countries and generated predictions on the numbers of cases by using the developed model. Based on the descriptive analysis, we pointed out that while Poland and Turkey had relatively closer values and distributions on the study variables, South Korea had more stable data compared to Poland and Turkey. We mainly showed that while the stringency index of the current day was associated with mobility data of the same day, the current day’s mobility was associated with the numbers of cases 1 month later. By obtaining 89.3% prediction accuracy, we also concluded that the use of mobility data and implementation of big data analytics technique may enable decision-making in managing uncertain environments created by outbreak situations. We finally proposed implications for policymakers for deciding on the targeted levels of mobility to maintain numbers of cases in a manageable range based on the results of created scenarios.

Effects of COVID-19 government travel restrictions on mobility in a rural border area of Northern Thailand: A mobile phone tracking study.

BackgroundThailand is among the top five countries with effective COVID-19 transmission control. This study examines how news of presence of COVID-19 in Thailand, as well as varying levels of government restriction on movement, affected human mobility in a rural Thai population along the border with Myanmar.MethodsThis study makes use of mobility data collected using a smartphone app. Between November 2019 and June 2020, four major events concerning information dissemination or government intervention give rise to five time intervals of analysis. Radius of gyration is used to analyze movement in each interval, and movement during government-imposed curfew. Human mobility network visualization is used to identify changes in travel patterns between main geographic locations of activity. Cross-border mobility analysis highlights potential for intervillage and intercountry disease transmission.ResultsInter-village and cross-border movement was common in the pre-COVID-19 period. Radius of gyration and cross-border trips decreased following news of the first imported cases. During the government lockdown period, radius of gyration was reduced by more than 90% and cross-border movement was mostly limited to short-distance trips. Human mobility was nearly back to normal after relaxation of the lockdown.ConclusionsThis study provides insight into the impact of the government lockdown policy on an area with extremely low socio-economic status, poor healthcare resources, and highly active cross-border movement. The lockdown had a great impact on reducing individual mobility, including cross-border movement. The quick return to normal mobility after relaxation of the lockdown implies that close monitoring of disease should be continued to prevent a second wave.

The use of anonymized and aggregated telecom mobility data by a public health agency during the COVID-19 pandemic: Learnings from both the operator and agency perspective

Abstract The COVID-19 pandemic and associated measures implemented have rapidly changed how people move about and behave in society. Utilizing data on people’s mobility could provide unique and valuable insights to governments and institutions to better manage the crisis. These entities, however, have not traditionally had access to, nor the experience of applying, continuous anonymized and aggregated data on people mobility. This article aims to show how the Public Health Agency in Sweden successfully collaborated with a Nordic Telecoms operator to make use of such data during the COVID-19 pandemic. Specifically, it investigates how the collaboration started, approaches used to go from data to insight, outcomes and impact, and lessons learned on both sides. Telia, the largest telecom operator in the Nordics, had an existing product commercially available that provided anonymized and aggregated insights about people’s movement. Several challenges existed within Telia as it was the first time worldwide a collaboration with a Public Health Agency would take place and social benefits had to be weighed against commercial and reputational risks. The hypothesis at the beginning of the pandemic was that the solution could be adapted to fit the needs of policymakers and the internal challenges could be overcome, while providing a meaningful contribution to the fight against the virus. The results show that it is possible to both form a mutually beneficial collaboration between a telecom operator and a public institution, and to make use of mobility data in evidence-based policymaking without compromising applicable personal data protection laws.

Alzheimer's Disease stage identification using deep learning models.

The aim of this research is to identify the stage of Alzheimer's Disease (AD) patients through the use of mobility data and deep learning models. This process facilitates the monitoring of the disease and allows actions to be taken in order to provide the optimal treatment and the prevention of complications. We employed data from 35 patients with AD collected by smartphones for a week in a daycare center. The data sequences of each patient recorded the accelerometer changes while daily activities were performed and they were labeled with the stage of the disease (early, middle or late). Our methodology processes these time series and uses a Convolutional Neural Network (CNN) model to recognize the patterns that identify each stage. The CNN-based method achieved a 90.91% accuracy and an F1-score of 0.897, greatly improving the results obtained by the traditional feature-based classifiers. In our research, we show that mobility data can be a valuable resource for the treatment of patients with AD as well as to study the progress of the disease. The use of our CNN-based method improves the accuracy of the identification of AD stages in comparison to common supervised learning models.

Identifying tourist experiences in Maui, Hawaii using mobile location data

Spatial big data are revolutionizing the way that information is collected, stored and leveraged for decision-making. The advancements in spatial big data and their analytics have allowed for more granular-level information to be collected and analyzed on the spatiotemporal movements of people. However, critical reviews of spatial big data have found that many theoretical and quantitative misunderstandings exist which causes confusion for practitioners during applied practice. The level of information detail that can be confidently extracted from mobile data are not yet determined. In response, this paper strives to demonstrate the complexity of information that is available within mobile data through two inter-related objectives: (i) to explore the use of mobile data to group tourists based on their travel experience in Maui, Hawaii, and, (ii) to identify the linkage between accommodation types and travel experiences. Geofences were created around key points of interests and a k-means clustering was done in order to group travelers based on their travel experiences and time spent within these points of interest. It was found that tourist choices in accommodation resulted in distinct travel behaviors. This work provides a new use case of k-means clustering and mobile location data for the purpose of creating pseudo-loyalty data and consumer segments for points of interest that could not otherwise have data collected.

Exploring strengths and weaknesses of mobility inference from mobile phone data vs. travel surveys

Origin–destination (OD) matrices serve as a basis for travel demand modelling. Traditionally, they are derived from travel surveys that collect detailed trip information but with several shortcomings. Mobile phones are regarded as a useful source of information on people’s daily mobility. This work explores the use of mobile data in the context of mobility studies by comparing matrices derived from both types of sources over the same region. The results reveal many common features in the trip information. Moreover, although the use of mobile technology may raise questions for short trips, the huge representativeness of this technology captures the mobility in OD connections extensively regardless the area. This is crucial for non-populated areas (e.g. industrial parks or educational campuses), which constitute important mobility hotspots. Based on these findings, an applicable data fusion approach to obtain the optimum accuracy from these heterogeneous sources is presented and applied.

Mobility Trends Reports Revealed "Self-isolation Fatigue" in Japan: Use of Mobility Data for Coronavirus Disease Control.

Mobility Trends Reports Revealed "Self-isolation Fatigue" in Japan: Use of Mobility Data for Coronavirus Disease Control.

Prevalence of Internet Addiction Among College Students

The worldwide system of computer networks is known as Internet. Variety of Information and Communication facilities are provided by global computer networks. Internet is the most costeffective communications method in the world. Internet is mostly used for E-mails social networking online movies instant messaging online shopping gaming data or file sharing etc. To facilitate communication and research activities internet was originally designed. The use of internet in recent years has the dramatic increase in the usage of internet and leading to internet addiction1 .Objectives The study aimed to assess the prevalence of Internet addiction among college students and to find the association between internet addiction scores and the selected demographical variables.Material and methods Data were collected by using standardized 20 items internet addiction scale. The demographic variables analyzed in the study were age gender religion type of family family income and source of internet use. The samples for the study were selected by using the convenient sampling technique. The data obtained were tabulated and analyzed in terms of objectives of the study by using descriptive and inferential statistics.Results The result of the study showed that the majority of the students participated in the study belonged to the age group of 18-20 years i.e. 68 and the minimum i.e. 024 belonged to the age group above 22 years. The majority of respondents were female i.e. 4386 among which 2958 respondents were from christia religion. The majority of the respondentrsquos family income was above 60000month and majority of them were from joint family i.e. 4386. The respondents were asked regarding the source of internet use where majority f the students reported use of mobile data i.e. 2754 where remaining 2346 reported use of WiFi as source of internet. The findings revealed that 34 students i.e. 74 were under mild internet addiction category 12 students i.e. 24 were moderately addicted and 1 student i.e. 2 were severely addicted to the internet use. The findings also revealed that there is no association between the internet addiction scores and demographical variables.

A Primer on MIMO Detection Algorithms for 5G Communication Network

In the recent past, demand for large use of mobile data has increased tremendously due to the proliferation of hand held devices which allows millions of people access to video streaming, VOIP and other internet related usage including machine to machine (M2M) communication. One of the anticipated attribute of the fifth generation (5G) network is its ability to meet this humongous data rate requirement in the order of 10s Gbps. A particular promising technology that can provide this desired performance if used in the 5G network is the massive multiple-input, multiple-output otherwise called the Massive MIMO. The use of massive MIMO in 5G cellular network where data rate of the order of 100x that of the current state of the art LTE-A is expected and high spectral efficiency with very low latency and low energy consumption, present a challenge in symbol/signal detection and parameter estimation as a result of the high dimension of the antenna elements required. One of the major bottlenecks in achieving the benefits of such massive MIMO systems is the problem of achieving detectors with realistic low complexity for such huge systems. We therefore review various MIMO detection algorithms aiming for low computational complexity with high performance and that scales well with increase in transmit antennas suitable for massive MIMO systems. We evaluate detection algorithms for small and medium dimension MIMO as well as a combination of some of them in order to achieve our above objectives. The review shows no single one detector can be said to be ideal for massive MIMO and that the low complexity with optimal performance detector suitable for 5G massive MIMO system is still an open research issue. A comprehensive review of such detection algorithms for massive MIMO was not presented in the literature which was achieved in this work.

Understanding the impact of water management on people for investment in sustainable infrastructure in the UK

This study explored whether digital footprinting through the use of mobile data could be used to improve the understanding of water and environmental events on people, to enhance the evidence base for investment in sustainable infrastructure. The study focused on Margate, UK, where an integrated stakeholder surface water management plan was being developed to support the town’s regeneration and urban growth aspirations. The objectives were to assess (a) the usefulness of mobile data in infrastructure studies and (b) the impacts of a recent water pollution and beach closure event on visitor footfall to and from Margate. It was found that digital footprinting techniques provided substantial new insight into human dynamics (spatially, temporally, and demographically) with visitor footfall to and from Margate strongly driven by weather, predominately from the local area and over 65 years of age, but with 16% of visitors from London. With further work (longer time periods, further population data, and statistical analysis) digital footprinting techniques could give Margate’s regeneration strategy an area to focus on in diversifying its economy and reliance on tourism, as well as moreover, a basis for future infrastructure appraisal and evaluation in the UK (potentially in real time). The development of the latter will pose many of the real privacy challenges that previous study has highlighted and is likely to be pursued due to the current financial climate needing to deliver better services for lower cost.

Learning Mobile Communications Standards through Flexible Software Defined Radio Base Stations

Mobile communications are today widespread and contribute to the development of our society. Every day new devices include some means of wireless transmission, which is becoming ubiquitous with the Internet of Things. These systems are standardized by international organizations such as the IEEE, 3GPP, and ETSI, among others. Even though knowledge of wireless standards is key to the understanding of these systems, wireless communications are quite often taught in engineering degrees in a traditional way, without much emphasis on the standardization. Moreover, strong focus is often placed on the theoretical performance analysis rather than on practical implementation aspects. In contrast, most of the current applications make extensive use of mobile data, and the global users' satisfaction is highly correlated with the mobile data throughput. Thus, modern wireless engineers need to have deep insight on the standards that define the mobile transmission systems, and this knowledge is not acquired following the traditional theoretical teaching schemes. In this article, a new learning approach is described. This novel paradigm is based on a new flexible hardware/software platform (FRAMED-SOFT), which is also detailed. Although the article is focused on two wireless standards, GSM and UMTS, the work discussed in this article can easily be extended to other standards of interest, such as LTE and beyond, WiFi, and WiMAX.