Special Issue "Spatial Data Science"

Special Issue Editors

Assoc. Prof. Fernando Bação
E-Mail Website
Guest Editor
NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal
Interests: data science; data mining; information systems; business analytics
Dr. Martin Behnisch
E-Mail Website
Guest Editor
Leibniz Institute of Ecological Urban and Regional Development, Dresden, Saxony, Germany
Interests: spatial analysis; geographic knowledge discovery; urban data mining; spatial science; quantitative geography; multivariate data analysis; research on building stocks and land consumption
Special Issues and Collections in MDPI journals
Assoc. Prof. Maribel Yasmina Santos
E-Mail Website
Guest Editor
University of Minho, Department of Information Systems, Campus de Azurém, 4800-058 Guimarães, Portugal
Interests: geospatial big data analytics; spatial databases; spatial decision support systems; spatial data warehousing; spatial online analytical processing

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 (3 papers)

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Research

Open AccessArticle
Quantitative Identification of Urban Functions with Fishers’ Exact Test and POI Data Applied in Classifying Urban Districts: A Case Study within the Sixth Ring Road in Beijing
ISPRS Int. J. Geo-Inf. 2019, 8(12), 555; https://doi.org/10.3390/ijgi8120555 - 03 Dec 2019
Abstract
Urban areas involve different functions that attract individuals and fit personal needs. Understanding the distribution and combination of these functions in a specific district is significant for urban development in cities. Many researchers have already studied the methods of identifying the dominant functions [...] Read more.
Urban areas involve different functions that attract individuals and fit personal needs. Understanding the distribution and combination of these functions in a specific district is significant for urban development in cities. Many researchers have already studied the methods of identifying the dominant functions in a district. However, the degree of collection and the representativeness of a function in a district are controlled not only by its number in the district but also by the number outside this district and a number of other functions. Thus, this study proposed a quantitative method to identify urban functions, using Fisher’s exact test and point of interest (POI) data, applied in determining the urban districts within the Sixth Ring Road in Beijing. To begin with, we defined a functional score based on three statistical features: the p-value, odds-ratio, and the frequency of each POI tag. The p-value and odds-ratio resulted from a statistical significance test, the Fisher’s exact test. Next, we ran a k-modes clustering algorithm to classify all urban districts in accordance with the score of each function and their combination in one district, and then we detected four different groups, namely, Work and Tourism Mixed-developed district, Mixed-developed Residential district, Developing Greenland district, and Mixed Recreation district. Compared with the other identifying methods, our method had good performance in identifying functions, except for transportation. In addition, the Coincidence Degree was used to evaluate the accuracy of classification. In our study, the total accuracy of identifying urban districts was 83.7%. Overall, the proposed identifying method provides an additional method to the various methods used to identify functions. Additionally, analyzing urban spatial structure can be simpler, which has certain theoretical and practical value for urban geospatial planning. Full article
(This article belongs to the Special Issue Spatial Data Science)
Open AccessArticle
Simplification and Detection of Outlying Trajectories from Batch and Streaming Data Recorded in Harsh Environments
ISPRS Int. J. Geo-Inf. 2019, 8(6), 272; https://doi.org/10.3390/ijgi8060272 - 12 Jun 2019
Abstract
Analysis of trajectory such as detection of an outlying trajectory can produce inaccurate results due to the existence of noise, an outlying point-locations that can change statistical properties of the trajectory. Some trajectories with noise are repairable by noise filtering or by trajectory-simplification. [...] Read more.
Analysis of trajectory such as detection of an outlying trajectory can produce inaccurate results due to the existence of noise, an outlying point-locations that can change statistical properties of the trajectory. Some trajectories with noise are repairable by noise filtering or by trajectory-simplification. We herein propose the application of a trajectory-simplification approach in both batch and streaming environments, followed by benchmarking of various outlier-detection algorithms for detection of outlying trajectories from among simplified trajectories. Experimental evaluation in a case study using real-world trajectories from a shipyard in South Korea shows the benefit of the new approach. Full article
(This article belongs to the Special Issue Spatial Data Science)
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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 - 08 Mar 2019
Cited by 1
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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