Location-based Technologies Research Articles

Private, Public, Personal: Shifting Patterns in Geospatial Data Sources in Geographic Research

Geospatial data sources include data collected by the public sector (i.e., government), private sector (i.e., industry), or through field work. Of these categories, the private sector, especially through tech firms, has provided new types of cutting-edge data sets that are particularly large and novel. These include data from social media platforms and location-based technologies such as Global Positioning Systems and mobile phones. Here, we explore the extent to which peer-reviewed geospatial research has adopted these data sources, perhaps in lieu of more traditional data types. We review peer-reviewed journal articles from six flagship journals in geographic information systems, geography, and urban analytics that publish research that uses spatial data analysis. We find that geospatial researchers continue to rely on government-collected data for their research, but that researchers’ use of data from the private sector have increased in recent years. This finding implies that as spatial analysis studies increasingly rely on private data sets, we should revisit (1) how industry data collection faces fewer regulations on data quality and has different motivations for their collection, (2) how this affects our ability to trust our data sets, and (3) the role of government and field work in data collection and dissemination in the future.

Analysis of agricultural insurance vulnerability in the face of natural disasters: Insights from Iran

Agricultural insurance plays a crucial role in managing and mitigating damages from natural disasters, while also providing support to farmers through equitable risk distribution. This study focuses on identifying vulnerabilities of the Agricultural Insurance Fund (AIF) in Iran during natural disasters and proposing solutions for maintaining preparedness and adapting to new circumstances. Utilizing a qualitative approach, the research involved a three-stage classic Delphi technique with 24 experts in agricultural insurance which purposefully selected using snowball sampling. Three open-ended questions on AIF vulnerabilities in facing natural disasters, AIF's adaptive capacity, and forecasting strategic measures for the future, were provided in the first research round. After refining in subsequent rounds, a consensus was reached on 19 vulnerability points, 7 adaptive capacity factors, and 20 strategic actions. Key indicator of vulnerabilities includes the “weak financial capacity of the insurance fund”. “Designing an integrated agricultural insurance system” was the most crucial factor in enhancing AIF's adaptive capacity. Finally, the study proposes strategies like "strengthening financial capacity and ensuring sustainable credit resources," "developing capacities of the insurance system with location-based technologies," and "differentiating catastrophic damages in the insurance tariffing system" to address vulnerabilities and enhance agricultural insurance resilience.

The Ethics of Electronic Tracking Devices in Dementia Care: An Interview Study with Developers

Wandering is a symptom of dementia that can have devastating consequences on the lives of persons living with dementia and their families and caregivers. Increasingly, caregivers are turning towards electronic tracking devices to help manage wandering. Ethical questions have been raised regarding these location-based technologies and although qualitative research has been conducted to gain better insight into various stakeholders' views on the topic, developers of these technologies have been largely excluded. No qualitative research has focused on developers’ perceptions of ethics related to electronic tracking devices. To address this, we performed a qualitative semi-structured interview study based on grounded theory. We interviewed 15 developers of electronic tracking devices to better understand how they perceive ethical issues surrounding the design, development, and use of these devices within dementia care. Our results reveal that developers are strongly motivated by moral considerations and believe that including stakeholders throughout the development process is critical for success. Developers felt a strong sense of moral obligation towards topics within their control and a weaker sense of moral obligation towards topics outside their control. This leads to a perceived moral boundary between development and use, where some moral responsibility is shifted to end-users.

“Those blimmin Ts and Cs”: a mixed methods analysis of how people manage personal information, privacy, and impressions

ABSTRACT Interconnected and smart technologies complicate personal information management (PIM) because users delegate the storing, organizing, and retrieving of personal information to smart and mobile service providers. Meta-level PIM activities are required to maintain the privacy and security of personal information. This study provides insights into how users of location tracking, mobile apps, and smart home technologies perceive PIM and privacy. We turn to the privacy as contextual integrity (CI) and impression management (IM) literatures to explore informational norms and interpersonal dynamics in PIM. This study is based on a mixed methods design to analyze focus groups and interviews with 106 British and Dutch respondents. Combining unsupervised Latent Dirichlet allocation (LDA) topic modeling and thematic analysis, we reveal discursive patterns in respondent accounts of technology use and provide an in-depth interpretation of these patterns. Our findings indicate that PIM practices are associated with the perceived appropriateness of information flows, anthropomorphic interpretations of technologies, and interpersonal surveillance. Thus, impressions are managed toward social actors as well as technology providers. We contribute to PIM research with a demonstration of how PIM in mobile, smart, and location-based technology use cannot be separated from contextual factors and strategies to manage impressions of habits and behaviors.

Digital twinning of surveillance robot

Surveillance robots provide troops with real-time information about their surroundings, including enemy positions, terrain, and potential threats. This information is invaluable for making informed decisions and ensuring the safety of military personnel. Geo-fenced robots are robots equipped with technology that restricts their movements within predefined geographic boundaries. These boundaries are typically established using GPS or other location-based technologies. In applications like the military, geo-fencing can be used to establish secure zones. Geo-fencing helps prevent robots from entering hazardous areas, reducing the risk of damage to the robot and potential harm to friendly forces or civilians This project aims at Digital twinning of robots by creating a virtual replica or model of a physical robot in a digital environment. Digital twinning allows engineers and designers to simulate and test the robot’s behavior, performance, and capabilities in a virtual environment before building the physical robot. This can lead to increased efficiency, reduced downtime, and cost savings. In this project, LiDAR (Light Detection and Ranging) sensor data is integrated with a digital twinned robot to create a virtual reproduction of the robot and its surroundings. LiDAR is a remote sensing technology that maps the robot’s surroundings in fine detail using 3D point clouds by measuring distances using laser pulses. Here we make use of RPLIDAR A1 M8 and acquire data from it using ROS with the help of a RaspberryPi4B Controller. Simulink is used to create a 3D model of the robot’s environment and the robot itself. Reinforcement learning and pure pursuit algorithms are used for developing them. This project discusses the need for geofenced autonomous robots and emphasizes the security and reliability it brings to military applications.

Spatio-temporal characteristics of human activities using location big data in Qilian Mountain National Park

ABSTRACT Human activities significantly impact the environment. Understanding the patterns and distribution of these activities is crucial for ecological protection. With location-based technology advancement, big data such as location and trajectory data can be used to analyze human activities on finer temporal and spatial scales than traditional remote sensing data. In this study, Qilian Mountain National Park (QMNP) was chosen as the research area, and Tencent location data were used to construct time series data. Time series clustering and decomposition were performed, and the spatio-temporal distribution characteristics of human activities in the study area were analyzed in conjunction with GPS trajectory data and land use data. The study found two distinct human activity patterns, Pattern A and Pattern B, in QMNP. Compared to Pattern B, Pattern A had a higher volume of location data and clear nighttime peaks. By incorporating land use and trajectory data, we conclude that Pattern A and Pattern B represent the activity patterns of the resident and tourist populations, respectively. Moreover, the study identified seasonal variations in human activities, with human activity in summer being approximately two hours longer than in winter. We also conducted an analysis of human activities in different counties within the study area.

Geo-Fencing or Geo-Conquesting? a strategic analysis of Location-Based coupon under different market structures

Location-based technology enables firms to target consumers with personalized coupons based on their real-time locations, making location-based coupons (LBC) an innovative marketing tool. In this paper, we consider two types of LBC strategies, namely defensive geo-fencing versus offensive geo-conquesting. With a defensive LBC strategy, a company sets a virtual geo-fence by offering coupons with deeper discounts to consumers located closer to the focal firm. Using a spatial model, we examine how two competing companies choose between defensive and offensive LBC strategies, as well as the impact of LBC strategies on company profits, consumer surplus, and social welfare. The results show that the defensive LBC strategy lowers revenue in a monopoly market but increases it under duopoly conditions. In a duopoly market, both firms adopting the defensive LBC strategy is the Nash equilibrium outcome, leading to the highest profits but lowest consumer surplus. The misalignment of interests among firms and consumers requires policymakers to regulate firms’ LBC strategies for the sake of customers.

Leveraging the Characteristics of Location-Based Communication in Online Marketing

Contemporary online marketing communications are increasingly integrated with modern technologies and innovative solutions. Locationbased technologies are one of the approaches that help a business target customers precisely based on their geographic location. Eventually, a number of benefits can be obtained by integrating location-based technologies into marketing strategies. However, it requires a high level of professionalism and may be followed by various complications and risks. Nevertheless, location-oriented marketing can be regarded as one of the most efficient approaches in today’s marketing communications. Therefore, it is necessary to research it theoretically, study practical cases and make relevant conclusions. It will allow businesses to improve their decisions concerning various marketing activities: audience segmentation, precise targeting, collecting and processing analytical data, and planning large-scale marketing campaigns. In this process, customer data privacy is one of the most important issues, which is a separate, complex object of research. Therefore, this paper focuses on reviewing characteristics, opportunities, consumer behavior traits, and benefits of location-based communication in the marketing context. The final parts of the paper provide an overall analysis of the issue and a list of recommendations as a conclusion.

Privacy may be more important in Beijing than in Hong Kong in location-based services

Location-based technologies are getting sophisticated, and there are many studies conducted focused on the influence of permission, trust, relevance, and context issues in the acceptance of location-based services (LBS) and advertising in various cultural backgrounds. Understanding the perception of LBS in the e-commerce maturity spectrum is important for marketers to maximise the impact of these technologies. In this empirical study, the authors found that respondents from Beijing are a little more acceptive of LBS in a certain category of products and services while respondents from Hong Kong are acceptive in other categories. The research also identified two groups of respondents - progressives and vexessives, in both locations. The larger group progressives are openly embracing LBS, and smaller group vexessives are often annoyed with LBS and are very slow in accepting these technologies. The challenge is to identify the efficacy of these technologies for various categories of products in different economic, legal, and cultural backgrounds and find effective solutions.

PyTrack: A Map-Matching-Based Python Toolbox for Vehicle Trajectory Reconstruction

The exponential growth of IoT devices, smartphones, smartwatches, and vehicles equipped with positioning technology, such as Global Positioning System (GPS) modules, has boosted the development of location-based services for several applications in Intelligent Transportation Systems. However, the inherent error of location-based technologies makes it necessary to align the positioning trajectories to the actual underlying road network, a process known as map-matching. To the best of our knowledge, there are no comprehensive tools that allow us to model street networks, conduct topological and spatial analyses of the underlying street graph, perform map-matching processes on GPS point trajectories, and deeply analyse and elaborate these reconstructed trajectories. To address this issue, we present PyTrack, an open-source map-matching-based Python toolbox designed for academics, researchers and practitioners that integrate the recorded GPS coordinates with data provided by the OpenStreetMap, an open-source geographic information system. This manuscript overviews the architecture of the library offering a detailed description of its capabilities and modules. Besides, we provide an introductory guide to getting started with PyTrack covering the most fundamental steps of our framework. For more information on PyTrack, users are encouraged to visit the official repository at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/cosbidev/PyTrack</uri> or the official documentation at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://pytrack-lib.readthedocs.io</uri> .

Multitasking and location-based advertising: effects of perceived intrusiveness, location proximity, and product durability on avoidance

Location-based technology enables marketers to identify the consumers who pass by their stores and send them mobile advertisements or coupons. While location-based advertising (LBA) has garnered both professional and academic interest, research on the responses of consumers to LBA is still scarce. Moreover, previous studies have not evaluated consumers’ situational factors as reaction constraints to LBA. This study attempts to identify (1) the effect of consumers’ multitasking on the avoidance of LBA, (2) the mediating role of the perceived intrusiveness between consumers’ multitasking and avoidance of LBA, and (3) the moderating roles of location proximity and product durability in the relationship between consumers’ multitasking and avoidance of LBA. Three experiments show that consumers who multitask tend to avoid LBA and perceived intrusiveness mediates consumers’ avoidance of LBA. Furthermore, this study reveals that LBA from close stores and advertising of durable goods increases the perceived intrusiveness and ad avoidance of multitasking consumers. This study contributes to the literature on LBA and suggests new directions for mobile marketers and retailers wanting to use LBA.

Advancements and research topics of museum-based mobile learning: a review of journal publications from 2008 to 2019

ABSTRACT This research reviewed the publications in the Scopus database in the museum-based mobile learning field based on the technology-based learning review model from 2008 to 2019. The aim of this study was to investigate trends in the field of museum-based mobile learning from the main journals and papers, including countries and areas, participants, research methods, learning domains, the orientation of adopted sensing or location-based technologies, learning strategies, and research topics. The cluster and co-occurrence analysis methods were used to analyze the main key terms and the relationships between them. Three thematic clusters, namely strategies and media for mobile-based museum learning, personalized supports for mobile-based museum learning, and applications of mobile-based museum learning were identified. The results show that inquiry-based learning was the most adopted learning strategy for higher education, while contextual mobile learning was the most frequently employed strategy for elementary school students; such extensively adopted mobile learning strategies as peer assessment, project-based learning, video sharing, and synchronous sharing were not adopted in the studies. In addition, an important finding in the literature of the main research purpose of museum-based mobile learning research is the affective domain, followed by the influence of learner characteristics and the effects of mobile learning.

The City Experience with Location-Based Media: An Examination Through Cognitive Maps

Purpose The physical space has been an important value for supporting human experience. With the development of mobile devices and location-based media, these technologies have been increasingly present in our virtual spatial experience and the experience of physical city has been changed permanently. Accordingly, this paper aims to discuss the experience potential of urban space with location-based technologies. Design/Methodology/Approach The research structure of this study is based on the literature of location-based media and ‘Space of flow’ theory. Within the methodology, a workshop is conducted to review the cognitive mappings of the virtual city experience. The workshop is structured through two segments: Documenting the spatial experience revealed using location-based media made by cognitive mappings and testing participants' direct experiences within structured questionnaire-based interviews and finally, interpretating of the obtained data. Findings This paper provides an experiential examination of the relationship between the usage of the degree of location-based media and cognitive experience. Findings point out that experience may differ depending on the media interface used and it is controversial comparing with the physical space experience with its multiple relationships. Research Limitations/Implications The research subject had been exemplified with limited location-based media in a specific location. Originality/Value The proposed method of examining the degree of spatial experience by the location-based media linking with interviews determines the originality of the study. There are also limited studies on the topic of location-based media by focusing on the users' cognitive maps.

Pervasive Learning Environment for Educational Makerspaces with Emerging Technologies and Teaching and Learning Transformation

In 21st century the technological innovation has reshaped our educational system. Different educational methods and environments are used to facilitate students and teachers. Educational makerspaces are also one of them. This new trend has shifted educational system to student centered instead of teacher centered. Pervasive Learning (P-Learning) environment for educational makerspaces is one of the emerging environments for teaching and learning due to price reduction in handheld devices’, facility for sharing the technological resources, support, and smartness of smartphone technology. Educational Makerspaces environment provide the facility to teachers and learners to create a virtual environment fully equipped with latest ICT tools to share ideas, to perform experiments and collaborate with each other whenever and wherever they are. Thus, P-learning for educational makerspaces can access and share the resources for teaching and learning material beyond the boundaries of the traditional classroom arrangement. This paper presents the idea of P-learning environment for educational makerspaces which is not limited to a single geographic location or mobile or location-based technologies; rather, it accesses, shares the resources, and facilitates teaching and learning from anywhere and at any time with any handheld device means 24*7*12. The purpose of this paper is to propose a fully equipped makerspace classroom so that the students from remote areas or who cannot afford laboratory expenses can access virtually labs, share their ideas, collaborate with each other, and perform experiments

Mapping hybrid cities through location-based technologies: A systematic review of the literature

Over the past two decades, the development of a new set of technologies, devices and applications has supported the emergence of a new infocommunicational landscape in cities—one of the key fuels to the growth of smart cities movement. Digital media platforms, ubiquitous technologies and geographic annotation technologies have blurred the boundaries of virtual and physical environments—with an access through digital devices and specifically mobile devices. The paper aims to investigate the emerging location-based and mapping technologies to develop a clearer understanding on their applicability in the urban context by identifying and categorizing the key drivers based on desired outcomes. In order to achieve this aim, the study undertakes a systematic literature review on the planned and applied use of location-based technologies for mapping urban environments. In doing so, the paper outlines the spatial distribution of these advanced technologies across globe, and presents a synthesis of the emerging themes and categories, and potential avenues for future research. The study findings reveal three types of mapping approaches—i.e., reason-oriented, sense-oriented and community-oriented—that are linked to two modes of use—i.e., desktop and in-situ.

Unifying Online and Offline Preference for Social Link Prediction

Recent advances in network representation learning have enabled significant improvement in the link prediction task, which is at the core of many downstream applications. As an increasing amount of mobility data become available because of the development of location-based technologies, we argue that this resourceful mobility data can be used to improve link prediction tasks. In this paper, we propose a novel link prediction framework that utilizes user offline check-in behavior combined with user online social relations. We model user offline location preference via a probabilistic factor model and represent user social relations using neural network representation learning. To capture the interrelationship of these two sources, we develop an anchor link method to align these two different user latent representations. Furthermore, we employ locality-sensitive hashing to project the aggregated user representation into a binary matrix, which not only preserves the data structure but also improves the efficiency of convolutional network learning. By comparing with several baseline methods that solely rely on social networks or mobility data, we show that our unified approach significantly improves the link prediction performance.Summary of Contribution: This paper proposes a novel framework that utilizes both user offline and online behavior for social link prediction by developing several machine learning algorithms, such as probabilistic factor model, neural network embedding, anchor link model, and locality-sensitive hashing. The scope and mission has the following aspects: (1) We develop a data and knowledge modeling approach that demonstrates significant performance improvement. (2) Our method can efficiently manage large-scale data. (3) We conduct rigorous experiments on real-world data sets and empirically show the effectiveness and the efficiency of our proposed method. Overall, our paper can contribute to the advancement of social link prediction, which can spur many downstream applications in information systems and computer science.

A study on the use of mobile computing technologies for improving the mobility of Windhoek residents

The aim of this study was to investigate the use of mobile computing technologies for improving the mobility of Windhoek residents, through exploring the perceptions and attitudes of Windhoek taxi drivers and passengers subjected to a real time experiment of an existing mobile taxi hailing and dispatcher system, Taxi StartApp. The study used the Experience Sampling Method (ESM) where users simulated a taxi hailing service that included a pick and drop in real time and then assessed their perceptions of the location-based technology and its service. The research further deployed a widely used System Usability Scale to evaluate the Taxi StartApp system usability, as a combination of effectiveness, efficiency and satisfaction. This research employed qualitative analysis through the adoption of ESM. Eighty samples were selected randomly among taxi drivers and passengers within the town (Windhoek) vicinity. The research falls in the technology adoption research category and the lessons learned from the ESM will help developers to come up with demographically appropriate applications that will address the transportation and mobility challenges in Windhoek.

Analysis of distance measures in spatial trajectory data clustering

Tremendous growth of Location-based technologies resulted in the generation of a huge volume of spatial data, which needs to be analyzed to get potentially important patterns. The spatial patterns extracted can be used to design a better infrastructure ensuring reliable service coverage. Trajectory data is one variant of spatial data that are generated by moving objects travelling across. It is represented as a sequence of spatial coordinates (latitude, longitude) of a location. Trajectory clustering tries to group similar spatial data points to extract the most common movement behaviors. Trajectory data poses major challenges including uncertainty, sampling rate, representation, relationships, spatial autocorrelation, serialization, redundancy, and triviality, which makes it hard to apply traditional clustering algorithms over trajectory data. In this paper, K-Means and DBSCAN (Density-based spatial clustering of applications with noise) clustering algorithms are analyzed using different similarity measures like Euclidean, Hausdorff and Haversine distances with the help of index measures say Adjusted Rand Index (ARI) and Fowlkes-Mallows scores (FMS). Experiment is carried out over two different trajectory datasets and it is proved that usage of Haversine distance for clustering is efficient than Euclidean and Hausdorff distances in terms of spatial trajectory data.

Human Mobility Prediction With Region-Based Flows and Water Consumption

We are witnessing an increasing need to accurately measure people's mobility as it has become an instrumental factor for the development of innovative services in multiple domains. In this context, several ICT solutions have relied on location-based technologies such as GPS, WiFi or Bluetooth to track individual's movements. However, these technologies are limited by the privacy restrictions of data providers. In this paper we propose a methodology to robustly predict citizens' mobility patterns based on heterogeneous data from different sources. Particularly, our methodology focuses on a human mobility predictor based on a low-resolution mobility dataset and the use of water consumption data as a facilitator of this prediction task. As a result, this work explores whether the water consumption within a geographical region can reveal human activity patterns relevant from the point of view of the mobility mining discipline. This approach has been tested in a residential area near Madrid (Spain) obtaining quite promising results.

The Location estimation of seismic wave propagation layer velocities

On the basis of the original location-based technology developed by the authors for constructing seismic images of the deep structure of the earth’s interiora method is proposed for estimating the seismic wave propagation layer velocities. The technology ensures the unambiguity and stability of seismic imaging by successively solving direct kinematic problems for all reflecting boundaries. It consists in locating the reflection pulse’s sources from the boundaries of the deep section’s layers and restoring the kinematic parameters of the layers through them. To assess the next layer velocity, information is used on the parameters of the preceding boundaries and the direct kinematic problem’s solution with the additional equality’s condition of local velocities at the reflection’s points from the underlying boundary.