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Spatial Data Science
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Spatial Data Science

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

Deadline for manuscript submissions: closed (31 March 2020) | Viewed by 34287

Special Issue Editors

Dr. 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; spatial data science; remote sensing; information systems
Special Issues, Collections and Topics in MDPI journals
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, Collections and Topics in MDPI journals
Dr. 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 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

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

Published Papers (7 papers)

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Editorial

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5 pages, 200 KiB  
Editorial
Spatial Data Science
by Fernando Bacao, Maribel Yasmina Santos and Martin Behnisch
ISPRS Int. J. Geo-Inf. 2020, 9(7), 428; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9070428 - 08 Jul 2020
Cited by 5 | Viewed by 2863
Abstract
The field of data science has had a significant impact in both academia and industry, and with good reason [...] Full article
(This article belongs to the Special Issue Spatial Data Science)

Research

Jump to: Editorial

14 pages, 8590 KiB  
Article
Spatio-Temporal Analysis of Intense Convective Storms Tracks in a Densely Urbanized Italian Basin
by Matteo Sangiorgio and Stefano Barindelli
ISPRS Int. J. Geo-Inf. 2020, 9(3), 183; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9030183 - 24 Mar 2020
Cited by 8 | Viewed by 8785
Abstract
Intense convective storms usually produce large rainfall volumes in short time periods, increasing the risk of floods and causing damages to population, buildings, and infrastructures. In this paper, we propose a framework to couple visual and statistical analyses of convective thunderstorms at the [...] Read more.
Intense convective storms usually produce large rainfall volumes in short time periods, increasing the risk of floods and causing damages to population, buildings, and infrastructures. In this paper, we propose a framework to couple visual and statistical analyses of convective thunderstorms at the basin scale, considering both the spatial and temporal dimensions of the process. The dataset analyzed in this paper contains intense convective events that occurred in seven years (2012–2018) in the Seveso-Olona-Lambro basin (North of Italy). The data has been acquired by MeteoSwiss using the Thunderstorm Radar Tracking (TRT) algorithm. The results show that the most favorable conditions for the formation of convective events occur in the early afternoon and during summertime, confirming the key role of the temperature in atmospheric convection. The orography emerged as a driver for convection, which takes place more frequently in mountain areas. The storm paths analysis shows that the predominant direction is from South-West to North-East. Considering storm duration, long-lasting events reach higher values of radar reflectivity and cover more extended areas than short-lasting ones. The results obtained can be exploited for many practical applications including nowcasting, alert systems, and sensors deployment. Full article
(This article belongs to the Special Issue Spatial Data Science)
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14 pages, 4799 KiB  
Article
Analyzing Road Coverage of Public Vehicles According to Number and Time Period for Installation of Road Inspection Systems
by Takehiro Kashiyama, Yoshihide Sekimoto, Toshikazu Seto and Ko Ko Lwin
ISPRS Int. J. Geo-Inf. 2020, 9(3), 161; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9030161 - 10 Mar 2020
Cited by 3 | Viewed by 2307
Abstract
Shortages of engineers and financial resources have made it difficult for municipalities to identify and address problems with aging road infrastructures. To resolve these problems, numerous studies have focused on automating road inspection, including a study in which we developed a smartphone-based road [...] Read more.
Shortages of engineers and financial resources have made it difficult for municipalities to identify and address problems with aging road infrastructures. To resolve these problems, numerous studies have focused on automating road inspection, including a study in which we developed a smartphone-based road inspection system. For efficient operation of the system, it is necessary to understand the usage of vehicles in which the system will be installed. In this study, we analyzed the usage of public vehicles with long-term global positioning system (GPS) probe data collected from public vehicles operating in Kakogawa city and Fujisawa city in Japan. As a result, we discovered that local governments of the same size have similar tendencies in terms of road coverage. Moreover, we found that installing road inspection systems on only a few public vehicles can cover the entire road inspection area. We anticipate that these results will assist local governments in making informed decisions during the system introduction process and provide an indicator of the accuracy required for road inspection systems to future researchers. Full article
(This article belongs to the Special Issue Spatial Data Science)
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18 pages, 2521 KiB  
Article
Spatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods
by Wolfgang B. Hamer, Tim Birr, Joseph-Alexander Verreet, Rainer Duttmann and Holger Klink
ISPRS Int. J. Geo-Inf. 2020, 9(1), 44; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9010044 - 15 Jan 2020
Cited by 16 | Viewed by 4388
Abstract
Real-time identification of the occurrence of dangerous pathogens is of crucial importance for the rapid execution of countermeasures. For this purpose, spatial and temporal predictions of the spread of such pathogens are indispensable. The R package papros developed by the authors offers an [...] Read more.
Real-time identification of the occurrence of dangerous pathogens is of crucial importance for the rapid execution of countermeasures. For this purpose, spatial and temporal predictions of the spread of such pathogens are indispensable. The R package papros developed by the authors offers an environment in which both spatial and temporal predictions can be made, based on local data using various deterministic, geostatistical regionalisation, and machine learning methods. The approach is presented using the example of a crops infection by fungal pathogens, which can substantially reduce the yield if not treated in good time. The situation is made more difficult by the fact that it is particularly difficult to predict the behaviour of wind-dispersed pathogens, such as powdery mildew (Blumeria graminis f. sp. tritici). To forecast pathogen development and spatial dispersal, a modelling process scheme was developed using the aforementioned R package, which combines regionalisation and machine learning techniques. It enables the prediction of the probability of yield- relevant infestation events for an entire federal state in northern Germany at a daily time scale. To run the models, weather and climate information are required, as is knowledge of the pathogen biology. Once fitted to the pathogen, only weather and climate information are necessary to predict such events, with an overall accuracy of 68% in the case of powdery mildew at a regional scale. Thereby, 91% of the observed powdery mildew events are predicted. Full article
(This article belongs to the Special Issue Spatial Data Science)
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23 pages, 33648 KiB  
Article
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
by Disheng Yi, Jing Yang, Jingjing Liu, Yusi Liu and Jing Zhang
ISPRS Int. J. Geo-Inf. 2019, 8(12), 555; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8120555 - 03 Dec 2019
Cited by 19 | Viewed by 3292
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)
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25 pages, 10378 KiB  
Article
Simplification and Detection of Outlying Trajectories from Batch and Streaming Data Recorded in Harsh Environments
by Iq Reviessay Pulshashi, Hyerim Bae, Hyunsuk Choi, Seunghwan Mun and Riska Asriana Sutrisnowati
ISPRS Int. J. Geo-Inf. 2019, 8(6), 272; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8060272 - 12 Jun 2019
Cited by 2 | Viewed by 3325
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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23 pages, 8777 KiB  
Article
From Motion Activity to Geo-Embeddings: Generating and Exploring Vector Representations of Locations, Traces and Visitors through Large-Scale Mobility Data
by Alessandro Crivellari and Euro Beinat
ISPRS Int. J. Geo-Inf. 2019, 8(3), 134; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8030134 - 08 Mar 2019
Cited by 34 | Viewed by 6678
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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