The ubiquity of mobile devices is increasingly integrated into our daily routines. Each successive iteration of these devices introduces novel functionalities, enhancing their accessibility and affordability, while also expanding the availability of new applications. Mobile phone technology has undergone significant advancements, resulting in more sophisticated devices and operating systems. Consequently, a diverse array of mobile applications tailored for smartphone operating systems, provided by various mobile vendors, has emerged. A mobile application is defined as a software application developed specifically for use on small, wireless computing devices, such as smartphones and tablets, rather than desktop or laptop computers. Educators and researchers should acquaint themselves with fundamental terminologies pertinent to mobile applications, as they underpin comprehension in this field. These terminologies include location-based services, indoor positioning systems, call detail records, push notifications, mobile device sensors, in-app data capture, mobile data analytics, crowdsourcing data collection, and mobile navigation systems. Although technological progress has been made regarding mobile devices equipped with computing power, leading to a shift from desktop computers, many limitations and challenges remain, specifically in the concern of data governance and privacy issues.
Wang, S. and Xiao, H. (2024). Mobile Applications and Technologies. The Geographic Information Science & Technology Body of Knowledge (2024 Version), John P. Wilson (ed.). DOI: 10.22224/gistbok/2024.1.5.
Location-Based Service (LBS) represents a dynamic and versatile technology that leverages the geographical position of a user to provide tailored information, services, or functionality. The mechanism underlying LBS primarily relies on global positioning system (GPS) technology, supplemented by cellular, Wi-Fi, or Bluetooth signals for increased accuracy in urban environments. Through triangulation or trilateration methods, LBS determines a user's location, often with remarkable precision, facilitating a range of applications across diverse sectors. represent a dynamic and versatile technology that leverages the geographical position of a user to provide tailored information, services, or functionality (Usman et al., 2018). LBS employs a combination of hardware and software components. Hardware includes GPS receivers or sensors embedded in mobile devices, while software encompasses algorithms for location determination, map rendering, and data processing. LBS mobile apps collect a wealth of data from users who "check in" at various locations, providing valuable insights into consumer behavior and trends (Tu et al., 2017). This data includes informatoin such as the user's current location, the time of the check-in, and the specific place or business they are visiting. By analyzing this data, businesses can can a better understanding of customer preferences, foot traffic patterns, and the popularity of certain locations. This information can be used to tailor marketing strategies, optimize store locations, and enhance customer engagement.
Indoor Positioning Systems (IPSs) are technologies that enable locating people or objects inside buildings using radio waves, magnetic fields, acoustic signals, or other sensory information collected by mobile devices. IPSs represent an innovative technology designed to track and locate objects or individuals within enclosed spaces where GPS signals are inaccessible or unreliable. The mechanism behind IPSs involve a combination of different techniques such as Wi-Fi, Bluetooth Low Energy, Radio Frequency Identification, and magnetic positioning (Liu et al., 2007). These techniques utilize signals emitted by fixed reference points or beacons strategically placed throughout the indoor environment to triangulate the position of a target device or individual. IPSs find applications across various sectors including retail, healthcare, logistics, and security. In retail, IPS enables personalized shopping experiences, wayfinding, and proximity-based marketing. Healthcare institutions utilize IPSs for asset tracking, patient monitoring, and navigation within hospital premises. In logistics, IPSs optimize warehouse management, inventory tracking, and order fulfilment processes. The potential for research in IPSs is extensive, focusing on enhancing the accuracy, scalability, and real-time performance of indoor positioning systems. Additionally, studies explore novel algorithms for the seamless integration of multiple positioning technologies (e.g., Van Haute et al., 2016), the development of energy-efficient localization methods, and the exploration of IPS applications in emerging fields such as smart homes, indoor robotics, and virtual reality environments. Overall, IPSs continue to evolve as a transformative technology with diverse applications and promising avenues for further research and development.
Call Detail Records (CDRs) are produced by telecommunications companies that detail the facts of a telecommunication transaction but not the content of the communication. CDRs are a fundamental component of telecommunications systems, providing a comprehensive log of communication activities. The mechanism of CDRs involves the systematic recording of metadata about phone calls, including caller and recipient identities, call duration, timestamps, and call type (e.g., voice, message). This data is typically captured by telecommunication service providers through signaling protocols such as Signaling System 7 or Session Initiation Protocol (SIP) (Wang et al., 2010). CDRs find widespread application in telecommunications network management, billing, fraud detection, and law enforcement investigations. Moreover, CDRs serve as valuable resources for research across various disciplines. Researchers leverage CDR datasets to analyze patterns of human mobility, social interactions, and communication dynamics, contributing insights to urban planning, epidemiology, and sociology. Additionally, CDR analysis facilitates studies on network traffic optimization, anomaly detection, and predictive modeling, advancing telecommunications infrastructure and services.
Push notifications in mobile applications serve as a crucial means of engaging users and delivering timely information or updates. They basically refer to the messages that are sent from a mobile application to the user's device even when the app is not actively in use. The mechanism behind push notifications involves a client-server architecture where the mobile app registers with a server, allowing the server to send notifications to the device even when the app is not actively running (Warren et al., 2014). Push notifications find diverse applications across industries, from alerting users about new messages, updates, or promotions in social media and messaging apps to providing real-time information in news, weather, or transportation apps. Moreover, push notifications are instrumental in increasing user retention, engagement, and conversion rates for businesses. The potential for research in push notifications spans several areas, including personalized notification delivery algorithms (Mehrotra et al., 2015), user behavior analysis, and the impact of notification frequency and timing on user engagement. Additionally, research explores the effectiveness of push notifications in influencing user actions and perceptions, as well as the ethical considerations surrounding notification practices to ensure a positive user experience while respecting user privacy preferences.
Mobile Device Sensors (MDSs) are components in mobile devices that detect changes in the environment and transmit the information to other components in the device. MDSs play a pivotal role in enhancing the functionality and user experience of mobile applications. The mechanism underlying mobile device sensors involves hardware components embedded within smartphones and tablets, including accelerometers, gyroscopes, GPS receivers, magnetometers, and ambient light sensors, among others. These sensors detect and measure various physical phenomena such as motion, orientation, location, and environmental conditions. Techniques for utilizing mobile device sensors in applications vary widely, from basic motion sensing for screen orientation adjustment to complex location tracking for navigation and augmented reality experiences (Lane et al., 2010). Mobile applications leverage sensors for diverse purposes, including fitness tracking, gaming, navigation, photography, and environmental monitoring. Moreover, mobile device sensors enable context-aware applications that adapt their behaviors based on the user's surroundings or movements. The potential for research in mobile device sensors is vast, encompassing areas such as sensor fusion techniques for improved accuracy and reliability, energy-efficient sensor data processing algorithms, and the development of novel sensor-based interaction paradigms.
In-app data capture represents an approach to gathering a wide range of information directly within a mobile application. This process is pivotal in understanding user behaviors, preferences, and interactions with the app, extending beyond mere usage metrics to include qualitative and quantitative data from device sensors. By harnessing this data, developers and researchers can delve into the nuances of user engagement, identify patterns, and tailor the app's functionality to better meet user needs. The scope of in-app data capture is broad, encompassing everything from tracking how users navigate through the app, to recording the time spent on specific features, and even capturing environmental conditions via device sensors. For instance, an app might use the smartphone's GPS to gather location data, accelerometers to detect movement, or microphones to collect audio data—all in service of creating a more responsive and personalized user experience. Moreover, this process plays a crucial role in the iterative development of mobile applications. Feedback loops can be established where the data captured is analyzed to inform updates and improvements, ensuring the app evolves in line with user expectations and technological advancements. In-app data capture also extends its utility to specialized fields such as health monitoring, where apps collect vital health statistics (Jones et al., 2018), and location-based services, which rely on real-time geographic data to offer personalized recommendations or alerts (Gavalas & Kenteris, 2011). As technology advances, the capacity for in-depth data capture within apps continues to expand, opening new frontiers for personalized digital experiences and providing valuable insights into the ever-changing landscape of human-computer interaction.
Mobile data analytics refers to the process that involves scrutinizing a vast array of data generated by mobile devices and applications to uncover insights into user behavior, app performance, and marketing effectiveness. This analytical approach enables stakeholders to dive deep into the nuances of how users engage with applications, pinpointing patterns, preferences, and pain points. The ultimate goal of mobile data analytics is to leverage these insights for optimizing app features, tailoring content, improving user interfaces, understanding human behaviors, and guiding strategic marketing decisions. At the core of mobile data analytics is the collection of diverse data types, ranging from basic usage statistics such as session lengths and frequency of app use to more complex metrics like in-app purchase behavior, ad interactions, and response to push notifications. Advanced analytics techniques, including predictive modeling and machine learning, are employed to sift through this data, identifying trends that might not be immediately obvious. For instance, analytics can reveal how different user segments interact with an app, shedding light on customized strategies to enhance engagement for each group. By integrating GPS signal analysis, this process goes beyond traditional app interaction metrics, enabling a deeper understanding of geographical usage patterns, user movements, and location-based interactions (Li et al., 2021). This spatial dimension of data analytics unlocks the potential for apps to offer highly personalized experiences and services, such as location-based recommendations, navigation assistance, and geofencing-triggered notifications.
Crowdsourcing data collection represents a paradigm shift in how geographic, environmental, social, and behavioral data is gathered, leveraging the collective power and ubiquity of mobile device users (Huang et al., 2024). This approach democratizes data collection, drawing on the contributions of a vast and diverse pool of participants, who, through their daily interactions with mobile applications (e.g., social media platforms), enrich databases with a breadth of information unattainable by traditional means, like questionnaires and surveys. Whether actively, by submitting observations, images, or other forms of data, or passively, through background data capture as they engage with an app, users become part of the community in the data collection process. The strength of crowdsourcing lies in its volume and variety (e.g., geo-tagged tweets, images, mobile photo signal data, check-in data from location-based service). By tapping into a large, geographically dispersed user base, it gathers data across a wide array of contexts and locations, offering insights into patterns and phenomena at both local and global scales. For example, iNaturalist is an international social networking resource that allows users to browse and post observations, photos, videos and findings (Matheson, 2014) while eBird data provides biostatical information about estimates of species ranges, occurrences, relative abundances, and trends in relative abundance (Johnston et al., 2021). Such data are particularly valuable in fields such as urban planning, environmental monitoring, and disaster response, where real-time, location-specific data can inform critical decision-making and policy development. Moreover, crowdsourcing fosters a sense of community and engagement among participants, encouraging users to contribute to causes or initiatives they care about.
Mobile navigation systems (e.g., Google Maps, Apple Maps, and Baidu Maps) have revolutionized the way we explore and interact with our surroundings, embodying the synthesis of GPS technology, digital mapping, and real-time traffic data. By integrating these components into user-friendly mobile applications, they offer an indispensable tool for travelers, commuters, and logistics operations, providing precise, turn-by-turn navigation instructions that adapt dynamically to changing conditions on the ground. The core of these systems lies in their ability to process vast amounts of geographic data, including road networks, points of interest, and terrain features, alongside real-time traffic flow information and incident reports. This enables them to not only guide users from point A to B but also to optimize routes based on current traffic conditions, estimated travel times, and user preferences for factors such as distance, speed, or scenic value. Beyond mere route guidance, mobile navigation systems enhance the user experience with features like voice commands, lane guidance, speed limit alerts, and the integration of local services and attractions. This level of integration transforms navigation from a simple utility into a rich, interactive journey, facilitating discovery and exploration. The evolution of mobile navigation systems reflects broader trends in GIS, IoT (Internet of Things), and smart city technologies, highlighting their role in shaping future mobility solutions. As these systems continue to advance, incorporating more granular data and predictive analytics, they will offer even more personalized and efficient navigation options, further embedding themselves into the fabric of daily life and global commerce.
Data governance and privacy concerns in mobile applications encompass a comprehensive framework designed to safeguard user data while maintaining its utility and integrity. This framework addresses the entire lifecycle of data, from collection through processing to storage and deletion, ensuring that personal information is handled in a manner that respects user privacy and adheres to regulatory standards. In the context of mobile applications, effective data governance involves clear policies on data collection, specifying what data is gathered, for what purpose, and how it is processed. This transparency is important for building trust with users, who are increasingly aware of and concerned about how their data is used. Moreover, robust data governance requires secure storage solutions and encryption protocols to protect data from unauthorized access, breaches, and leaks, thereby safeguarding personal information against potential misuse. Privacy concerns are particularly pronounced in mobile applications due to the personal nature of mobile devices and the sensitivity of the information that can be collected, ranging from location data to personal contacts and communication. As such, mobile apps must not only secure consent from users before collecting and using their data but also provide them with control over their information, including options to view, modify, and delete their data. Compliance with privacy laws and regulations, such as the General Data Protection Regulation (GDPR) in Europe (Voigt et al., 2017), California Consumer Privacy Act (CCPA) (Baik et al., 2020), and others worldwide, is a critical aspect of data governance. These regulations mandate strict criteria for data management, including user consent, data minimization, and the right to privacy.
Explain the important concepts related to mobile applications in terms of their mechanism, technique, and usage.
List the examples of mobile applications in different domains.
Name key data analytics approaches for analyzing data generated by mobile applications.
Describe the limitations and challenges of mobile applications.