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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 6183

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Special Issue Editors

Prof. Dr. Jingzhong Li

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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

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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

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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 (9 papers)

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Research

37 pages, 16383 KB

Open AccessArticle

Generating Realistic Urban Patterns: A Controllable cGAN Approach with Hybrid Loss Optimization

by Amgad Agoub and Martin Kada

ISPRS Int. J. Geo-Inf. 2025, 14(10), 375; https://doi.org/10.3390/ijgi14100375 - 25 Sep 2025

Abstract

This study explores the use of conditional Generative Adversarial Networks (cGANs) for simulating urban morphology, a domain where such models remain underutilized but have significant potential to generate realistic and controllable city patterns. To explore this potential, this research includes several contributions: a bespoke model architecture that integrates attention mechanisms with visual reasoning through a generalized conditioning layer. A novel mechanism that enables the steering of urban pattern generation through the use of statistical input distributions, the development of a novel and comprehensive training dataset, meticulously derived from open-source geospatial data of Berlin. Our model is trained using a hybrid loss function, combining adversarial, focal and L1 losses to ensure perceptual realism, address challenging fine-grained features, and enforce pixel-level accuracy. Model performance was assessed through a combination of qualitative visual analysis and quantitative evaluation using metrics such as Kullback–Leibler Divergence (KL Divergence), Structural Similarity Index (SSIM), and Dice Coefficient. The proposed approach has demonstrated effectiveness in generating realistic and spatially coherent urban patterns, with promising potential for controllability. In addition to showcasing its strengths, we also highlight the limitations and outline future directions for advancing future work. Full article

(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)

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19 pages, 11819 KB

Open AccessArticle

Spatiotemporal Dynamics and Multi-Scale Equity Evaluation of Urban Rail Accessibility: Evidence from Hangzhou

by Jiasheng Zhu and Xiaoping Rui

ISPRS Int. J. Geo-Inf. 2025, 14(9), 361; https://doi.org/10.3390/ijgi14090361 - 18 Sep 2025

Viewed by 331

Abstract

In recent years, the rapid expansion of urban rail transit has significantly improved travel efficiency, yet it has also exacerbated spatial inequality in service coverage. Accessibility, as a fundamental metric for evaluating the equity of service distribution, remains limited by three major shortcomings in current assessment methods: the neglect of actual road network characteristics, reliance on a single static scale, and the absence of quantitative mechanisms to assess accessibility equity. These deficiencies hinder a comprehensive understanding of how equity evolves with the spatiotemporal dynamics of rail systems. To address the aforementioned issues, this study proposes an innovative spatiotemporally dynamic and multi-scale analytical framework for evaluating urban rail accessibility and its equity implications. Specifically, we develop a network-based buffer decay model to refine service population estimation by incorporating realistic walking paths, capturing both distance decay and road network constraints. The framework integrates multiple spatial analytical techniques, including the Gini coefficient, Lorenz curve, global and local spatial autocorrelation, center-of-gravity shift, and standard deviation ellipse, to quantitatively assess the equity and evolutionary patterns of accessibility across multiple spatial scales. Taking the central urban area of Hangzhou as a case study, this research investigates the spatiotemporal patterns and equity changes in metro station accessibility in 2019 and 2023. The results indicate that the expansion of the metro network has partially improved overall accessibility equity: the Gini coefficient at the TAZ (Traffic Analysis Zone) scale decreased from 0.56 to 0.425. Nevertheless, significant inequality remains at finer spatial resolutions (grid-level Gini coefficient = 0.404). In terms of spatial pattern, the core area (e.g., Wulin Square) forms a ‘high-high’ accessibility agglomeration area, while the urban fringe area (e.g., northern Yuhang) presents a ‘low-low’ agglomeration, and the problem of local ‘accessibility depression’ still exists. Additionally, the accessibility centroid has consistently shifted northwestward, and the long axis of the standard deviation ellipse has rotated from an east–west to a northwest-southeast orientation, indicating a growing spatial polarization between core and peripheral zones. The findings suggest that improving equity in urban rail accessibility cannot rely solely on expanding network size; rather, it requires coordinated strategies involving network structure optimization, branch line development, multimodal integration, and the construction of efficient transfer systems to promote more balanced and equitable spatial distribution of rail transit resources citywide. Full article

(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)

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22 pages, 2750 KB

Open AccessArticle

Spatiotemporal Evolution and Differential Characteristics of Logistics Resilience in Provinces Along the Belt and Road in China

by Yi Liang, Zhaoxu Yuan, Yan Fang and Han Liu

ISPRS Int. J. Geo-Inf. 2025, 14(9), 360; https://doi.org/10.3390/ijgi14090360 - 18 Sep 2025

Viewed by 287

Abstract

Based on provincial panel data from 2014 to 2023, this study employs the entropy weight method to construct an indicator system for measuring the logistical resilience of regions along China’s Belt and Road Initiative (BRI). The Dagum Gini coefficient is used to analyze regional disparities in resilience levels. Furthermore, when geographical factors are integrated, spatial autocorrelation analysis via Moran’s I index is conducted on the measurement results to explain the spatial heterogeneity among variables. The results reveal several key findings: (1) During the implementation of the BRI, the logistical resilience of regions along the route has improved to varying degrees, indicating enhanced ability of the logistics industry to withstand external risks and recover from disruptions. (2) The level of regional logistical resilience exhibits a spatial pattern similar to that of logistics industry development, characterized by a gradual decline from the southeastern coastal areas toward the northwestern inland regions. (3) Logistical resilience within the study areas has increasingly significant spatial spillover effects; that is, regions with developed logistics industries positively impact surrounding areas, driving improvements in their resilience levels. The results of this study suggest a growing trend of spatial convergence in logistical resilience across these regions. Based on these results, corresponding policy recommendations are proposed to provide insights for enhancing regional logistical resilience. Full article

(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)

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25 pages, 6422 KB

Open AccessArticle

Evaluating UAV Flight Parameters for High-Accuracy in Road Accident Scene Documentation: A Planimetric Assessment Under Simulated Roadway Conditions

by Thanakorn Phojaem, Adisorn Dangbut, Panuwat Wisutwattanasak, Thananya Janhuaton, Thanapong Champahom, Vatanavongs Ratanavaraha and Sajjakaj Jomnonkwao

ISPRS Int. J. Geo-Inf. 2025, 14(9), 357; https://doi.org/10.3390/ijgi14090357 - 17 Sep 2025

Viewed by 344

Abstract

Unmanned Aerial Vehicles (UAVs) have become increasingly valuable for accident scene reconstruction and forensic surveying due to their flexibility and ability to capture high-resolution imagery. This study investigates the impact of flight altitude, camera angle, and image overlap on the spatial accuracy of 3D models generated from UAV imagery. A total of 27 flight configurations were conducted using a DJI Phantom 4 Pro V2, combining three altitudes (30 m, 45 m, 60 m), three camera angles (90°, 75°, 60°), and three overlap levels (60%, 70%, 80%). The resulting 3D models were assessed by comparing measured linear distances between ground control points with known reference distances. The Root Mean Square Error (RMSE) was used to quantify model accuracy. The results indicated that lower flight altitudes, nadir or moderately oblique camera angles, and higher image overlaps consistently yielded the most accurate reconstructions. A Wilcoxon rank-sum test confirmed that the differences in accuracy across parameter settings were statistically significant. These findings highlight the critical role of flight configuration in achieving centimeter-level accuracy, as evidenced by RMSE values ranging from 1.7 to 7.6 cm, and provide practical recommendations for optimizing UAV missions in forensic and engineering applications. Full article

(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)

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16 pages, 2224 KB

Open AccessArticle

Analysis of Hotel Reviews and Ratings with Geographical Factors in Seoul: A Quantitative Approach to Understanding Tourist Satisfaction

by Abhilasha Kashyap and Seong-Yun Hong

ISPRS Int. J. Geo-Inf. 2025, 14(9), 334; https://doi.org/10.3390/ijgi14090334 - 29 Aug 2025

Viewed by 902

Abstract

This study examines how hotel characteristics and urban spatial context influence tourist satisfaction in Seoul, South Korea, by integrating sentiment analysis of online reviews with regression modeling. Drawing on 4500 TripAdvisor reviews from 75 hotels, sentiment scores were extracted using aspect-based sentiment analysis, and two regression approaches, ordinary least squares (OLS) and spatial autoregressive combined models, were applied to evaluate how hotel specific features, such as the age and scale of the hotels and room rates, and their geographic characteristics, such as the proximity to airports and cultural landmarks, affect both emotional sentiment and formal hotel ratings. The OLS model for sentiment scores identified the scale and rating of the hotels as well as the proximity to the airports as key predictors. Additionally, the spatial autoregressive combined model was also statistically significant, suggesting spatial spillover effects. A separate model for the traditional rating revealed weaker associations, with only the hotel’s opening year reaching significance. These findings highlight a divergence between emotional responses and structured ratings, with sentiment scores more sensitive to spatial context. This study offers practical implications for hotel managers and urban planners, emphasizing the value of incorporating spatial factors into hospitality research to better understand the tourist experience. Full article

(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)

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41 pages, 3940 KB

Open AccessArticle

Economic Optimization of Bike-Sharing Systems via Nonlinear Threshold Effects: An Interpretable Machine Learning Approach in Xi’an, China

by Haolong Yang, Chen Feng and Chao Gao

ISPRS Int. J. Geo-Inf. 2025, 14(9), 333; https://doi.org/10.3390/ijgi14090333 - 27 Aug 2025

Viewed by 796

Abstract

As bike-sharing systems become increasingly integral to sustainable urban mobility, understanding their economic viability requires moving beyond conventional linear models to capture complex operational dynamics. This study develops an interpretable analytical framework to uncover non-linear relationships governing bike-sharing economic performance in Xi’an, China, utilizing one-month operational data across 202 Transportation Analysis Zones (TAZs). Combining spatial analysis with explainable machine learning (XGBoost–SHAP), we systematically examine how operational factors and built environment characteristics interact to influence economic outcomes, achieving superior predictive performance (R² = 0.847) compared to baseline linear regression models (R² = 0.652). The SHAP-based interpretation reveals three key findings: (1) bike-sharing performance exhibits pronounced spatial heterogeneity that correlates strongly with urban functional patterns), with commercial districts and transit-adjacent areas demonstrating consistently higher economic returns. (2) Gradual positive relationships emerge across multiple factors—including bike supply density (maximum SHAP contribution +1.0), commercial POI distribution, and transit accessibility—with performance showing consistent but moderate improvements rather than dramatic threshold effects. (3) Significant interaction effects are quantified between key factors, with bike supply density and commercial POI density exhibiting strong synergistic relationships (interaction values 1.5–2.0), particularly in areas combining high commercial activity with good transit connectivity. The findings challenge simplistic linear assumptions in bike-sharing management while providing quantitative evidence for spatially differentiated strategies that account for moderate threshold behaviors and factor synergies. Cross-validation results (5-fold, R² = 0.89 ± 0.018) confirm model robustness, while comprehensive performance metrics demonstrate substantial improvements over traditional approaches (35.1% RMSE reduction, 36.6% MAE improvement). The proposed framework offers urban planners a data-driven tool for evidence-based decision-making in sustainable mobility systems, with broader methodological applicability for similar urban contexts. Full article

(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)

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16 pages, 2015 KB

Open AccessArticle

LTVPGA: Distilled Graph Attention for Lightweight Traffic Violation Prediction

by Yingzhi Wang, Yuquan Zhou and Feng Zhang

ISPRS Int. J. Geo-Inf. 2025, 14(9), 332; https://doi.org/10.3390/ijgi14090332 - 27 Aug 2025

Viewed by 490

Abstract

Traffic violations, the primary cause of road accidents, threaten public safety by disrupting traffic flow and causing substantial casualties and economic losses. Accurate spatiotemporal prediction of violations offers critical insights for proactive traffic management. While Graph Attention Network (GAT) methods excel in spatiotemporal forecasting, their practical deployment is hindered by prohibitive computational costs when handling dynamic large-scale data. To address this issue, we propose a Lightweight Traffic Violation Prediction with Graph Attention Distillation (LTVPGA) model, transferring spatial topology comprehension from a complex GAT to an efficient multilayer perceptron (MLP) via knowledge distillation. Our core contribution lies in topology-invariant knowledge transfer, where spatial relation priors distilled from the teacher’s attention heads enable the MLP student to bypass explicit graph computation. This approach achieves significant efficiency gains for large-scale data—notably accelerated inference time and reduced memory overhead—while preserving modeling capability. We conducted a performance comparison between LTVPGA, Conv-LSTM, and GATR (teacher model). LTVPGA achieved revolutionary efficiency: consuming merely 15% memory and 0.6% training time of GATR while preserving nearly the same accuracy. This capacity enables practical deployment without sacrificing fidelity, providing a scalable solution for intelligent transportation governance. Full article

(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)

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22 pages, 3025 KB

Open AccessArticle

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 620

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 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 KB

Open AccessArticle

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 806

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 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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