Mobile Data Analysis Research Articles

Mobile Apps for Vaccination Services: Content Analysis and Quality Assessment.

Vaccination services are increasingly in demand by the public, and mobile apps are an effective tool to meet that demand. However, the characteristics and quality of these apps are unknown. Commonly used vaccination service apps on the market were surveyed with regard to quality, service content, and user experience to evaluate and guide users. The Qimai Data mobile app data analytics platform was used to search for common vaccination service apps by keyword, and the WeChat and Alipay platforms were searched for apps. The apps included in the study were independently evaluated by two reviewers using the Mobile Application Rating Scale, and the service content and user experience of the apps were analyzed. The intragroup correlation coefficient between raters was used to measure interrater reliability. In the app stores of the four major Android platforms and the iOS app store, 1092 and 207 apps were found, respectively; 189 WeChat applets and 30 Alipay applets were also found. A total of 29 apps was ultimately included in this study according to the inclusion criteria, including 21 independent apps, 4 WeChat applets, and 4 Alipay applets. Significant differences were found between independent apps and applets in terms of the quality score (t449.57=-5.301; P<.001) and the subjective quality score (z=-4.753; P<.001). No significant differences were found between iOS and Android platforms in terms of the quality score (t1404=-2.55; P=.80) and the subjective quality score (z=-0.137; P=.89). There was good intragroup consistency among the raters. In this study, independent apps and nonindependent apps that rely on social and payment platforms for implementation were included in the vaccination services category. The overall quality of these apps was acceptable. Nonindependent running apps were found to have slightly lower scores and showed room for improvement, and scores for the participatory apps were found to be generally low overall.

From Barista to Bytes: How Starbucks Brewed a Digital Revolution

This study examines Starbucks' digital transformation journey, analyzing its strategies, implementation, and impact on performance and competitive positioning. Using a qualitative case study approach, the article explores how Starbucks leveraged an interconnected ecosystem of mobile applications, artificial intelligence, cloud computing, and data analytics to enhance customer engagement and optimize operations. The research investigates how these technologies synergistically contribute to Starbucks' "Digital Flywheel" framework, driving personalization, streamlining operations, and creating a seamless omnichannel experience. Drawing on company reports, academic literature, and industry analyses, this study provides a comprehensive view of Starbucks' digital initiatives and their collective impact. Findings reveal that Starbucks' success stems from a holistic approach to digital transformation, encompassing technological implementation, organizational restructuring, and cultural change. The study critically examines the effectiveness of the "Digital Flywheel" framework, comparing it with similar models in digital transformation literature. While highlighting the significant impact of these initiatives on Starbucks' financial performance, customer engagement, and operational efficiency, the research also explores challenges in data privacy, technology investment costs, and potential risks associated with digital transformation. The study provides a nuanced analysis of how Starbucks navigated these challenges, offering valuable insights for practitioners. This research contributes to the growing literature on digital transformation in retail, offering a balanced perspective on both the opportunities and challenges of digital innovation. By examining Starbucks' journey, the study critically assesses how traditional retailers can adapt to the digital age, while acknowledging the variability in outcomes across different sectors.

Diabetes Disparities: Addressing Health Inequities and Improving Outcomes in Underserved Populations

Diabetes mellitus, characterized by high blood sugar levels due to insulin deficiency or resistance, is a global public health crisis with significant disparities in prevalence, management, and outcomes, particularly among underserved populations such as racial and ethnic minorities, low-income groups, and rural residents. These populations faced higher rates of diabetes and complications due to social determinants of health, including economic instability, inadequate education, and limited access to healthcare. Barriers such as lack of insurance, transportation challenges, and cultural and linguistic differences further hindered effective diabetes management. Lifestyle and behavioral factors, including diet, physical activity, medication adherence, and mental health, play critical roles in diabetes outcomes. Policy and system-level interventions, such as expanding healthcare coverage, improving access to care, and addressing social determinants of health, are essential to reducing disparities. Technological innovations like telehealth, mobile health applications, wearable devices, and data analytics offered significant potential to enhance diabetes management in underserved populations. This review was conducted using a comprehensive analysis of current literature and existing studies to provide insights into the multifaceted nature of diabetes disparities and to highlight successful interventions and necessary policy changes to improve health outcomes for underserved populations. Addressing these inequities required a multifaceted approach, including community-based interventions, policy reforms, and technological advancements to ensure equitable diabetes care for all. Keywords: Diabetes disparities, Underserved populations, social determinants of health, Healthcare access, Technological innovations.

Developing a system for the real-time collection and analysis of mobile vehicle emission data

In this study, an integrated system for remotely monitoring air pollution data was developed. This system combines vehicle exhaust emission data with driving information. It comprises an exhaust extraction device (EED), an improved On-Board Diagnostic II (OBDII) device, a mobile app, and a backend server database. The EED detects exhaust gases such as NOx, COx, and PM2.5 and transmits this information to the OBDII device through 2.4G communication. The OBDII device gathers vehicle driving information through a CAN 2.0 interface, integrates this information with vehicle exhaust emission data, and transmits the integrated information to the app through Bluetooth. The app forwards the integrated data to the backend database through 4G/5G communication for storage. Overall, this system provides a platform for the comprehensive collection, analysis, and application of big data pertaining to vehicle pollution sources and driving behavior. Analysis of actual driving experiments involving three vehicles revealed a correlation of over 80 % between vehicles’ revolutions per minute and CO2 and PM2.5 emissions, indicating that driving behaviors affect vehicle exhaust emissions. In summary, the proposed system not only stores relevant driving information in real time but also provides a platform for the future analysis of driving behavior and vehicle exhaust emissions.

Systematic review protocol of digital divides in the medical sciences area

Introduction: the development of digital technologies in the area of medical sciences has progressively advanced in recent years, initially conceptualized as electronic health, whose characteristics were fundamentally informative, its main tool being the internet and extending to other services such as mobile health and telehealth of great importance for medicine. Objective: the present systematic review aims to determine the main digital technologies in the health area, developed during the years 2020 to 2022 and their applicability in the different fields of medicine.Method: the methodology used was based on the review of original research articles on four categories of study: electronic medical records, telemedicine, mobile health and data analysis, obtained from the Scopus, Web of Science, ProQuest and EBSCO databases. Thirty articles were selected, respecting the screening and eligibility criteria considered in the PRISMA protocol. Results: the results of the review showed the remarkable development in the application of electronic medical records and telemedicine in the different areas of health, constituting between them 60 % of the publications; mobile applications and data analysis were the ones with the lowest results, but with the greatest technological innovation. Conclusion: we conclude the importance of the applicability of new technologies developed during the health emergency in different health areas

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.

RADAR-Pipeline: Scalable Feature Generation for Mobile Health Data

Introduction &amp; BackgroundRADAR-Pipeline is an open-source Python framework designed to simplify and enhance mobile health data analysis. It has been designed to efficiently read and process the large amount of data generated through the RADAR-Base platform. RADAR-base is a scalable, real-time streaming and analytics open-source platform to facilitate research access and customisation requirements. Studies using the Radar-base platform have collected fine-grained longitudinal data from wearables and phones. The data can potentially create multitudes of digital biomarkers, which can be used to inform us greatly about the disease condition. Due to the sheer size of the data, it can be difficult for researchers to read and process those data -- a common task is identifying useful features and common data processing/analysis steps previously used by the community. Up to now, these have been hand-crafted by individual data scientists, often lacking the capability to be easily reused by the community without author-specific knowledge. Furthermore, generating variables based on already established research on a larger scale can be challenging and could hinder replication. Hence, we have designed RADAR-Pipeline to help researchers overcome these challenges. It empowers them to create and share their data analysis and visualisation pipelines, fostering collaboration and knowledge sharing within the research community. Objectives &amp; ApproachThe primary objective of RADAR-Pipeline is to offer researchers a user-friendly and powerful platform to develop and share their research. Researchers can build reusable analysis and visualisation pipelines to ensure consistent and reliable results. It simplifies big data analysis by leveraging Apache Spark to handle large and complex mobile health datasets efficiently. Researchers can also save time and effort by reusing and extending existing pipelines built by others. Finally, the RADAR-Pipeline promotes collaboration and recognition by allowing researchers to share their work through the RADAR-base Analytics Catalogue, making their pipelines citable and accessible to the wider research community. Whilst Radar-pipeline has been designed to read data from Radar-base, it can also be used to read data from any dataset which uses Hadoop Distributed File System (HDFS) file system namespace. Relevance to Digital FootprintsMobile health data is rich and valuable for understanding human behaviour and health. RADAR-Pipeline addresses the challenges associated with analysing large and complex mobile health datasets, enabling researchers to extract valuable insights that can be used to (1) Improve public health: By enabling efficient analysis of large-scale mobile health data, RADAR-Pipeline can contribute to research efforts aimed at improving population health outcomes and developing effective interventions; (2) Personalised healthcare: By facilitating the extraction of individual-level features from mobile health data, RADAR-Pipeline can seamlessly be integrated with Kafka data streams and machine learning pipelines to process the data in real-time, which can then be utilised to create more effective and targeted real-time interventions. (3) Promote reproducible research: The framework's emphasis on transparency and reproducibility in research aligns with the conference's focus on the responsible use of digital mobile health data. Conclusions &amp; ImplicationsRADAR-Pipeline is a valuable tool for researchers, offering them the means to harness the potential of mobile health data. By adopting this framework, researchers can achieve efficient and scalable data analysis, thereby streamlining the extracting insights from digital footprints. This efficiency enables researchers to delve deeper into the data and uncover valuable patterns and trends. Furthermore, RADAR-Pipeline promotes collaboration and knowledge sharing within the research community. By providing a standardised framework for data analysis, RADAR-Pipeline facilitates collaboration among researchers, leading to the sharing of best practices and the dissemination of knowledge.

Non-linear impact of the built environment on metro commuter flows before and after the COVID-19 outbreak: A case study in Guangzhou

The built environment is a crucial factor influencing subway commuting. However, the extent to which the built environment affects metro travel during the COVID-19 pandemic remains under explored. In particular, people's travel patterns and destinations might have changed during the pandemic, influenced by the built environment. The degree to which these influences differ from those in normal circumstances has scarcely been studied. This study investigates the non-linear effects of the built environment on metro commuters within an 800-m radius of subway stations. Analysis of mobile signal data from Guangzhou Metro Line 1 showed that the importance of the built environment was 81.69% and 70.06% before and after the outbreak, respectively. Based on the threshold effects of the built environment on subway passenger flows before and after the pandemic, this study determined the immediate needs of commuters for the built environment after the outbreak. This information can guide planning strategies around subway stations facing public health emergencies. This study highlights the non-linear impact of the built environment on subway passenger flows after an outbreak. Thus, the number of parking lots, the proportion of medical facilities and the distance to the central business district are urgent demands for subway passengers after the outbreak.

THE ENVIROMENTAL AND SOCIAL IMPACT OF CARPOOLING

Abstract: Car pooling System is an automated system which reduces the misery of travelers and makes them find cars in short period of time. Car pooling is an application of finding car in which drivers who are traveling to work alone can ask for fellow passengers through our application. For those who use public-transport system to go to work daily can use this application to find drivers who are traveling to the same destination in a short path. It provides with a simple riding platform between the car owner and car user. This project enables users to access mobility assets own by others exactly when they need. I t shows a medium for available cars to pick up them on the interest of car owner with time and capacity. This project aims at creation of a Car-pooling System. This paper delves into the design and implementation of a novel carpooling project aimed at addressing the challenges of urban transportation sustainability. With burgeoning urban populations, congestion, and environmental concerns, the need for innovative solutions is paramount. Carpooling presents a viable avenue to alleviate these issues by fostering shared mobility, reducing traffic congestion, and cutting down carbon emissions. The project employs a multi-faceted approach, leveraging advancements in mobile technology, data analytics, and user incentives to optimize the carpooling experience. Key components include a user-friendly mobile application for seamless ride matching, dynamic routing algorithms to optimize trip efficiency, and incentive structures to encourage participation and retention. Through a combination of quantitative analysis and qualitative feedback, the efficacy of the carpooling project is evaluated. Metrics such as reduction in vehicle miles traveled, greenhouse gas emissions, and user satisfaction levels are assessed to gauge its impact on urban transportation sustainability. The findings of this research contribute to the growing body of knowledge on sustainable transportation solutions and provide insights for policymakers, urban planners, and transportation stakeholders seeking to promote carpooling initiatives. Ultimately, the project aims to foster a culture of shared mobility and pave the way for more efficient and sustainable urban transportation systems. IndexTerms – HTML, CSS, Javascript. Bootsrap, MySQL, Python.

Demand-Responsive Transport for Urban Mobility: Integrating Mobile Data Analytics to Enhance Public Transportation Systems

Demand-Responsive Transport for Urban Mobility: Integrating Mobile Data Analytics to Enhance Public Transportation Systems

FINCHILD: The Missing Child Finder Application

"FINCHILD" is an innovative and user-friendly mobile application designed to address the critical issue of missing children by leveraging real-time tracking technology. The application aims to empower parents, guardians, and law enforcement agencies with an effective tool to locate and recover missing children swiftly and efficiently. In a world where child safety is paramount, FINCHILD fills a significant gap by harnessing the power of real-time location tracking, geofencing, and community engagement. The application provides a comprehensive platform that integrates the latest advancements in mobile technology and data analytics to ensure a rapid and coordinated response in the event a child goes missing. The FINCHILD application is a cutting-edge real-time solution designed to address the critical issue of missing children. Leveraging advanced technologies, including GPS tracking, facial recognition, and real-time data analytics, FINCHILD aims to significantly reduce the response time and enhance the efficiency of locating missing children. Key Words: GPS Tracking, Facial Recognition, Real-Time Alerts, Community Engagement.

Implementation on Large-Scale Mobile Fitness App Usage Analysis for Smart Health

Understanding the determinants behind the exercise habits of urban residents, encompassing aerobic activities and training regimens, is of paramount importance for informed public policy formulation and urban planning. The utilization of mobile fitness app data, which characterizes exercise behaviors concerning time and location, offers a unique avenue for unveiling the key factors influencing physical activity. In this study, we conducted a comprehensive analysis of mobile fitness app data, collecting information from over 14,000 cellular towers and studying the exercise patterns of 4,000 users. Our rigorous investigation reveals that temporal elements, such as the day of the week and the time of day, wield significant influence over human exercise preferences. For instance, data shows that on weekdays, there's a 20% increase in exercise app usage during the evening hours (6 PM - 9 PM), while weekends see a 15% spike in early morning activities (6 AM - 8 AM). Geographical location also emerges as a prominent influencer. Our analysis indicates that individuals residing closer to parks or fitness centers exhibit a 25% higher likelihood of engaging in outdoor workouts. Importantly, personal income levels have surfaced as a fundamental factor significantly shaping exercise habits. Data suggests that individuals with higher incomes allocate, on average, 10% more of their time to structured training regimens. This research contributes essential insights for urban policy development and city planning, aimed at fostering a healthier and more physically active urban populace. Key Words: Tracking nutritional intake, Analyzing meals, Visualizing nutrition data, Daily nutrition overview, Calculating BMI, Image recognition for nutrition, Menu nutritional analysis, Tracking steps, Monitoring physical activity, Providing workout plans, Home workout routines, Gym workout routines, Planning a balanced diet, Diet plans based on BMI, Fitness and wellness application.

ANALYSING STUDENTS’ MOBILITY AT HIGHER EDUCATION INSTITUTIONS: CASE OF UKRAINIAN UNIVERSITY DURING THE WAR

Higher Education Institutions (HEIs) often overlook the importance of systematic data collection and analysis. This oversight can obscure critical trends and decision-making insights, particularly student mobility. HEIs management may not detect the outflow of students related to academic mobility and therefore will not make timely managerial decisions. This article aims to provide guidance to university management on how data collection and analysis can improve informed decision-making, focusing on student mobility, especially in times of severe disruptions, like military conflicts. To reach this goal, desk research of previous literature was conducted to identify risks and challenges related to students’ mobility and the previous experience to address them. Secondary data analysis of student outflow at the Faculty of Economics of the National University “Kyiv-Mohyla Academy” (NaUKMA) from 2015–2022, alongside a student survey provided insights into the students’ academic mobility process dynamics. The findings demonstrate a significant outflow of students from educational programs, highlighting the opportunities and risks associated with academic mobility. Analysis of this data reveals critical insights into student motivation, which can significantly influence their decisions and behavior. Students’ mobility data analysis will instantly point out the problem to HEI management, making it possible to prevent the consequences.

Mobile Application Software as Guidance and Counselling Tool for Students of Federal College of Education, Gidan Madi

This project aims to develop a Mobile Application Software as a Guidance and Counselling Tool for Students of Federal College of Education Gidanmadi, Sokoto State, Nigeria. The goal is to provide students with a convenient, accessible, and effective way to access mental health services and support. The project involves a needs assessment, literature review, conceptual framework development, user interface design, mobile application development, usability testing, data analysis, design changes and evaluation. The methodology will be tailored to the specific needs and goals of the research, and may involve different stages and methods depending on the context and resources available. The expected results of developing a mobile application software as a guidance and counselling tool for students include increased accessibility, improved student well-being, enhanced counselor efficiency, greater student engagement, increased student retention, and improved student satisfaction. At the end of the research the students can be able to use the mobile application software for easy access to counselling and guidance services that can help them cope with various challenges they face in their academic and personal life. Guidance and counseling, Information and communication technology, Mobile application

Spatial and Temporal Exploratory Factor Analysis of Urban Mobile Data Traffic

Spatial and Temporal Exploratory Factor Analysis of Urban Mobile Data Traffic

Epistemic domination by data extraction: questioning the use of biometrics and mobile phone data analysis in asylum procedures

ABSTRACT In a growing number of destination countries state authorities have started to use various digital devices such as analysis of data captured from mobile phones to verify asylum seekers’ claimed country of origin. This move has prompted some critics to claim that asylum decision-making is increasingly delegated to machines. Based on fieldwork at a reception centre in Germany, this paper mobilises insights from science and technology studies (STS) to develop a framework that allows for more nuanced analyses and modes of critiques of the digitisation of asylum procedures. Rather than thinking human and non-human forms of agency as external to one another in order to juxtapose them in a zero-sum game, I comprehend the introduction of digital technologies as a reconfiguration of existing human-machine configurations. This conception highlights how the use digital technologies enables caseworkers to retain their position as an epistemic authority in asylum decision-making by assembling clues about asylum seekers’ country of origin generated by digital technologies into hard juridical evidence. Subsequently, I develop an alternative critique that focuses on epistemic implications of the digitisation of asylum procedures. I identify a particular version of data colonialism that enables epistemic domination by means of data extraction.

MMPL: Multi-Objective Multi-Party Learning via Diverse Steps

Multi-party learning is a specific framework of distributed learning which is widely exploited in the medical system and mobile data analysis. By installing a central server, individual devices update the model parameters instead of sharing sensitive data. This method can protect data privacy with the highest measures. But during the communication round, it is difficult to balance the model performance and the computational costs. In this article, we propose a novel framework for multiparty learning, named Multi-objective Multi-Party Learning via Diverse Steps (MMPL). We regard multi-party learning as a multi-objective problem and employ evolutionary optimization for analysis. Within the design of our framework, we try to use the neural network as a bridge to connect evolutionary optimization with multi-party learning. During the study, we propose a novel space-searching strategy for complex encoding problems. An individual wised value is installed on each private device for a differentiated treatment. Experimental results between our method and the comparative algorithms show that the proposed algorithm can achieve better performance while partly ameliorating the time-consuming problem.

Economic Philosophy as the Existence of the 5.0 Era Development in Sharia and Conventional Banking

With digital transformation and progress in technology, the 5.0 era has a huge impact on the industry of Islamic and conventional banking. Understanding the existence of Islamic and conventional banking in the 5.0 era relies heavily on economic philosophy. This study aims to understand the role of economic philosophy in maintaining and developing Islamic and conventional banking in the 5.0 era. This study used a descriptive analysis approach, and the data were collected from literature and other relevant sources. Conventional banking, on the other hand, relies on the philosophy of economy neoclassical, which focuses on the development products and services. Islamic and conventional banking are influenced by progress technology and digital transformation in the 5.0 era. Application mobile banking, digital payments, data analytics, artificial intelligence, and blockchain technology are several examples of very important technology or facing challenges and exploiting opportunities in this era. However, in the era of 5.0, economic philosophy is also challenging to apply in Islamic and conventional banking. Conventional banking can notice the difference between the social and financial aspects, but Sharia banking must consider current principles to integrate technology. Sharia and conventional banking need to keep going and develop relevant digital products and services, strengthen regulation and supervision, and guarantee security and privacy of customer’s information. Research results show that good Sharia banking as well conventional are based on economic philosophy. Islamic banking relies on philosophy of Islamic economics, which focuses on principles like prohibition of usury, justice, and sustainable environment.&#x0D; Keywords: philosophy, economics, Sharia, conventional

Simplifying COVISimplifying COVID-19 Data Analytics for Efficient Pandemic Management: A Novel ApproachD-19 Data Analytics for Efficient Pandemic Management: A Novel Approach

This study presents a streamlined approach to pandemic management by simplifying COVID-19 data analytics. It focuses on the significant role of mobility patterns in forecasting case trajectories. Utilizing open mobility data from Google and Apple, a novel predictive model is proposed that aids health authorities in scenario projection and case monitoring. This model facilitates informed decision-making with minimal economic impact during future outbreaks. Key findings highlight the profound link between mobility changes and COVID-19 case trends, emphasizing the necessity of integrating mobility data into predictive models. The model, employing linear and polynomial regression analyses and incorporating the basic reproduction number, R0, forecasts can be extended up to 90 days. The study acknowledges limitations, particularly the reliance on mobility data that does not fully encompass all variables affecting virus transmission. Moreover, it explores the mental health implications of mobility restrictions, suggesting a broader impact of pandemic management strategies. The proposed model is a practical tool for managing pandemics through mobility data analysis, underscoring the need for comprehensive studies on the wider effects of mobility changes to guide public health policies

Accuracy analysis of mobile signaling data in measuring travel indices: Based on the comparison with household travel survey

Accuracy analysis of mobile signaling data in measuring travel indices: Based on the comparison with household travel survey