Special Issue "Geovisualization and Geo Visual Knowledge Discovery"

Special Issue Editors

Prof. Dr. Boris Kovalerchuk
E-Mail Website
Guest Editor
Department of Computer Science, Central Washington University, Ellensburg, WA 98926, USA
Interests: geospatial data analysis; geospatial visualization; machine learning; visual knowledge discovery; geospatila data fusion and conflation
Dr. Eugene Levin
E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering at Michigan Tech, Houghton, MI, 49931,USA
Interests: geospatial big data; geospatial VR/AR/MR; 3D visualisation; integrated geospatial technologies data and systems; human–computer simbiosis; visual analytivs

Special Issue Information

Dear Colleagues,

Geovisualization has been in the forefront of GIS for years, with the focus on visualizing already discovered geospatial knowledge. The current trend is moving this domain towards integrated visual knowledge discovery by combining new developments in geospatial visualization, data fusion, and machine learning for multiple geospatial domains. This Special Issue invites papers in all these areas and their integration.

The specific topics of interest include but are not limited to the following:

  • Geospatial visualization;
  • Visual analytics and knowledge discovery;
  • Visualization of high-dimensional geospatial data without loss of information;
  • Interpretable machine learning to support geospatial big data application scenarios;
  • Geospatial visualization to support smart cities;
  • Geospatial data visualization to support the Internet of Things (IoT);
  • Geospatial data processing to support 3D printed construction;
  • Geospatial virtual, augmented, and mixed reality;
  • Human–computer symbiosis to support efficient geospatial visualization;
  • Geosensorics to support 4D geospatial analytics;
  • Geospatial visualization in support of autonomous cars navigation;
  • Geospatial visual analytics for GEOINT;
  • Geospatial aspects of robotic agriculture application scenarios;
  • Visual aids to support geospatial education in civil engineering, intel, and construction.

Prof. Dr. Boris Kovalerchuk
Dr. Eugene Levin
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 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.

Published Papers (3 papers)

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Research

Open AccessArticle
Geospatial Virtual Reality for Cyberlearning in the Field of Topographic Surveying: Moving Towards a Cost-Effective Mobile Solution
ISPRS Int. J. Geo-Inf. 2020, 9(7), 433; https://doi.org/10.3390/ijgi9070433 - 10 Jul 2020
Cited by 3 | Viewed by 1075
Abstract
In spite of the tremendous success in artificial intelligence technology and a high level of automation in geospatial data obtaining processes, there is still a need for topographical field data collection by professional surveyors. Understanding terrain topology and topography is a cognitive skill [...] Read more.
In spite of the tremendous success in artificial intelligence technology and a high level of automation in geospatial data obtaining processes, there is still a need for topographical field data collection by professional surveyors. Understanding terrain topology and topography is a cognitive skill set that has to be demonstrated by geospatial Subject Matter Experts (SME) for the productive work in the topographic surveying field. For training of the mentioned above skillset, one has to be exposed to the theory and must also practice with surveying instruments in field conditions. The challenge of any surveying/geospatial engineering workforce training is to expose students to field conditions which might be limited due to equipment expenses and meteorological conditions that prevent good data collection. To meet this challenge, the Integrated Geospatial Technology research group is working on a geospatial virtual reality (VR) project which encompasses the following components: (a) immersive visualization of terrain; (b) virtual total station instrument; (c) virtual surveyor with reflector installed on the virtual rod. The application scenario of the technology we are working with has the following stages: (1) student is installing total station on the optimal location; (2) students move virtual surveyor on the sampling points they consider to be important (3) contours are generated and displayed in 3D being superimposed on 3D terrain; (4) accuracy of terrain modeling is observable and measurable by comparing the sampling model with initial one. Full article
(This article belongs to the Special Issue Geovisualization and Geo Visual Knowledge Discovery)
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Open AccessArticle
Analysis of Green Spaces by Utilizing Big Data to Support Smart Cities and Environment: A Case Study About the City Center of Shanghai
ISPRS Int. J. Geo-Inf. 2020, 9(6), 360; https://doi.org/10.3390/ijgi9060360 - 01 Jun 2020
Cited by 1 | Viewed by 980
Abstract
Green areas or parks are the best way to encourage people to take part in physical exercise. Traditional techniques of researching the attractiveness of green parks, such as surveys and questionnaires, are naturally time consuming and expensive, with less transferable outcomes and only [...] Read more.
Green areas or parks are the best way to encourage people to take part in physical exercise. Traditional techniques of researching the attractiveness of green parks, such as surveys and questionnaires, are naturally time consuming and expensive, with less transferable outcomes and only site-specific findings. This research provides a factfinding study by means of location-based social network (LBSN) data to gather spatial and temporal patterns of green park visits in the city center of Shanghai, China. During the period from July 2014 to June 2017, we examined the spatiotemporal behavior of visitors in 71 green parks in Shanghai. We conducted an empirical investigation through kernel density estimation (KDE) and relative difference methods on the effects of green spaces on public behavior in Shanghai, and our main categories of findings are as follows: (i) check-in distribution of visitors in different green spaces, (ii) users’ transition based on the hours of a day, (iii) famous parks in the study area based upon the number of check-ins, and (iv) gender difference among green park visitors. Furthermore, the purpose of obtaining these outcomes can be utilized in urban planning of a smart city for green environment according to the preferences of visitors. Full article
(This article belongs to the Special Issue Geovisualization and Geo Visual Knowledge Discovery)
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Open AccessArticle
Location-Based Social Network’s Data Analysis and Spatio-Temporal Modeling for the Mega City of Shanghai, China
ISPRS Int. J. Geo-Inf. 2020, 9(2), 76; https://doi.org/10.3390/ijgi9020076 - 29 Jan 2020
Cited by 6 | Viewed by 1078
Abstract
The aim of the current study is to analyze and extract the useful patterns from Location-Based Social Network (LBSN) data in Shanghai, China, using different temporal and spatial analysis techniques, along with specific check-in venue categories. This article explores the applications of LBSN [...] Read more.
The aim of the current study is to analyze and extract the useful patterns from Location-Based Social Network (LBSN) data in Shanghai, China, using different temporal and spatial analysis techniques, along with specific check-in venue categories. This article explores the applications of LBSN data by examining the association between time, frequency of check-ins, and venue classes, based on users’ check-in behavior and the city’s characteristics. The information regarding venue classes is created and categorized by using the nature of physical locations. We acquired the geo-location information from one of the most famous Chinese microblogs called Sina-Weibo (Weibo). The extracted data are translated into the Geographical Information Systems (GIS) format, and after analysis the results are presented in the form of statistical graphs, tables, and spatial heatmaps. SPSS is used for temporal analysis, and Kernel Density Estimation (KDE) is applied based on users’ check-ins with the help of ArcMap and OpenStreetMap for spatial analysis. The findings show various patterns, including more frequent use of LBSN while visiting entertainment and shopping locations, a substantial number of check-ins from educational institutions, and that the density extends to suburban areas mainly because of educational institutions and residential areas. Through analytical results, the usage patterns based on hours of the day, days of the week, and for an entire six months, including by gender, venue category, and frequency distribution of the classes, as well as check-in density all over Shanghai city, are thoroughly demonstrated. Full article
(This article belongs to the Special Issue Geovisualization and Geo Visual Knowledge Discovery)
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