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IJGI
Special Issues
Spatial Data Science and Knowledge Discovery
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Spatial Data Science and Knowledge Discovery

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1034

Special Issue Editors

Prof. Dr. Jingzhong Li

E-Mail Website
Guest Editor
Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
Interests: map generalization and scale transformation; map design and spatial information visualization; spatial analysis and big data mining; geographic information engineering and smart services
Dr. Dev Raj Paudyal

E-Mail Website
Guest Editor
School of Surveying and Built Environment, University of Southern Queensland West Street, Toowoomba, QLD 4350, Australia
Interests: spatial data infrastructure (SDI); land administration; cadastre; IoT; citizen science; mixed method research; land use policy; catchment management; health informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Binbin Lu

E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: geocomputation; spatial statistics; geographically weighted (GW) modelling; spatio-temporal big data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Spatial data science and knowledge discovery have emerged as pivotal fields in addressing complex challenges related to urbanization, environmental sustainability, and resource management. The integration of advanced geospatial technologies, artificial intelligence, and big data analytics has revolutionized our ability to extract actionable insights from spatial datasets.

This Special Issue aims to showcase cutting-edge research that bridges theoretical innovation with practical applications, fostering interdisciplinary collaboration to advance spatial data science methodologies and their real-world impact. This Special Issue seeks to highlight transformative research that addresses global challenges through spatial data science. By combining geospatial analysis with emerging technologies such as AI, IoT, and cloud computing, we aim to advance methodologies that improve living spaces, inform policy, and promote sustainable development. Submissions may include original research, reviews, and interdisciplinary studies that align with the themes.

We invite contributions that explore novel approaches, tools, and case studies in spatial data science and knowledge discovery. Topics of interest include, but are not limited to, the following:

  • Spatial Data Mining and Machine Learning: Development of algorithms for pattern recognition, clustering, and predictive modeling in geospatial contexts.
  • Spatial Heterogeneity and Drivers: Analysis of regional disparities, spatiotemporal dynamics, and driving mechanisms using spatial autocorrelation, geographically weighted regression (GWR), or mixed GWR (MGWR) techniques.
  • 3D Modeling and Spatial Digital Twins: Innovations in 3D spatial modeling, urban digital twins, and their applications in smart cities, built and natural environment including mining and cultural heritage preservation.
  • Geospatial Knowledge Graphs: Integration of domain knowledge with spatial data for enhanced decision-making in environmental and urban planning.
  • Spatial Sampling and Inference: Methodological advances in spatial statistics, uncertainty modeling, and scalable solutions for large datasets.
  • Applications in Sustainability: Case studies on land-use optimization, land administration, non-grain cultivation dynamics, climate resilience, environmental monitoring, resource management and public health.
  • Ethical, legal, and societal impacts of spatial data science

We look forward to your submissions.

Prof. Dr. Jingzhong Li
Dr. Dev Raj Paudyal
Dr. Binbin Lu
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 1900 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
  • geospatial AI
  • knowledge discovery
  • 3D modeling
  • smart cities
  • sustainability

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Published Papers (2 papers)

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22 pages, 3025 KiB  
Article
Exploring the Spatial Association Between Spatial Categorical Data Using a Fuzzy Geographically Weighted Colocation Quotient Method
by Ling Li, Lian Duan, Meiyi Li and Xiongfa Mai
ISPRS Int. J. Geo-Inf. 2025, 14(8), 296; https://doi.org/10.3390/ijgi14080296 - 29 Jul 2025
Viewed by 229
Abstract
Spatial association analysis is essential for understanding interdependencies, spatial proximity, and distribution patterns within spatial data. The spatial scale is a key factor that significantly affects the result of spatial association mining. Traditional methods often rely on a fixed distance threshold (bandwidth) to [...] Read more.
Spatial association analysis is essential for understanding interdependencies, spatial proximity, and distribution patterns within spatial data. The spatial scale is a key factor that significantly affects the result of spatial association mining. Traditional methods often rely on a fixed distance threshold (bandwidth) to define the scale effect, which can lead to scale sensitivity and discontinuity results. To address these limitations, this study introduces the Fuzzy Geographically Weighted Colocation Quotient (FGWCLQ) method. By integrating fuzzy theory, FGWCLQ replaces binary distance cutoffs with continuous membership functions, providing a more flexible and stable approach to spatial association mining. Using Point of Interest (POI) data from the Beijing urban area, FGWCLQ was applied to explore both intra- and inter-category spatial association patterns among star hotels, transportation facilities, and tourist attractions at different fuzzy neighborhoods. The results indicate that FGWCLQ can reliably discover global prevalent spatial associations among diverse facility types and visualize the spatial heterogeneity at various spatial scales. Compared to the deterministic GWCLQ method, FGWCLQ delivers more stable and robust results across varying spatial scales and generates more continuous association surfaces, which enable clear visualization of hierarchical clustering. Empirical findings provide valuable insights for optimizing the location of star hotels and supporting decision-making in urban planning. The method is available as an open-source Matlab package, providing a practical tool for diverse spatial association investigations. Full article
(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)
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24 pages, 2538 KiB  
Article
A Spatio-Temporal Evolutionary Embedding Approach for Geographic Knowledge Graph Question Answering
by Chunju Zhang, Chaoqun Chu, Kang Zhou, Shu Wang, Yunqiang Zhu, Jianwei Huang, Zhaofu Wu and Fei Gao
ISPRS Int. J. Geo-Inf. 2025, 14(8), 295; https://doi.org/10.3390/ijgi14080295 - 28 Jul 2025
Viewed by 337
Abstract
In recent years, geographic knowledge graphs (GeoKGs) have shown great promise in representing spatio-temporal and event-driven knowledge. However, existing knowledge graph embedding approaches mainly focus on structural patterns and often overlook the dynamic evolution of entities in both time and space, which limits [...] Read more.
In recent years, geographic knowledge graphs (GeoKGs) have shown great promise in representing spatio-temporal and event-driven knowledge. However, existing knowledge graph embedding approaches mainly focus on structural patterns and often overlook the dynamic evolution of entities in both time and space, which limits their effectiveness in downstream reasoning tasks. To address this, we propose a spatio-temporal evolutionary knowledge embedding approach (ST-EKA) that enhances entity representations by modeling their evolution through type-aware encoding, temporal and spatial decay mechanisms, and context aggregation. ST-EKA integrates four core components, including an entity encoder constrained by relational type consistency, a temporal encoder capable of handling both time points and intervals through unified sampling and feedforward encoding, a multi-scale spatial encoder that combines geometric coordinates with semantic attributes, and an evolutionary knowledge encoder that employs attention-based spatio-temporal weighting to capture contextual dynamics. We evaluate ST-EKA on three representative GeoKG datasets—GDELT, ICEWS, and HAD. The results demonstrate that ST-EKA achieves an average improvement of 6.5774% in AUC and 5.0992% in APR on representation learning tasks. In question answering tasks, it yields a maximum average increase of 1.7907% in AUC and 0.5843% in APR. Notably, it exhibits superior performance in chain queries and complex spatio-temporal reasoning, validating its strong robustness, good interpretability, and practical application value. Full article
(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)
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