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Intelligent Systems Based on Open and Crowdsourced Location Data

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

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 9324

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Special Issue Editors

Dr. Fernando Terroso-Sáenz

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Guest Editor

Polytechnic School, Catholic University of Murcia, Campus de los Jerónimos, 30107 Guadalupe, Murcia, Spain
Interests: smart cities; urban computing; smart mobility; machine learning; volunteer geographic information

Dr. Andrés Muñoz

E-Mail Website
Guest Editor

Polytechnic School, Catholic University of Murcia, Campus de los Jerónimos, 30107 Guadalupe, Murcia, Spain
Interests: knowledge engineering; semantic web; ambient intelligence; intelligent environments; context-awareness
Special Issues, Collections and Topics in MDPI journals

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 submissions that pass pre-check are 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 1700 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 (3 papers)

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Research

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23 pages, 3092 KiB

Open AccessArticle

An End-to-End Point of Interest (POI) Conflation Framework

by Raymond Low, Zeynep Duygu Tekler and Lynette Cheah

ISPRS Int. J. Geo-Inf. 2021, 10(11), 779; https://doi.org/10.3390/ijgi10110779 - 15 Nov 2021

Cited by 29 | Viewed by 2818

Abstract

Point of interest (POI) data serves as a valuable source of semantic information for places of interest and has many geospatial applications in real estate, transportation, and urban planning. With the availability of different data sources, POI conflation serves as a valuable technique for enriching data quality and coverage by merging the POI data from multiple sources. This study proposes a novel end-to-end POI conflation framework consisting of six steps, starting with data procurement, schema standardisation, taxonomy mapping, POI matching, POI unification, and data verification. The feasibility of the proposed framework was demonstrated in a case study conducted in the eastern region of Singapore, where the POI data from five data sources was conflated to form a unified POI dataset. Based on the evaluation conducted, the resulting unified dataset was found to be more comprehensive and complete than any of the five POI data sources alone. Furthermore, the proposed approach for identifying POI matches between different data sources outperformed all baseline approaches with a matching accuracy of 97.6% with an average run time below 3 min when matching over 12,000 POIs to result in 8699 unique POIs, thereby demonstrating the framework’s scalability for large scale implementation in dense urban contexts. Full article

(This article belongs to the Special Issue Intelligent Systems Based on Open and Crowdsourced Location Data)

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14 pages, 1200 KiB

Open AccessEditor’s ChoiceArticle

OurPlaces: Cross-Cultural Crowdsourcing Platform for Location Recommendation Services

by Luong Vuong Nguyen, Jason J. Jung and Myunggwon Hwang

ISPRS Int. J. Geo-Inf. 2020, 9(12), 711; https://doi.org/10.3390/ijgi9120711 - 27 Nov 2020

Cited by 13 | Viewed by 2762

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); (

i i

) cognition (how people [...] Read more.

i i

) cognition (how people have experienced these places); and (

i i i

) 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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Review

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30 pages, 2202 KiB

Open AccessReview

The Architecture of Mass Customization-Social Internet of Things System: Current Research Profile

by Zixin Dou, Yanming Sun, Zhidong Wu, Tao Wang, Shiqi Fan and Yuxuan Zhang

ISPRS Int. J. Geo-Inf. 2021, 10(10), 653; https://doi.org/10.3390/ijgi10100653 - 28 Sep 2021

Cited by 13 | Viewed by 2886

Abstract

In the era of big data, mass customization (MC) systems are faced with the complexities associated with information explosion and management control. Thus, it has become necessary to integrate the mass customization system and Social Internet of Things, in order to effectively connecting customers with enterprises. We should not only allow customers to participate in MC production throughout the whole process, but also allow enterprises to control all links throughout the whole information system. To gain a better understanding, this paper first describes the architecture of the proposed system from organizational and technological perspectives. Then, based on the nature of the Social Internet of Things, the main technological application of the mass customization–Social Internet of Things (MC–SIOT) system is introduced in detail. On this basis, the key problems faced by the mass customization–Social Internet of Things system are listed. Our findings are as follows: (1) MC–SIOT can realize convenient information queries and clearly understand the user’s intentions; (2) the system can predict the changing relationships among different technical fields and help enterprise R&D personnel to find technical knowledge; and (3) it can interconnect deep learning technology and digital twin technology to better maintain the operational state of the system. However, there exist some challenges relating to data management, knowledge discovery, and human–computer interaction, such as data quality management, few data samples, a lack of dynamic learning, labor consumption, and task scheduling. Therefore, we put forward possible improvements to be assessed, as well as privacy issues and emotional interactions to be further discussed, in future research. Finally, we illustrate the behavior and evolutionary mechanism of this system, both qualitatively and quantitatively. This provides some idea of how to address the current issues pertaining to mass customization systems. Full article

(This article belongs to the Special Issue Intelligent Systems Based on Open and Crowdsourced Location Data)

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