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IJGI
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Advances in Computational Approaches for Spatial Analysis and Modeling
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Advances in Computational Approaches for Spatial Analysis and Modeling

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 7005

Special Issue Editors

Dr. Wenwu Tang

E-Mail Website
Guest Editor
Center for Applied Geographic Information Science and Department of Geography and Earth Sciences, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, USA
Interests: GIS and spatial analysis and modeling; agent-based models and spatiotemporal simulation; cyberinfrastructure and high-performance computing; complex adaptive spatial systems; land use and land cover change
Special Issues, Collections and Topics in MDPI journals
Dr. Eric Delmelle

E-Mail Website
Guest Editor
Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
Interests: persistent organic pollutants; environmental causes of human disease; air pollution; diabetes; cardiovascular disease
Special Issues, Collections and Topics in MDPI journals
Dr. Minrui Zheng

E-Mail Website
Guest Editor
School of Public Adminstration and Policy, Renmin University of China, No. 59 Zhongguancun Street, Haidian District, Beijing 100872, China
Interests: GIScience; spatial analysis and modeling; machine learning algorithms; cyberinfrastructure and high-performance computing; spatial econometrics; web GIS
Ms. Yu Lan

E-Mail
Assistant Guest Editor
Department of Geography and Earth Sciences, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, USA
Interests: GIScience; geospatial web; geovisualization; geo-big data analysis; epidemiology

Special Issue Information

Dear Colleagues,

Spatial analysis and modeling (SAM) serves as a powerful tool for understanding the complexity of geospatial phenomena. With the availability of high-resolution geospatial data and the increasing complexities of geospatial analytics approaches, the resolution of geospatial problems using these data and approaches poses significant computational challenges.

The past decades have witnessed advances in both computing and modeling performance by advanced computational approaches. These advanced computational approaches comprise machine learning algorithms (e.g., artificial neural networks, random forests, evolutionary algorithms, and swarm intelligence) and cyberinfrastructure (e.g., high-performance computing, cloud computing, and web technologies). With their powerful and intellectual abilities, advanced computational approaches for SAM have gained increasing attention from researchers interested in the study of spatially explicit phenomena.

This Special Issue originates from the AAG Annual Meeting session of “Symposium on Frontiers in CyberGIS and Geospatial Data Science: Advances in Computational Approaches for Spatial Analysis and Modeling”, held in Denver, CO, USA, on April 6–10, 2020. This session aims to gather researchers sharing their experiences of advanced computational approaches to the SAM community. While papers in this section are invited to this Special Issue, other original works aligned with this topic are highly welcomed.

Relevant topics include, but not limited to:

  • Applications of artificial intelligence techniques
  • Applications of cyberinfrastructure
  • Geovisualization and geovisual analytics
  • Innovative algorithms for spatial analysis and modeling
  • Address challenges in big geospatial data
  • New approaches or improvement of existing computational approaches

Dr. Wenwu Tang
Dr.  Eric Delmelle
Dr. Minrui Zheng
Ms. Yu Lan
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

  • spatial analysis and modeling
  • geocomputation
  • big data analytics
  • artificial intelligence and machine learning
  • cyberinfrastructure
  • geovisualization
  • algorithms

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Published Papers (2 papers)

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Research

20 pages, 3597 KiB  
Article
A CUDA-Based Parallel Geographically Weighted Regression for Large-Scale Geographic Data
by Dongchao Wang, Yi Yang, Agen Qiu, Xiaochen Kang, Jiakuan Han and Zhengyuan Chai
ISPRS Int. J. Geo-Inf. 2020, 9(11), 653; https://doi.org/10.3390/ijgi9110653 - 30 Oct 2020
Cited by 10 | Viewed by 3249
Abstract
Geographically weighted regression (GWR) introduces the distance weighted kernel function to examine the non-stationarity of geographical phenomena and improve the performance of global regression. However, GWR calibration becomes critical when using a serial computing mode to process large volumes of data. To address [...] Read more.
Geographically weighted regression (GWR) introduces the distance weighted kernel function to examine the non-stationarity of geographical phenomena and improve the performance of global regression. However, GWR calibration becomes critical when using a serial computing mode to process large volumes of data. To address this problem, an improved approach based on the compute unified device architecture (CUDA) parallel architecture fast-parallel-GWR (FPGWR) is proposed in this paper to efficiently handle the computational demands of performing GWR over millions of data points. FPGWR is capable of decomposing the serial process into parallel atomic modules and optimizing the memory usage. To verify the computing capability of FPGWR, we designed simulation datasets and performed corresponding testing experiments. We also compared the performance of FPGWR and other GWR software packages using open datasets. The results show that the runtime of FPGWR is negatively correlated with the CUDA core number, and the calculation efficiency of FPGWR achieves a rate of thousands or even tens of thousands times faster than the traditional GWR algorithms. FPGWR provides an effective tool for exploring spatial heterogeneity for large-scale geographic data (geodata). Full article
(This article belongs to the Special Issue Advances in Computational Approaches for Spatial Analysis and Modeling)
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20 pages, 4725 KiB  
Article
Uncorrelated Geo-Text Inhibition Method Based on Voronoi K-Order and Spatial Correlations in Web Maps
by Yufeng He, Yehua Sheng, Yunqing Jing, Yue Yin and Ahmad Hasnain
ISPRS Int. J. Geo-Inf. 2020, 9(6), 381; https://doi.org/10.3390/ijgi9060381 - 9 Jun 2020
Cited by 3 | Viewed by 2654
Abstract
Unstructured geo-text annotations volunteered by users of web map services enrich the basic geographic data. However, irrelevant geo-texts can be added to the web map, and these geo-texts reduce utility to users. Therefore, this study proposes a method to detect uncorrelated geo-text annotations [...] Read more.
Unstructured geo-text annotations volunteered by users of web map services enrich the basic geographic data. However, irrelevant geo-texts can be added to the web map, and these geo-texts reduce utility to users. Therefore, this study proposes a method to detect uncorrelated geo-text annotations based on Voronoi k-order neighborhood partition and auto-correlation statistical models. On the basis of the geo-text classification and semantic vector transformation, a quantitative description method for spatial autocorrelation was established by the Voronoi weighting method of inverse vicinity distance. The Voronoi k-order neighborhood self-growth strategy was used to detect the minimum convergence neighborhood for spatial autocorrelation. The Pearson method was used to calculate the correlation degree of the geo-text in the convergence region and then deduce the type of geo-text to be filtered. Experimental results showed that for given geo-text types in the study region, the proposed method effectively calculated the correlation between new geo-texts and the convergence region, providing an effective suggestion for preventing uncorrelated geo-text from uploading to the web map environment. Full article
(This article belongs to the Special Issue Advances in Computational Approaches for Spatial Analysis and Modeling)
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