Location-aware Technologies Research Articles

Implementation on Location-Based Services Using Geofencing

Geo-fencing (geofencing) is an innovative technology that utilizes the Global Positioning System (GPS) to create virtual boundaries, enabling various applications such as electronic toll collection, contextual advertising, and tourist information systems. This system aims to explore the effectiveness of spatial data in enhancing advertising performance. By leveraging tracking systems and monitoring based on satellite navigation services, including the geofencing function, businesses can accurately pinpoint their locations, boost sales, and improve overall efficiency. Geofencing facilitates remote monitoring of defined geographic areas through virtual fences, allowing for automatic detections when tracked mobile objects enter or exit these areas. This paper introduces the fundamental concepts of geofencing and explores its applications in the transportation and logistics sector. Key Words: Geofence boundaries, Shopping app, User preferences, Geofence triggers, GPS technology, User notifications, Marketing strategy, Mobile commerce, Location-aware technology, Mobile user engagement, Retail industry.

Juxtaposing individual and group mobility from sparse Wi-Fi signatures with cloud-assisted computing: a case study for a multidisciplinary university campus

ABSTRACT Understanding human mobility undoubtedly enriches common goods. Among available location-aware technologies, Wi-Fi provides a more sustainable and high-resolution means to study human mobility patterns as it has become conspicuous and affordable recently. Whilst existing studies have shed light on facets of personal mobility, the intricate dynamics of group mobility have garnered comparatively scant empirical scrutiny in real-world settings, especially in the juxtaposition with individual mobility. Moreover, they have often overlooked the multifaceted nature of personal attributes influencing daily routines. This study introduces a comprehensive framework that takes advantage of the readily available Wi-Fi connection data and cloud-assisted computing for juxtaposing individual and group mobility. The framework was tested on auniversity campus that provides representative human mobility patterns with diverse attributes. Two tests that aim to demonstrate the framework’s capability to (1) capture individual mobility patterns by using data processing amenable to the spatiotemporal sparseness and to formulate group mobility and (2) differentiate quantitatively the spatiotemporal signatures of distributions, night activities, transitions, and network topologies were conducted. This study uncovers distinct disparities between individuals and groups, and heterogeneities among different attributes in both an empirical and real-world scenario with a reduction in computation time to approximately 6% of the baseline.

Using a Cost-Distance Time-Geographic Approach to Identify Red Deer Habitat Use in Banff National Park, Alberta, Canada

Animal movements are realizations of complex spatiotemporal processes. Central to these processes are the varied environmental contexts in which animals move, which fundamentally impact the movement trajectories of individuals at fine spatial and temporal scales. An emerging perspective in time geography is the direct examination of the influence that varying contexts may have on observed movements. An approach that considers environmental context can yield actionable information for wildlife management, planning, and conservation; for instance, identifying areas of probable occupancy by an animal may improve the efficiency of fieldwork. This research develops the first known practical application of a new cost-distance-based, probabilistic voxel space–time prism (CDBPSTP) in efforts to more realistically characterize the unobserved habitat occupancies of animals occurring between known positions provided by location-aware technologies. The CDBPSTP method is applied to trajectory data collected for a group of red deer (Cervus elaphus) tracked near Banff National Park, Alberta, Canada. As a demonstration of the added value from examining how context influences movement, CDBPSTP habitat occupancy results are compared to the earlier PSTP method in context with empirical and theoretical understandings of red deer habitat preference and space-use behaviors. This comparison reveals that with CDBPSTP, variation present in the mover’s environment is explicitly considered as an influence on the mover’s probable path and occupancies between observations of its location. With the increasing availability of high-resolution geolocational and associated environmental data, this study highlights the potential for CDBPSTP to be leveraged as a broadly applicable tool in animal movement analysis.

Personalizing the dichotomy of fixed and flexible activities in everyday life: deriving prism anchors from GPS-enabled survey data.

Space-time prism is a fundamental concept in time geography that can model an individual's accessibility to resources under space-time constraints. A prism anchor is often defined by work, school, or home activity with a fixed location and schedule. Trips and other activities are relatively flexible and scheduled between prism anchors. This fixity-flexibility dichotomy may not capture the increasing complexity of human mobility behaviors or variations among individuals. Recent developments in location-aware technologies allow us to collect person-level mobility data with detailed space-time paths and contextual information. This article develops methods to extract prism anchors from these GPS-based survey data and examines whether home, work, and school activities can always be used to define prism anchors for everyone. To illustrate our methods, we use data collected in Minnesota and Beijing as two study cases. Results in both study cases suggest that not everyone has home, work, or school anchors, and people with the same socio-demographic background tend to have similar anchor types. By deriving home, work, and school anchors, we can better understand how a person's everyday schedules are governed by home, work, and school and refine person-based accessibility measures.

ILAT (Software as a Service): Interactive Learning Application Tool for Autism Screening and Assessment in children with Autism Spectrum Disorder

Autism is a type of neurological disorder usually noticeable during the early stage of childhood, especially between one to three years and occurs in all social groups. The common problem experienced by the autism subjects includes lack in social interaction, poor communication skill, overexcited, unable to express their emotions. While these disorders are not fully curable, early detection can reduce the severity with proper therapy. Even though there are no appropriate medications and treatments, still we can improve the lifestyle of the autism subject through various supportive therapies. If this disorder is not detected at early stages, the severity rate may probably increase during the later stage. Developing countries like India witness 0.2 percentage of the autism population in the overall community based on the information provided by the Rehabilitation Council of India. Express growth in the Information and Communication Technologies allows developing various assistive tools to enhance the lifestyle of the autism people. Fourth Generation Technologies like the Internet of Things, Wearable Devices, Cloud Computing, Big Data Analytics, and Artificial Intelligence, Mobile devices, Location-aware technology, Sensors, Augmented and Virtual Reality together provide a smart solution to all the sufferers. The objective of Interactive Learning Application Tool is used for Autism Screening and Assessment in children with Autism Spectrum Disorder and extended to explore the assistive technologies available to serve the community. This will enhance the social interaction, learning and communication skills in children, a tool for analysing the aggressive level, a tool for caregivers and supportive and ranking tool for psychiatrist dealing with autism subject.

Mobile times and temporalities: Histories of geomediation of time

The mobile ontology of locative media and ubiquity of location-aware technologies have led to an explicit focus on “where” and an implicit focus on “when” in geomedia studies. While welcoming this focus, we argue that this spatial bias has led the temporal dimensions of geomedia to be overlooked. Despite the growing interest that draws academic attention to mediation of time and temporal dimensions of media and data practices, there is still limited discussion on time and temporality of geomedia. We aim to fill this gap and open a debate about the temporality of geomedia based on seven oral history interviews that we conducted with mobile media scholars who pioneered in research in mobile phones from late 1990s onwards. These historical accounts include the narratives of how mobile phones were used for time-keeping, synchronizing, presencing, and coordinating everyday life. Hence, this article grounds mediation of time in the histories of geomedia.

Public space and public rituals: Engagement and protest in the digital age

In our technology-based society, individuals have more tools that they can use to manage and ‘show’ themselves in public space. In public space, they are monitored by agencies using surveillance practices but they also share information through location-aware technologies. This profound condition alters social norms and, with that, not only change self-rituals practiced in public but also group gatherings in public spaces. With an emphasis on political protests, this commentary focuses on a set of related questions: what characterises contemporary self-rituals in public space? How are these rituals being altered by digitisation processes? How are these changes manifested in the performance of the self during protests? This commentary suggests that public protests in the digital age are ‘moments of togetherness’, accelerated by social media, which dramatically enhance personalisation processes in collective actions. Reflecting on the contemporary alteration of group rituals and protests as extensions of the self, the commentary ends with a discussion about the opportunities and challenges this might bring for future collective actions.

Multi-attribute interactive visualization of three-dimensional trajectory sets

Rapidly advancing location-awareness technologies and services have collected and stored massive amounts of moving object trajectory data with attribute information that involves various degrees of spatial scales, timescales, and levels of complexity. Unfortunately, interesting behaviors regarding combinations of attributes are scarcely extracted from datasets. Further, trajectories are typically dependent on the environment of three-dimensional space, and another issue of interest to us is to preserve spatial-location visualization while guaranteeing the description of temporal information. Therefore, we developed a novel analytics tool that combines visual and interactive components to enable a dynamic visualization of three-dimensional trajectory multi-attribute behaviors. Under the context of spatiotemporal analysis, this approach integrates multiple attributes into one view to efficiently explore the attribute visualization problem of multi-attribute combination without over-plotting. To assess the feasibility of our solution, we visualized and analyzed multi-attribute information of moving object trajectories using a real mining truck dataset as a case study.

Temporal Geo-Social Personalized Keyword Search Over Streaming Data

The unprecedented rise of social media platforms, combined with location-aware technologies, has led to continuously producing a significant amount of geo-social data that flows as a user-generated data stream. This data has been exploited in several important use cases in various application domains. This article supports geo-social personalized queries in streaming data environments. We define temporal geo-social queries that provide users with real-time personalized answers based on their social graph. The new queries allow incorporating keyword search to get personalized results that are relevant to certain topics. To efficiently support these queries, we propose an indexing framework that provides lightweight and effective real-time indexing to digest geo-social data in real time. The framework distinguishes highly dynamic data from relatively stable data and uses appropriate data structures and a storage tier for each. Based on this framework, we propose a novel geo-social index and adopt two baseline indexes to support the addressed queries. The query processor then employs different types of pruning to efficiently access the index content and provide a real-time query response. The extensive experimental evaluation based on real datasets has shown the superiority of our proposed techniques to index real-time data and provide low-latency queries compared to existing competitors.

Mapping Palestine/Israel through Interactive Documentary

Available on publicly accessible websites, interactive documentaries are typically free to use, allowing audiences to navigate through amounts of information too large for standard film or television documentaries. Media literacy, however, is needed to understand the ways that interactive documentaries reveal or conceal their power to narrate. Examining ARTE France’s Gaza Sderot (2008–9), Zochrot’s iNakba (2014), and Dorit Naaman’s Jerusalem, We Are Here (2016), this article discusses documentaries that prompt audiences to reflect upon asymmetries in the power to forget history and the responsibility to remember it by mapping Palestinian geographies that have been rendered invisible. Since media ecologies are increasingly militarized, particularly in Palestine/Israel, interactive documentaries like iNakba and Jerusalem, We Are Here can disrupt Israeli state branding as technologically innovative while minimizing risk of surveillance by avoiding the use of location-aware technologies that transform interaction into tracking.

LED Wristbands for Cell-Based Crowd Evacuation: An Adaptive Exit-Choice Guidance System Architecture.

Cell-based crowd evacuation systems provide adaptive or static exit-choice indications that favor a coordinated group dynamic, improving evacuation time and safety. While a great effort has been made to modeling its control logic by assuming an ideal communication and positioning infrastructure, the architectural dimension and the influence of pedestrian positioning uncertainty have been largely overlooked. In our previous research, a cell-based crowd evacuation system (CellEVAC) was proposed that dynamically allocates exit gates to pedestrians in a cell-based pedestrian positioning infrastructure. This system provides optimal exit-choice indications through color-based indications and a control logic module built upon an optimized discrete-choice model. Here, we investigate how location-aware technologies and wearable devices can be used for a realistic deployment of CellEVAC. We consider a simulated real evacuation scenario (Madrid Arena) and propose a system architecture for CellEVAC that includes: a controller node, a radio-controlled light-emitting diode (LED) wristband subsystem, and a cell-node network equipped with active Radio Frequency Identification (RFID) devices. These subsystems coordinate to provide control, display, and positioning capabilities. We quantitatively study the sensitivity of evacuation time and safety to uncertainty in the positioning system. Results showed that CellEVAC was operational within a limited range of positioning uncertainty. Further analyses revealed that reprogramming the control logic module through a simulation optimization process, simulating the positioning system’s expected uncertainty level, improved the CellEVAC performance in scenarios with poor positioning systems.

Tourism Geography through the Lens of Time Use: A Computational Framework Using Fine-Grained Mobile Phone Data

Location-aware technologies and big data are transforming the ways we capture and analyze human activities. This has particularly affected tourism geography, which aims to study tourist activities within the context of space and places. In this study, we argue that the tourism geography of cities can be better understood through the time use of tourists captured by fine-grained human mobility observations. By using a large-scale mobile phone data set collected in three cities in South Korea (Gangneung, Jeonju, and Chuncheon), we develop a computational framework to enable accurate quantification of tourist time use, the visualization of their spatiotemporal activity patterns, and systematic comparisons across cities. The framework consists of several approaches for the extraction and semantic labeling of tourist activities, visual-analytic tools (time use diagram, time–activity diagram) for examining their time use, as well as quantitative measures that facilitate day-to-day comparisons. The feasibility of the framework is demonstrated by performing a comparative analysis in three cities during representative days when tourists tended to show more regular patterns. The framework is also employed to examine tourist time use during special events, using Gangneung during the 2018 Winter Olympics (WO) as an example. The findings are validated by comparing the spatiotemporal patterns with the WO calendar of events. The study provides a new perspective that connects time geography and tourism through the usage of spatiotemporal big data. The computational framework can be applied to compatible data sets to advance time geography, tourism, and urban mobility research.

Location-aware systems or location-based services: a survey with applications to CoViD-19 contact tracking

With the CoViD-19 pandemic, location awareness technologies have seen renewed interests due to the numerous contact tracking mobile application variants developed, deployed, and discussed. For some, location-aware applications are primarily a producer of geospatial Big Data required for vital geospatial analysis and visualization of the spread of the disease in a state of emergency. For others, comprehensive tracking of citizens constitutes a dangerous violation of fundamental rights. Commercial web-based location-aware applications both collect data and—through spatial analysis and connection to services—provide value to users. This value is what motivates users to share increasingly private and comprehensive data. The willingness of users to share data in return for services has been a key concern with web-based variants of the technology since the beginning. With a focus on two privacy preserving CoViD-19 contact tracking applications, this survey walks through the key steps of developing a privacy preserving context-aware application: from types of applications and business models, through architectures and privacy strategies, to representations.

Novel trajectory privacy-preserving method based on clustering using differential privacy

With the development of location-aware technology, a large amount of location data of users is collected by the trajectory database. If these trajectory data are directly used for data mining without being processed, it will pose a threat to the user's personal privacy. At the moment, differential privacy is favored by experts and scholars because of its strict mathematical rigor, but how to apply differential privacy technology to trajectory clustering analysis is a difficult problem. To solve the problems in which existing trajectory privacy-preserving models have poor data availability or difficulty to resist complex privacy attacks, we devise novel trajectory privacy-preserving method based on clustering using differential privacy. More specifically, Laplacian noise is added to the count of trajectory location in the cluster to resist the continuous query attack. Then, radius-constrained Laplacian noise is added to the trajectory location data in the cluster to avoid too much noise affecting the clustering effect. According to the noise location data and the count of noise location, the noise clustering center in the cluster is obtained. Finally, it is considered that the attacker can associate the user trajectory with other information to form secret reasoning attack, and secret reasoning attack model is proposed. And we use the differential privacy technology to give corresponding resistance. Experimental results using the open data show that the proposed algorithm can not only effectively protect the private information of the trajectory data, but also ensure the data availability in cluster analysis. And compared with other algorithms, our algorithm has good effect on some evaluation indicators.

Tree-Based and Optimum Cut-Based Origin-Destination Flow Clustering

Data about the movements of diverse objects, including human beings, animals, and commodities, are collected in growing amounts as location-aware technologies become pervasive. Clustering has become an increasingly important analytical tool for revealing travel patterns from large-scale movement datasets. Most existing methods for origin-destination (OD) flow clustering focus on the geographic properties of an OD flow but ignore the temporal information preserved in the OD flow, which reflects the dynamic changes in the travel patterns over time. In addition, most methods require some predetermined parameters as inputs and are difficult to adjust considering the changes in the users’ demands. To overcome such limitations, we present a novel OD flow clustering method, namely, TOCOFC (Tree-based and Optimum Cut-based Origin-Destination Flow Clustering). A similarity measurement method is proposed to quantify the spatial similarity relationship between OD flows, and it can be extended to measure the spatiotemporal similarity between OD flows. By constructing a maximum spanning tree and splitting it into several unrelated parts, we effectively remove the noise in the flow data. Furthermore, a recursive two-way optimum cut-based method is utilized to partition the graph composed of OD flows into OD flow clusters. Moreover, a criterion called CSSC (Child tree/Child graph Self-Similarity Criterion) is formulated to determine if the clusters meet the output requirements. By modifying the parameters, TOCOFC can obtain clustering results for different time scales and spatial scales, which makes it possible to study movement patterns from a multiscale perspective. However, TOCOFC has the disadvantages of low efficiency and large memory consumption, and it is not conducive to quickly handling large-scale data. Compared with previous works, TOCOFC has a better clustering performance, which is reflected in the fact that TOCOFC can guarantee a balance between clusters and help to fully understand the corresponding patterns. Being able to perform the spatiotemporal clustering of OD flows is also a highlight of TOCOFC, which will help to capture the differences in the patterns at different times for a deeper analysis. Extensive experiments on both artificial spatial datasets and real-world spatiotemporal datasets have demonstrated the effectiveness and flexibility of TOCOFC.

Generating pedestrian walking behavior considering detour and pause in the path under space-time constraints

Recently, the demand for public open spaces in cities including pedestrian-oriented streets, squares, and plazas has been growing, for better social and psychological health of modern communities. To create lively public spaces, understanding various pedestrian behaviors has been increasing in importance. For example, we need to consider pedestrians’ behavior taking a detour to see beautiful scenery or stopping a walk to watch a street performance, in addition to usual goal-oriented behavior. Recent massive trajectory data from location-aware technologies, such as GPS, Wi-Fi, and Bluetooth technology, makes data-driven approaches promising. Inverse reinforcement learning, one of the data-driven approaches, is a powerful tool to infer the utility function with respect to points of interest (POI) from observed pedestrian trajectories. This approach can generate trajectories based on attractiveness of the open space with any POI distributions because the attractiveness is represented by the utility functions of the POIs. In this paper, we propose a two-step approach to generate the various trajectories. The first step infers the utility functions of the POIs and stochastic policy by inverse reinforcement learning, and the second step generates trajectories under the constraints on given destinations and arrival time. We numerically demonstrated that our approach generated not only goal-oriented but also detour-and-pause behaviors under given destination and time constraints in two types of environments representing an open public space.

Wayfinding with my iPhone: An autoethnographic account of technological companionship and its affects

Location-aware technologies such as GPS devices and smartphones are integral to everyday, mundane navigational practices or ‘wayfinding’. The personalisation, portability and popularity of these devices means that wayfinding can be accomplished with near-instant access to place-based information. But how do people connect to these devices in more intimate, emotional, and haptic ways? To address this question, this paper draws on autoethnographic fieldwork involving wayfinding devices, using a series of iPhone navigation apps. The paper presents a series of short narratives taken from my field diaries exploring the ways in which I perceived and performed my iPhone device as a companion during my everyday mobilities. The paper focuses on three mechanisms that facilitated relations of companionship: product design, sensory engagement and emotional/affective encounters. Building on these insights, the paper argues that relations of companionship with technological devices come into focus in particular moments when the life cycles of the users and their devices collide. The various emotions and affects, which circulate in these moments, are critical to how we make sense of space, place, and our mobilities, as well as ongoing engagements with human/technology relations.

Revealing Spatial-Temporal Characteristics and Patterns of Urban Travel: A Large-Scale Analysis and Visualization Study with Taxi GPS Data

Mobility and spatial interaction data have become increasingly available due to the widespread adoption of location-aware technologies. Examples of mobile data include human daily activities, vehicle trajectories, and animal movements. In this study we focus on a special type of mobility data, i.e., origin–destination (OD) pairs, and propose a new adapted chord diagram plot to reveal the urban human travel spatial-temporal characteristics and patterns of a seven-day taxi trajectory data set collected in Beijing; this large scale data set includes approximately 88.5 million trips of anonymous customers. The spatial distribution patterns of the pick-up points (PUPs) and the drop-off points (DOPs) on weekdays and weekends are analyzed first. The maximum of the morning and the evening peaks are at 8:00–10:00 and 17:00–19:00. The morning peaks of taxis are delayed by 0.5–1 h compared with the commuting morning peaks. Second, travel demand, intensity, time, and distance on weekdays and weekends are analyzed to explore human mobility. The travel demand and high-intensity travel of residents in Beijing is mainly concentrated within the 6th Ring Road. The residents who travel long distances (>10 km) and for a long time (>60 min) mainly from outside the 6th Ring Road and the surrounding new towns of Beijing. The circular structure of the travel distance distribution also confirms the single-center urban structure of Beijing. Finally, a new adapted chord diagram plot is proposed to achieve the spatial-temporal scale visualization of taxi trajectory origin–destination (OD) flows. The method can characterize the volume, direction, and properties of OD flows in multiple spatial-temporal scales; it is implemented using a circular visualization package in R (circlize). Through the visualization experiment of taxi GPS trajectory data in Beijing, the results show that the proposed visualization technology is able to characterize the spatial-temporal patterns of trajectory OD flows in multiple spatial-temporal scales. These results are expected to enhance current urban mobility research and suggest some interesting avenues for future research.

The Enthusiast, the Interested, the Sceptic, and the Cynic

We discuss the user study of a mobile cultural heritage game designed to stimulate reflection about a city's history. Aided by location-aware technology, the game fosters the serendipitous discovery of points of interest, historical images and stories, whilst players wander the city. This exploration differs from the typical precalculated path recommendations used by other location-based applications. It triggers reflection about the city's past that is as unique as its visitors. Ours is one of the first studies to attempt an understanding of the effects of serendipitous urban discovery and historic reflection-triggering technologies on user experience. We combined field trials with controlled experiments, analysing perceptions of the experience and value using responses expressed in open-ended questionnaire items. Using thematic coding and sentiment analysis, we observed types of emotional responses, indicating four potential profiles of their likelihood towards future technology adoption. Enthusiastic and Interested users appreciated the freedom of movement choice that created an autonomous experience that fostered a sense of personal accomplishment. The interface interactions of the game, designed to stimulate reflection, supported a feeling of connectedness to others. In contrast, Cynical and Sceptical users were less tolerant of perceived technological issues, requiring more perfection in functionality and design. These users are less likely to be the early adopters of serendipitous location-based apps. The game was developed as part of a large cultural informatics project but, unlike typical evaluations, we conducted this study midway through the project and not at its end. This approach (1) gave the team the possibility to take stock, pause, and reflect and (2) provided insight on future design improvements for increasing the perceived value of serendipitous urban discovery applications. Our results contribute towards a grounded understanding of user experience and help progress the development of cultural heritage applications that incorporate elements of reflection and/or place-based exploration into their functionalities.

Spatio-Temporal Fingerprint Localization for Shipboard Wireless Sensor Networks

Location-aware technology has numerous potential applications in safety, living, and services in the shipboard environment. Fingerprint-based methods that use the received signal strength indication (RSSI) to provide high positioning accuracy have been widely adopted for indoor localization. However, the RSSI can be severely disturbed by the steel structure and dynamics of the shipboard environment, which prevents it from being implemented in shipboard localization in practice. In this paper, a novel approach, called spatio-temporal fingerprint localization, is proposed. This approach can alleviate the impact of the dynamic shipboard environment and enhance the localization robustness. To adapt to the environmental dynamics, a radio time series is proposed to filter irrelevant noise from location fingerprints. An extraction method called radio spatial features is proposed to identify the highly location related features from the redundant RSSI information by using linear discriminant analysis and principal component analysis. Extensive experiments on the passenger ship Yangtze 2 demonstrate the effectiveness of the proposed algorithm in providing higher accuracy than previous fingerprint-based methods.