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

Special Issue Editors

Prof. Dr. Georg Gartner
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
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 1000 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 (8 papers)

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Research

Open AccessArticle
Uber Movement Data: A Proxy for Average One-way Commuting Times by Car
ISPRS Int. J. Geo-Inf. 2020, 9(3), 184; https://doi.org/10.3390/ijgi9030184 - 24 Mar 2020
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)
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Open AccessArticle
On the Right Track: Comfort and Confusion in Indoor Environments
ISPRS Int. J. Geo-Inf. 2020, 9(2), 132; https://doi.org/10.3390/ijgi9020132 - 24 Feb 2020
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)
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Open AccessArticle
Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data
ISPRS Int. J. Geo-Inf. 2020, 9(2), 125; https://doi.org/10.3390/ijgi9020125 - 21 Feb 2020
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)
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Open AccessArticle
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://doi.org/10.3390/ijgi9020097 - 04 Feb 2020
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)
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Open AccessArticle
User Preferences on Route Instruction Types for Mobile Indoor Route Guidance
ISPRS Int. J. Geo-Inf. 2019, 8(11), 482; https://doi.org/10.3390/ijgi8110482 - 25 Oct 2019
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)
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Open AccessArticle
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://doi.org/10.3390/ijgi8020093 - 16 Feb 2019
Cited by 7
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)
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Open AccessArticle
Reduction of Map Information Regulates Visual Attention without Affecting Route Recognition Performance
ISPRS Int. J. Geo-Inf. 2018, 7(12), 469; https://doi.org/10.3390/ijgi7120469 - 30 Nov 2018
Cited by 4
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)
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Open AccessArticle
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://doi.org/10.3390/ijgi7110442 - 13 Nov 2018
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)
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