Special Issue "Spatial Data Science"

Special Issue Editors

Guest Editor
Assoc. Prof. Fernando Bação

NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal
Website | E-Mail
Interests: data science; data mining; information systems; business analytics
Guest Editor
Dr. Martin Behnisch

Leibniz Institute of Ecological Urban and Regional Development, Dresden, Saxony, Germany
Website | E-Mail
Interests: spatial analysis; geographic knowledge discovery; urban data mining; spatial science; quantitative geography; multivariate data analysis; research on building stocks and land consumption
Guest Editor
Assoc. Prof. Maribel Yasmina Santos

School of Engineering, Department of Information Systems, ALGORITMI Research Centre, Portugal
Website | E-Mail
Interests: business intelligence and analytics; data mining; data warehousing; spatial data mining; spatio-temporal data models; spatial reasoning

Special Issue Information

Dear Colleagues,

The burgeoning field of Data Science has had a significant impact in both academia and industry, and with good reason. The ability to make use of large amounts of data to find solutions for pressing problems in society, environment and business, constitutes both an opportunity and a challenge. Data is our best prospect to significantly improve our understanding of the world, ease the attrition in human/environment interaction, optimize resource allocation and mitigate human suffering and deprivation. Nevertheless, data, especially big data, pose difficult research challenges that need to be met and overcome, in order to bring these promises to fruition. To address these challenges is the mission of Data Science. Different types of data require specific tools methods and different analysis contexts require different analytic approaches. Spatial data science is concerned with research and problems where location is a central component of the problem. Spatial data science expertise is central in many practical problems, such as environmental management, public health, crime, remote sensing, just to mention a few. Significant progress has been made in the last few years, often driven by the industry. Academia needs to support this progress, contributing with general solutions and fundamental principles that can be of use in different contexts.

Assoc. Prof. Fernando Bação
Assoc. Prof. Maribel Yasmina Santos
Dr. Martin Behnisch
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

  • Spatial data science
  • Big Data
  • Geoinformation
  • GIScience
  • Geographic Data Mining
  • Geocomputation
  • Smart Cities
  • Remote Sensing

Published Papers (1 paper)

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Research

Open AccessArticle From Motion Activity to Geo-Embeddings: Generating and Exploring Vector Representations of Locations, Traces and Visitors through Large-Scale Mobility Data
ISPRS Int. J. Geo-Inf. 2019, 8(3), 134; https://doi.org/10.3390/ijgi8030134
Received: 29 January 2019 / Revised: 23 February 2019 / Accepted: 4 March 2019 / Published: 8 March 2019
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Abstract
The rapid growth of positioning technology allows tracking motion between places, making trajectory recordings an important source of information about place connectivity, as they map the routes that people commonly perform. In this paper, we utilize users’ motion traces to construct a behavioral [...] Read more.
The rapid growth of positioning technology allows tracking motion between places, making trajectory recordings an important source of information about place connectivity, as they map the routes that people commonly perform. In this paper, we utilize users’ motion traces to construct a behavioral representation of places based on how people move between them, ignoring geographical coordinates and spatial proximity. Inspired by natural language processing techniques, we generate and explore vector representations of locations, traces and visitors, obtained through an unsupervised machine learning approach, which we generically named motion-to-vector (Mot2vec), trained on large-scale mobility data. The algorithm consists of two steps, the trajectory pre-processing and the Word2vec-based model building. First, mobility traces are converted into sequences of locations that unfold in fixed time steps; then, a Skip-gram Word2vec model is used to construct the location embeddings. Trace and visitor embeddings are finally created combining the location vectors belonging to each trace or visitor. Mot2vec provides a meaningful representation of locations, based on the motion behavior of users, defining a direct way of comparing locations’ connectivity and providing analogous similarity distributions for places of the same type. In addition, it defines a metric of similarity for traces and visitors beyond their spatial proximity and identifies common motion behaviors between different categories of people. Full article
(This article belongs to the Special Issue Spatial Data Science)
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