Special Issue "Recent Trends in Location Based Services and Science"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (15 July 2020).

Special Issue Editors

Prof. Dr. Georg Gartner
E-Mail Website
Guest Editor
Department of Geodesy and Geoinformation, Technical University Vienna, Vienna, Austria
Interests: theoretical cartography; location-based services; web mapping; semiology; service oriented cartography; semantic cartography
Special Issues and Collections in MDPI journals
Dr. Haosheng Huang
E-Mail Website
Guest Editor
Geographic Information Science (GIS), Department of Geography, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
Interests: GIScience; location based services; geospatial big data analytics
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

We are now living a mobile information era, which is fundamentally changing science and human society. Location Based Services (LBS), which deliver information depending on the location of the (mobile) device and user, play a key role in this mobile information era. Recent years have seen rapid progress in location based services and science, especially concerning the increasing demands in expanding LBS from outdoor to indoor, from location-aware to context-aware, and from navigation systems and mobile guides to more diverse applications (e.g., healthcare, transportation, gaming), as well as the appearance of new interface technologies (e.g., digital glasses, smartwatches), the increasing smartness of our environments and cities, and the growing ubiquity of LBS in our daily life.

This Special Issue aims to provide a general picture of recent research activities related to LBS. We invite original research contributions on all areas of location-based services and science, including (but not limited to):

  • Context and user modelling
  • Mobile user interface and interaction
  • Ubiquitous positioning
  • Evaluation and user studies
  • Analysis of LBS-generated data (e.g., tracking data, social media data, crowdsourced geographic information)
  • Social and behavioral implications of LBS (e.g., privacy, ethics, and business aspects)

Prof. Dr. Georg Gartner
Dr. Haosheng Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Location Based Services (LBS)
  • Global Positioning System (GPS)
  • Context-aware Services
  • Positioning
  • Mobile User Interface
  • Privacy
  • Location-Based Social Networks
  • Location Tracking
  • Activity Sensing

Published Papers (13 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Near Relation-Based Indoor Positioning Method under Sparse Wi-Fi Fingerprints
ISPRS Int. J. Geo-Inf. 2020, 9(12), 714; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120714 - 01 Dec 2020
Viewed by 556
Abstract
Indoor positioning is of great importance in the era of mobile computing. Currently, considerable focus has been on RSS-based locations because they can provide position information without additional equipment. However, this method suffers from two challenges: (1) fingerprint ambiguity and (2) labour-intensive fingerprint [...] Read more.
Indoor positioning is of great importance in the era of mobile computing. Currently, considerable focus has been on RSS-based locations because they can provide position information without additional equipment. However, this method suffers from two challenges: (1) fingerprint ambiguity and (2) labour-intensive fingerprint collection. To overcome these drawbacks, we provide a near relation-based indoor positioning method under a sparse Wi-Fi fingerprint. To effectively obtain the fingerprint database, certain interpolation methods are used to enrich sparse Wi-Fi fingerprints. A near relation boundary is provided, and Wi-Fi fingerprints are constrained to this region to reduce fingerprint ambiguity, which can also improve the efficiency of fingerprint matching. Extensive experiments show that the kriging interpolation method performs well, and a positioning accuracy of 2.86 m can be achieved with a near relation under a 1 m interpolation density. Full article
(This article belongs to the Special Issue Recent Trends in Location Based Services and Science)
Show Figures

Figure 1

Article
Articulated Trajectory Mapping for Reviewing Walking Tours
ISPRS Int. J. Geo-Inf. 2020, 9(10), 610; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100610 - 21 Oct 2020
Viewed by 592
Abstract
This paper addresses how to enrich a map-based representation for reviewing walking tours with the features of trajectory mapping and tracing animation. Generally, a trajectory generated by raw GPS data can often be difficult to browse through on a map. To resolve this [...] Read more.
This paper addresses how to enrich a map-based representation for reviewing walking tours with the features of trajectory mapping and tracing animation. Generally, a trajectory generated by raw GPS data can often be difficult to browse through on a map. To resolve this issue, we first illustrated tangled trajectory lines, inaccurate indoor positioning, and unstable trajectory lines as problems encountered when mapping raw trajectory data. Then, we proposed a new framework that focuses on GPS horizontal accuracy to locate indoor location points and find stopping points on an accelerometer. We also applied a conventional line simplification algorithm to make the trajectory cleaner and then integrated the extracted points with the clean trajectory line. Furthermore, our experiments with some actual logs of walking tours demonstrated that articulated trajectory mapping, which comprises simplification and characterization methods, sufficiently reliable and effective for better reviewing experiences. The paper contributes to the research on cleaning up map-based displays and tracing animations of raw trajectory GPS data by using not only location data but also sensor data that smartphones can collect. Full article
(This article belongs to the Special Issue Recent Trends in Location Based Services and Science)
Show Figures

Figure 1

Article
Towards Deriving Freight Traffic Measures from Truck Movement Data for State Road Planning: A Proposed System Framework
ISPRS Int. J. Geo-Inf. 2020, 9(10), 606; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100606 - 14 Oct 2020
Cited by 1 | Viewed by 834
Abstract
To make the right decisions on investments, operations, and policies in the public road sector, decision makers need knowledge about traffic measures of trucks, such as average travel time and the frequency of trips among geographical zones. Private logistics companies daily collect a [...] Read more.
To make the right decisions on investments, operations, and policies in the public road sector, decision makers need knowledge about traffic measures of trucks, such as average travel time and the frequency of trips among geographical zones. Private logistics companies daily collect a large amount of freight global positioning system (GPS) and shipment data. Processing such data can provide public decision makers with detailed freight traffic measures, which are necessary for making different planning decisions. The present paper proposes a system framework to be used in the research project “A new system for sharing data between logistics companies and public infrastructure authorities: improving infrastructure while maintaining competitive advantage”. Previous studies ignored the fact that the primary step for delivering valuable and usable data processing systems is to consider the final user’s needs when developing the system framework. Unlike existing studies, this paper develops the system framework through applying a user-centred design approach combining three main steps. The first step is to identify the specific traffic measures that satisfy the public decision makers’ planning needs. The second step aims to identify the different types of freight data required as inputs to the data processing system, while the third step illustrates the procedures needed to process the shared freight data. To do so, the current work employs methods of literature review and users’ need identification in applying a user-centralized approach. In addition, we develop a systematic assessment of the coverage and sufficiency of the currently acquired data. Finally, we illustrate the detailed functionality of the data processing system and provide an application case to illustrate its procedures. Full article
(This article belongs to the Special Issue Recent Trends in Location Based Services and Science)
Show Figures

Figure 1

Article
A Social–Aware Recommender System Based on User’s Personal Smart Devices
ISPRS Int. J. Geo-Inf. 2020, 9(9), 519; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090519 - 30 Aug 2020
Cited by 5 | Viewed by 917
Abstract
Providing recommendations in cold start situations is one of the most challenging problems for collaborative filtering based recommender systems (RSs). Although user social context information has largely contributed to the cold start problem, most of the RSs still suffer from the lack of [...] Read more.
Providing recommendations in cold start situations is one of the most challenging problems for collaborative filtering based recommender systems (RSs). Although user social context information has largely contributed to the cold start problem, most of the RSs still suffer from the lack of initial social links for newcomers. For this study, we are going to address this issue using a proposed user similarity detection engine (USDE). Utilizing users’ personal smart devices enables the proposed USDE to automatically extract real-world social interactions between users. Moreover, the proposed USDE uses user clustering algorithm that includes contextual information for identifying similar users based on their profiles. The dynamically updated contextual information for the user profiles helps with user similarity clustering and provides more personalized recommendations. The proposed RS is evaluated using movie recommendations as a case study. The results show that the proposed RS can improve the accuracy and personalization level of recommendations as compared to two other widely applied collaborative filtering RSs. In addition, the performance of the USDE is evaluated in different scenarios. The conducted experimental results on USDE show that the proposed USDE outperforms widely applied similarity measures in cold start and data sparsity situations. Full article
(This article belongs to the Special Issue Recent Trends in Location Based Services and Science)
Show Figures

Figure 1

Article
GroupSeeker: An Applicable Framework for Travel Companion Discovery from Vast Trajectory Data
ISPRS Int. J. Geo-Inf. 2020, 9(6), 404; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9060404 - 20 Jun 2020
Viewed by 833
Abstract
The popularity of mobile locate-enabled devices and Location Based Service (LBS) generates massive spatio-temporal data every day. Due to the close relationship between behavior patterns and movement trajectory, trajectory data mining has been applied in numerous fields to find the behavior pattern. Among [...] Read more.
The popularity of mobile locate-enabled devices and Location Based Service (LBS) generates massive spatio-temporal data every day. Due to the close relationship between behavior patterns and movement trajectory, trajectory data mining has been applied in numerous fields to find the behavior pattern. Among them, discovering traveling companions is one of the most fundamental techniques in these areas. This paper proposes a flexible framework named GroupSeeker for discovering traveling companions in vast real-world trajectory data. In the real-world data resource, it is significant to avoid the companion candidate omitting problem happening in the time-snapshot-slicing-based method. These methods do not work well with the sparse real-world data, which is caused by the equipment sampling failure or manual intervention. In this paper, a 5-stage framework including Data Preprocessing, Spatio-temporal Clustering, Candidate Voting, Pseudo-companion Filtering, and Group Merging is proposed to discover traveling companions. The framework even works well when there is a long time span during several days. The experiments result on two real-world data sources which offer massive amount of data subsets with different scale and different sampling frequencies show the effective and robustness of this framework. Besides, the proposed framework has a higher-efficiency performing when discovering satisfying companions over a long-term period. Full article
(This article belongs to the Special Issue Recent Trends in Location Based Services and Science)
Show Figures

Figure 1

Article
Uber Movement Data: A Proxy for Average One-way Commuting Times by Car
ISPRS Int. J. Geo-Inf. 2020, 9(3), 184; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9030184 - 24 Mar 2020
Cited by 1 | Viewed by 1135
Abstract
Recently, Uber released datasets named Uber Movement to the public in support of urban planning and transportation planning. To prevent user privacy issues, Uber aggregates car GPS traces into small areas. After aggregating car GPS traces into small areas, Uber releases free data [...] Read more.
Recently, Uber released datasets named Uber Movement to the public in support of urban planning and transportation planning. To prevent user privacy issues, Uber aggregates car GPS traces into small areas. After aggregating car GPS traces into small areas, Uber releases free data products that indicate the average travel times of Uber cars between two small areas. The average travel times of Uber cars in the morning peak time periods on weekdays could be used as a proxy for average one-way car-based commuting times. In this study, to demonstrate usefulness of Uber Movement data, we use Uber Movement data as a proxy for commuting time data by which commuters’ average one-way commuting time across Greater Boston can be figured out. We propose a new approach to estimate the average car-based commuting times through combining commuting times from Uber Movement data and commuting flows from travel survey data. To further demonstrate the applicability of the commuting times estimated by Uber movement data, this study further measures the spatial accessibility of jobs by car by aggregating place-to-place commuting times to census tracts. The empirical results further uncover that 1) commuters’ average one-way commuting time is around 20 min across Greater Boston; 2) more than 75% of car-based commuters are likely to have a one-way commuting time of less than 30 min; 3) less than 1% of car-based commuters are likely to have a one-way commuting time of more than 60 min; and 4) the areas suffering a lower level of spatial accessibility of jobs by car are likely to be evenly distributed across Greater Boston. Full article
(This article belongs to the Special Issue Recent Trends in Location Based Services and Science)
Show Figures

Figure 1

Article
On the Right Track: Comfort and Confusion in Indoor Environments
ISPRS Int. J. Geo-Inf. 2020, 9(2), 132; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9020132 - 24 Feb 2020
Cited by 3 | Viewed by 1015
Abstract
Indoor navigation systems are not well adapted to the needs of their users. The route planning algorithms implemented in these systems are usually limited to shortest path calculations or derivatives, minimalizing Euclidian distance. Guiding people along routes that adhere better to their cognitive [...] Read more.
Indoor navigation systems are not well adapted to the needs of their users. The route planning algorithms implemented in these systems are usually limited to shortest path calculations or derivatives, minimalizing Euclidian distance. Guiding people along routes that adhere better to their cognitive processes could ease wayfinding in indoor environments. This paper examines comfort and confusion perception during wayfinding by applying a mixed-method approach. The aforementioned method combined an exploratory focus group and a video-based online survey. From the discussions in the focus group, it could be concluded that indoor wayfinding must be considered at different levels: the local level and the global level. In the online survey, the focus was limited to the local level, i.e., local environmental characteristics. In this online study, the comfort and confusion ratings of multiple indoor navigation situations were analyzed. In general, the results indicate that open spaces and stairs need to be taken into account in the development of a more cognitively-sounding route planning algorithm. Implementing the results in a route planning algorithm could be a valuable improvement of indoor navigation support. Full article
(This article belongs to the Special Issue Recent Trends in Location Based Services and Science)
Show Figures

Figure 1

Article
Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data
ISPRS Int. J. Geo-Inf. 2020, 9(2), 125; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9020125 - 21 Feb 2020
Cited by 4 | Viewed by 1142
Abstract
Social media data analytics is the art of extracting valuable hidden insights from vast amounts of semi-structured and unstructured social media data to enable informed and insightful decision-making. Analysis of social media data has been applied for discovering patterns that may support urban [...] Read more.
Social media data analytics is the art of extracting valuable hidden insights from vast amounts of semi-structured and unstructured social media data to enable informed and insightful decision-making. Analysis of social media data has been applied for discovering patterns that may support urban planning decisions in smart cities. In this paper, Weibo social media data are used to analyze social-geographic human mobility in the CBD area of Shanghai to track citizen’s behavior. Our main motivation is to test the validity of geo-located Weibo data as a source for discovering human mobility and activity patterns. In addition, our goal is to identify important locations in people’s lives with the support of location-based services. The algorithms used are described and the results produced are presented using adequate visualization techniques to illustrate the detected human mobility patterns obtained by the large-scale social media data in order to support smart city planning decisions. The outcome of this research is helpful not only for city planners, but also for business developers who hope to extend their services to citizens. Full article
(This article belongs to the Special Issue Recent Trends in Location Based Services and Science)
Show Figures

Figure 1

Article
Research on Generating an Indoor Landmark Salience Model for Self-Location and Spatial Orientation from Eye-Tracking Data
ISPRS Int. J. Geo-Inf. 2020, 9(2), 97; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9020097 - 04 Feb 2020
Cited by 2 | Viewed by 830
Abstract
Landmarks play an essential role in wayfinding and are closely related to cognitive processes. Eye-tracking data contain massive amounts of information that can be applied to discover the cognitive behaviors during wayfinding; however, little attention has been paid to applying such data to [...] Read more.
Landmarks play an essential role in wayfinding and are closely related to cognitive processes. Eye-tracking data contain massive amounts of information that can be applied to discover the cognitive behaviors during wayfinding; however, little attention has been paid to applying such data to calculating landmark salience models. This study proposes a method for constructing an indoor landmark salience model based on eye-tracking data. First, eye-tracking data are taken to calculate landmark salience for self-location and spatial orientation tasks through partial least squares regression (PLSR). Then, indoor landmark salience attractiveness (visual, semantic and structural) is selected and trained by landmark salience based on the eye-tracking data. Lastly, the indoor landmark salience model is generated by landmark salience attractiveness. Recruiting 32 participants, we designed a laboratory eye-tracking experiment to construct and test the model. Finding 1 proves that our eye-tracking data-based modelling method is more accurate than current weighting methods. Finding 2 shows that significant differences in landmark salience occur between two tasks; thus, it is necessary to generate a landmark salience model for different tasks. Our results can contribute to providing indoor maps for different tasks. Full article
(This article belongs to the Special Issue Recent Trends in Location Based Services and Science)
Show Figures

Figure 1

Article
User Preferences on Route Instruction Types for Mobile Indoor Route Guidance
ISPRS Int. J. Geo-Inf. 2019, 8(11), 482; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8110482 - 25 Oct 2019
Cited by 3 | Viewed by 776
Abstract
Adaptive mobile wayfinding systems are being developed to ease wayfinding in the indoor environment. They present wayfinding information to the user, which is adapted to the context. Wayfinding information can be communicated by using different types of route instructions, such as text, photos, [...] Read more.
Adaptive mobile wayfinding systems are being developed to ease wayfinding in the indoor environment. They present wayfinding information to the user, which is adapted to the context. Wayfinding information can be communicated by using different types of route instructions, such as text, photos, videos, symbols or a combination thereof. The need for a different type of route instruction may vary at decision points, for example because of its complexity. Furthermore, these needs may be different for different user characteristics (e.g., age, gender, level of education). To determine this need for information, an online survey has been executed where participants rated 10 different route instruction types at several decision points in a case study building. Results show that the types with additional text were preferred over those without text. The photo instructions, combined with text, generally received the highest ratings, especially from first-time visitors. 3D simulations were appreciated at complex decision points and by younger people. When text (with symbols) is considered as a route instruction type, it is best used for the start or end instruction. Full article
(This article belongs to the Special Issue Recent Trends in Location Based Services and Science)
Show Figures

Figure 1

Article
Delimitating Urban Commercial Central Districts by Combining Kernel Density Estimation and Road Intersections: A Case Study in Nanjing City, China
ISPRS Int. J. Geo-Inf. 2019, 8(2), 93; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8020093 - 16 Feb 2019
Cited by 9 | Viewed by 1860
Abstract
An urban, commercial central district is often regarded as the heart of a city. Therefore, quantitative research on commercial central districts plays an important role when studying the development and evaluation of urban spatial layouts. However, conventional planar kernel density estimation (KDE) and [...] Read more.
An urban, commercial central district is often regarded as the heart of a city. Therefore, quantitative research on commercial central districts plays an important role when studying the development and evaluation of urban spatial layouts. However, conventional planar kernel density estimation (KDE) and network kernel density estimation (network KDE) do not reflect the fact that the road network density is high in urban, commercial central districts. To solve this problem, this paper proposes a new method (commercial-intersection KDE), which combines road intersections with KDE to identify commercial central districts based on point of interest (POI) data. First, we extracted commercial POIs from Amap (a Chinese commercial, navigation electronic map) based on existing classification standards for urban development land. Second, we calculated the commercial kernel density in the road intersection neighborhoods and used those values as parameters to build a commercial intersection density surface. Finally, we used the three standard deviations method and the commercial center area indicator to differentiate commercial central districts from areas with only commercial intersection density. Testing the method using Nanjing City as a case study, we show that our new method can identify seven municipal, commercial central districts and 26 nonmunicipal, commercial central districts. Furthermore, we compare the results of the traditional planar KDE with those of our commercial-intersection KDE to demonstrate our method’s higher accuracy and practicability for identifying urban commercial central districts and evaluating urban planning. Full article
(This article belongs to the Special Issue Recent Trends in Location Based Services and Science)
Show Figures

Figure 1

Article
Reduction of Map Information Regulates Visual Attention without Affecting Route Recognition Performance
ISPRS Int. J. Geo-Inf. 2018, 7(12), 469; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7120469 - 30 Nov 2018
Cited by 10 | Viewed by 2604
Abstract
Map-based navigation is a diverse task that stands in contradiction to the goal of completeness of web mapping services. As each navigation task is different, it also requires and can dispense with different map information to support effective and efficient wayfinding. Task-oriented reduction [...] Read more.
Map-based navigation is a diverse task that stands in contradiction to the goal of completeness of web mapping services. As each navigation task is different, it also requires and can dispense with different map information to support effective and efficient wayfinding. Task-oriented reduction of the elements displayed in a map may therefore support navigation. In order to investigate effects of map reduction on route recognition and visual attention towards specific map elements, we created maps in which areas offside an inserted route were displayed as transparent. In a route memory experiment, where participants had to memorize routes and match them to routes displayed in following stimuli, these maps were compared to unmodified maps. Eye movement analyses revealed that in the reduced maps, areas offside the route were fixated less often. Route recognition performance was not affected by the map reduction. Our results indicate that task-oriented map reduction may direct visual attention towards relevant map elements at no cost for route recognition. Full article
(This article belongs to the Special Issue Recent Trends in Location Based Services and Science)
Show Figures

Figure 1

Article
Complying with Privacy Legislation: From Legal Text to Implementation of Privacy-Aware Location-Based Services
ISPRS Int. J. Geo-Inf. 2018, 7(11), 442; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7110442 - 13 Nov 2018
Cited by 4 | Viewed by 1701
Abstract
An individual’s location data is very sensitive geoinformation. While its disclosure is necessary, e.g., to provide location-based services (LBS), it also facilitates deep insights into the lives of LBS users as well as various attacks on these users. Location privacy threats can be [...] Read more.
An individual’s location data is very sensitive geoinformation. While its disclosure is necessary, e.g., to provide location-based services (LBS), it also facilitates deep insights into the lives of LBS users as well as various attacks on these users. Location privacy threats can be mitigated through privacy regulations such as the General Data Protection Regulation (GDPR), which was introduced recently and harmonises data privacy laws across Europe. While the GDPR is meant to protect users’ privacy, the main problem is that it does not provide explicit guidelines for designers and developers about how to build systems that comply with it. In order to bridge this gap, we systematically analysed the legal text, carried out expert interviews, and ran a nine-week-long take-home study with four developers. We particularly focused on user-facing issues, as these have received little attention compared to technical issues. Our main contributions are a list of aspects from the legal text of the GDPR that can be tackled at the user interface level and a set of guidelines on how to realise this. Our results can help service providers, designers and developers of applications dealing with location information from human users to comply with the GDPR. Full article
(This article belongs to the Special Issue Recent Trends in Location Based Services and Science)
Show Figures

Figure 1

Back to TopTop