Special Issue "Intelligent Systems Based on Open and Crowdsourced Location Data"

Special Issue Editors

Dr. Fernando Terroso-Sáenz
Website
Guest Editor
Polytechnic School, Catholic University of Murcia, Campus de los Jerónimos, Guadalupe 30107, Murcia, Spain
Interests: smart cities; urban computing; smart mobility; machine learning; volunteer geographic information
Dr. Andrés Muñoz
Website
Guest Editor
Polytechnic School, Catholic University of Murcia, Campus de los Jerónimos, Guadalupe 30107, Murcia, Spain
Interests: knowledge engineering; semantic web; ambient intelligence; intelligent environments; context-awareness
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Special Issue Information

Dear Colleagues,

The last decade has witnessed the dawn of personal mobile contrivances as the center of our digital life. In that sense, manufacturers have greatly empowered such devices with new and more advanced sensing features. One clear example of this enrichment is the fact that mobile devices are now commonly equipped with different outdoor and indoor positioning technologies (e.g., GPS, RFID or Bluetooth). This ubiquity of location-aware personal devices has led users to generate an unprecedented amount of spati-temporal data. Furthermore, all these data can be easily hosted and shared in different crowdsourcing platforms such as online social networks like Twitter or collaborative applications like OpenStreetMap. At the same time, the Open Data movement encourages public and private institutions to publish their data freely and so that they are available to anyone. In an urban scope, this has released a huge amount of contextual data related to cities’ infrastructure, services, and population.

This wealth of open and crowdsourced location data clearly enables the development of an ecosystem of new, innovative, and cost-effective systems. Applications in smart mobility, smart tourism or smart marketing are some of the fields where these systems can create outstanding opportunities. However, there is lack of end-to-end solutions able to smoothly integrate, fuse, process, and analyze both types of data to extract meaningful and functional knowledge. This way, the aforementioned ecosystem is still in its early stage.

This Special Issue will promote the use of intelligent techniques and models to come up with solutions that actually profit from open and crowdsourced location data in many different perspectives, ranging from data management to machine learning fields. All in all, the Special Issue will offer the academic and industrial communities a way to share their different experiences and challenges in this fascinating field.

Areas of interest include but are not limited to the following ones:

  • Smart mobility;
  • Smart tourism;
  • Smart marketing;
  • Open governance;
  • Fusion techniques for user-generated data;
  • Security solutions for crowdsensing platforms;
  • Land-use discovery mechanisms;
  • Information models for crowdsensing and open data;
  • Recommendation systems;
  • Machine learning for volunteered geographic information;
  • Big Data solutions for open and crowdsensed environments;
  • Internet of Things (IoT) enablers.

Dr. Fernando Terroso-Sáenz
Dr Andrés Muñoz
Guest Editor

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

  • crowdsensing
  • open data
  • location data
  • data fusion
  • machine learning
  • information models

Published Papers (1 paper)

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Research

Open AccessArticle
OurPlaces: Cross-Cultural Crowdsourcing Platform for Location Recommendation Services
ISPRS Int. J. Geo-Inf. 2020, 9(12), 711; https://doi.org/10.3390/ijgi9120711 - 27 Nov 2020
Abstract
This paper presents a cross-cultural crowdsourcing platform, called OurPlaces, where people from different cultures can share their spatial experiences. We built a three-layered architecture composed of: (i) places (locations where people have visited); (ii) cognition (how people [...] Read more.
This paper presents a cross-cultural crowdsourcing platform, called OurPlaces, where people from different cultures can share their spatial experiences. We built a three-layered architecture composed of: (i) places (locations where people have visited); (ii) cognition (how people have experienced these places); and (iii) users (those who have visited these places). Notably, cognition is represented as a paring of two similar places from different cultures (e.g., Versailles and Gyeongbokgung in France and Korea, respectively). As a case study, we applied the OurPlaces platform to a cross-cultural tourism recommendation system and conducted a simulation using a dataset collected from TripAdvisor. The tourist places were classified into four types (i.e., hotels, restaurants, shopping malls, and attractions). In addition, user feedback (e.g., ratings, rankings, and reviews) from various nationalities (assumed to be equivalent to cultures) was exploited to measure the similarities between tourism places and to generate a cognition layer on the platform. To demonstrate the effectiveness of the OurPlaces-based system, we compared it with a Pearson correlation-based system as a baseline. The experimental results show that the proposed system outperforms the baseline by 2.5% and 4.1% in the best case in terms of MAE and RMSE, respectively. Full article
(This article belongs to the Special Issue Intelligent Systems Based on Open and Crowdsourced Location Data)
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