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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: 30 June 2026 | Viewed by 15398

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 250 words) can be sent to the Editorial Office for assessment.

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 (18 papers)

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Research

24 pages, 6395 KB  
Article
Research on Spatiotemporal Dynamic and Driving Mechanism of Urban Real Estate Inventory: Evidence from China
by Ping Zhang, Sidong Zhao, Hua Chen and Jiaoguo Ma
ISPRS Int. J. Geo-Inf. 2026, 15(1), 5; https://doi.org/10.3390/ijgi15010005 (registering DOI) - 20 Dec 2025
Abstract
Real estate inventory dynamics exhibit distinct temporal patterns and spatial heterogeneity, and precise identification of these trends serves as a prerequisite for effective policy formulation. Research on the spatiotemporal evolution patterns and influencing factors of real estate inventory holds significant academic and practical [...] Read more.
Real estate inventory dynamics exhibit distinct temporal patterns and spatial heterogeneity, and precise identification of these trends serves as a prerequisite for effective policy formulation. Research on the spatiotemporal evolution patterns and influencing factors of real estate inventory holds significant academic and practical value. By employing ESDA, the Boston Matrix, and geographically weighted regression models to analyze 2017–2022 data from 287 Chinese cities, this study reveals a cyclical shift in China’s real estate inventory management—from “destocking” to “restocking”. The underlying drivers have transitioned from policy-led interventions to fundamentals-driven factors, including population dynamics, income levels, and market expectations. China’s real estate inventory and its changes exhibit significant spatiotemporal differentiation and spatial agglomeration patterns, demonstrating a spatial structure characterized by “multiple clustered highlands with peripheral lowlands” led by urban agglomerations. The influencing mechanism of China’s real estate inventory constitutes a complex system shaped by three key dimensions: macro-level drivers, regional differentiation, and structural contradictions. Policymakers should reorient destocking policies from “short-term stimulus” to “long-term coordination”, from “industrial policy” to “spatial policy”, and from addressing market “symptoms” to tackling “root causes”. This study argues that effective destocking policies constitute a systematic engineering challenge, demanding policymakers demonstrate profound analytical depth. They must move beyond simplistic sales metrics and perform multi-dimensional evaluations encompassing economic geography, demographic trends, fiscal systems, and land supply mechanisms. This paradigm shift from “symptom management” to “root cause resolution” and “systemic regulation” is essential for achieving sustainable real estate market development. Full article
(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)
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22 pages, 1099 KB  
Article
Cross-Attention Diffusion Model for Semantic-Aware Short-Term Urban OD Flow Prediction
by Hongxiang Li, Zhiming Gui and Zhenji Gao
ISPRS Int. J. Geo-Inf. 2026, 15(1), 2; https://doi.org/10.3390/ijgi15010002 - 19 Dec 2025
Abstract
Origin–destination (OD) flow prediction is fundamental to intelligent transportation systems, yet existing diffusion-based models face two critical limitations. First, they inadequately exploit spatial semantics, focusing primarily on temporal dependencies or topological correlations while neglecting urban functional heterogeneity encoded in Points of Interest (POIs). [...] Read more.
Origin–destination (OD) flow prediction is fundamental to intelligent transportation systems, yet existing diffusion-based models face two critical limitations. First, they inadequately exploit spatial semantics, focusing primarily on temporal dependencies or topological correlations while neglecting urban functional heterogeneity encoded in Points of Interest (POIs). Second, static embedding fusion cannot dynamically capture semantic importance variations during denoising—particularly during traffic surges in POI-dense areas. To address these gaps, we propose the Cross-Attention Diffusion Model (CADM), a semantically conditioned framework for short-term OD flow forecasting. CADM integrates POI embeddings as spatial semantic priors and employs cross-attention to enable semantic-guided denoising, facilitating dynamic spatiotemporal feature fusion. This design adaptively reweights regional representations throughout reverse diffusion, enhancing the model’s capacity to capture complex mobility patterns. Experiments on real-world datasets demonstrate that CADM achieves balanced performance across multiple metrics. At the 30 min horizon, CADM attains the lowest RMSE of 5.77, outperforming iTransformer by 1.9%, while maintaining competitive performance at the 15 min horizon. Ablation studies confirm that removing POI features increases prediction errors by 15–20%, validating the critical role of semantic conditioning. These findings advance semantic-aware generative modeling for spatiotemporal prediction and provide practical insights for intelligent transportation systems, particularly for newly established transportation hubs or functional zone reconfigurations where semantic understanding is essential. Full article
(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)
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17 pages, 2657 KB  
Article
GEPReS: A Geospatially Enabled Predictive Recommendation System for the Preventive Management of Historical Buildings
by Noëlla Dolińska, Gabriela Wojciechowska, Joanna Bac-Bronowicz and Łukasz Jan Bednarz
ISPRS Int. J. Geo-Inf. 2026, 15(1), 1; https://doi.org/10.3390/ijgi15010001 - 19 Dec 2025
Abstract
This study introduces GEPReS, a Geospatially Enabled Predictive Recommendation System designed to support the preventive management of historical buildings through short-horizon risk forecasting and context-aware decision support. The system integrates Geographic Information Systems (GISs), Internet of Things (IoT) sensor networks, and authoritative meteorological [...] Read more.
This study introduces GEPReS, a Geospatially Enabled Predictive Recommendation System designed to support the preventive management of historical buildings through short-horizon risk forecasting and context-aware decision support. The system integrates Geographic Information Systems (GISs), Internet of Things (IoT) sensor networks, and authoritative meteorological data to generate timely, actionable recommendations for conservation interventions. These may include pre-emptive shutter closure during heatwaves, activation of ventilation under elevated humidity, or intensified monitoring of structurally sensitive zones during heavy precipitation. By coupling historical datasets with real-time telemetry and calibrated predictive models, GEPReS addresses the distinctive vulnerabilities of heritage structures, which arise from material sensitivity, conservation constraints, and operational limitations under contemporary climatic conditions. The architecture combines spatial analysis, typology-aware risk assessment, and reproducible modelling practices to ensure interpretability and compliance with conservation principles. Designed for scalability and online implementation, the system provides a modular framework capable of adapting to diverse building typologies and resource environments. The paper details the system architecture, data sources, modelling approach, and implementation challenges, supported by empirical evidence from multi-site pilot deployments. Full article
(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)
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22 pages, 5552 KB  
Article
MSA-UNet: Multiscale Feature Aggregation with Attentive Skip Connections for Precise Building Extraction
by Guobiao Yao, Yan Chen, Wenxiao Sun, Zeyu Zhang, Yifei Tang and Jingxue Bi
ISPRS Int. J. Geo-Inf. 2025, 14(12), 497; https://doi.org/10.3390/ijgi14120497 - 17 Dec 2025
Viewed by 69
Abstract
An accurate and reliable extraction of building structures from high-resolution (HR) remote sensing images is an important research topic in 3D cartography and smart city construction. However, despite the strong overall performance of recent deep learning models, limitations remain in handling significant variations [...] Read more.
An accurate and reliable extraction of building structures from high-resolution (HR) remote sensing images is an important research topic in 3D cartography and smart city construction. However, despite the strong overall performance of recent deep learning models, limitations remain in handling significant variations in building scales and complex architectural forms, which may lead to inaccurate boundaries or difficulties in extracting small or irregular structures. Therefore, the present study proposes MSA-UNet, a reliable semantic segmentation framework that leverages multiscale feature aggregation and attentive skip connections for an accurate extraction of building footprints. This framework is constructed based on the U-Net architecture, incorporating VGG16 as a replacement for the original encoder structure, which enhances its ability to capture low-discriminative features. To further improve the representation of image buildings with different scales and shapes, a serial coarse-to-fine feature aggregation mechanism was used. Additionally, a novel skip connection was built between the encoder and decoder layers to enable adaptive weights. Furthermore, a dual-attention mechanism, implemented through the convolutional block attention module, was integrated to enhance the focus of the network on building regions. Extensive experiments conducted on the WHU and Inria building datasets validated the effectiveness of MSA-UNet. On the WHU dataset, the model demonstrated a state-of-the-art performance with a mean Intersection over Union (mIoU) of 94.26%, accuracy of 98.32%, F1-score of 96.57%, and mean Pixel accuracy (mPA) of 96.85%, corresponding to gains of 1.41% in mIoU over the baseline U-Net. On the more challenging Inria dataset, MSA-UNet achieved an mIoU of 85.92%, indicating a consistent improvement of up to 1.9% over the baseline U-Net. These results confirmed that MSA-UNet markedly improved the accuracy and boundary integrity of building extraction from HR data, outperforming existing classic models in terms of segmentation quality and robustness. Full article
(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)
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27 pages, 8908 KB  
Article
Reducing Extreme Commuting by Built Environmental Factors: Insights from Spatial Heterogeneity and Nonlinear Effect
by Fengxiao Li, Xiaobing Liu, Xuedong Yan, Zile Liu, Xuefei Zhao and Lu Ma
ISPRS Int. J. Geo-Inf. 2025, 14(12), 487; https://doi.org/10.3390/ijgi14120487 - 9 Dec 2025
Viewed by 299
Abstract
Nowadays, the number of people enduring extreme commuting is increasing, exacerbating traffic problems and harming individual well-being. To quantify the extreme commuting, we propose an extreme commuting severity (ECS) index that combines the number of extreme commuting trips with their specific distances, where [...] Read more.
Nowadays, the number of people enduring extreme commuting is increasing, exacerbating traffic problems and harming individual well-being. To quantify the extreme commuting, we propose an extreme commuting severity (ECS) index that combines the number of extreme commuting trips with their specific distances, where a one-way trip with a commuting distance of at least 25 km is regarded as an extreme commuting trip. In Beijing, the ECS index shows substantial spatial variability, with maximum values exceeding 30,000 for origins and 50,000 for destinations, underscoring the severe commuting burden in specific areas. By integrating the geographically weighted random forest (GWRF) with Shapley additive explanations (SHAP), we model both nonlinear effects and spatial heterogeneity in how the built environment shapes extreme commuting. Compared with benchmark models, the proposed GWRF model achieves the highest predictive performance, yielding the largest R2 and the lowest absolute and relative indicators across both generation and attraction scenarios. Notably, the GWRF improves explanatory power over the global model by a substantial margin, highlighting the importance of incorporating spatial heterogeneity. SHAP-based global importance results show that residential density (17.58%) is the most influential factor for ECS, whereas in the attraction scenario, company density exhibits the strongest contribution (20.7%), reflecting the strong pull of major employment clusters. Local importance maps further reveal pronounced spatial differences in effect direction and magnitude. For instance, although housing prices have modest global importance, they display clear spatial heterogeneity: they exert the strongest influence on extreme commuting generation within the Fourth Ring Road and around the North Fifth Ring, whereas in the attraction scenario, their effects concentrate in the southern part of the core area. These findings provide new empirical insights into the mechanisms underlying extreme commuting and highlight the need for spatially differentiated planning strategies. Full article
(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)
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34 pages, 6591 KB  
Article
Comparative Framework for Multi-Modal Accessibility Assessment Within the 15-Minute City Concept: Application to Parks and Playgrounds in an Indian Urban Neighborhood
by Swati Bahale, Amarpreet Singh Arora and Thorsten Schuetze
ISPRS Int. J. Geo-Inf. 2025, 14(12), 479; https://doi.org/10.3390/ijgi14120479 - 2 Dec 2025
Viewed by 309
Abstract
Urban neighborhoods in India face an uneven distribution and limited accessibility to parks and playgrounds, particularly in dense mixed-use areas where rapid urbanization constrains green infrastructure planning. To address these challenges, the Sustainable Transportation Assessment Index (SusTAIN) framework was developed to evaluate sustainable [...] Read more.
Urban neighborhoods in India face an uneven distribution and limited accessibility to parks and playgrounds, particularly in dense mixed-use areas where rapid urbanization constrains green infrastructure planning. To address these challenges, the Sustainable Transportation Assessment Index (SusTAIN) framework was developed to evaluate sustainable transportation in Indian urban neighborhoods, with ‘Accessibility’ identified as a crucial subtheme. Recent advancements in Geographic Information Systems (GISs) and urban data analysis tools have enabled accessibility assessments of parks and playgrounds at a neighborhood scale, yet the OSMnx approach has been only marginally explored and compared in the literature. This study addresses this gap by comparing three tools—the Quantum Geographic Information System (QGIS), OSMnx, and Space Syntax—for accessibility assessments of parks and playgrounds in Ward 60 of Kalyan Dombivli city, based on the 15-Minute City concept. Accessibility was evaluated using 25 m and 100 m grid resolutions under peak and non-peak conditions across public and private transportation modes. The findings show that QGIS offers highly consistent results at micro-scale (25 m), while OSMnx provides better accuracy at coarser scales (100 m+). The results were validated with space syntax through integration and choice values. The comparison highlights spatial disparities in accessibility across different tools and transportation modes, including Intermediate Public Transport (IPT), which remains underexplored despite its crucial role in last-mile connectivity. The presented approach can support municipal authorities in optimizing neighborhood mobility and is transferable for applying the SusTAIN framework in other urban contexts. Full article
(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)
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19 pages, 4300 KB  
Article
Investigating the Imbalanced Patterns and Determinants of Kindergarten Distribution Across China
by Guiling Tang and Feng Xu
ISPRS Int. J. Geo-Inf. 2025, 14(12), 463; https://doi.org/10.3390/ijgi14120463 - 25 Nov 2025
Viewed by 419
Abstract
The unbalanced allocation of educational resources in kindergartens across China has attracted increasing attention from scholars and the public. However, few studies have examined their spatially imbalanced distribution and its influencing factors. Based on point-of-interest data, this study systematically analyzes the spatially imbalanced [...] Read more.
The unbalanced allocation of educational resources in kindergartens across China has attracted increasing attention from scholars and the public. However, few studies have examined their spatially imbalanced distribution and its influencing factors. Based on point-of-interest data, this study systematically analyzes the spatially imbalanced distribution characteristics of kindergartens in China from a multiscale perspective using the spatial analysis and spatial regression model to identify the factors influencing its formation pattern. The results reveal that the distribution pattern is “more in the southeast and fewer in the northwest,” with the Hu Huanyong Line serving as the boundary. Kernel density analysis revealed that areas with a density greater than 0.34 individual/km2 were primarily concentrated in provincial capitals and major metropolitan areas, exhibiting a gradual decrease outward from these core zones. It also reveals a “large dispersion and small aggregation”, with a concentration around mega-cities, urban agglomerations, and provincial capitals. Significant spatial auto-correlations were found at all administrative levels, with hotspots distributed in northeast, north, and southeast China. The spatial determinants of kindergartens distribution in China exhibited significant spatial heterogeneity. The findings provide a reference in improving the spatial pattern and the state of unbalanced development of kindergarten education in China, as well as scientific suggestions to optimize resource allocation. Full article
(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)
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25 pages, 10483 KB  
Article
Mapping the Spatiotemporal Urban Footprint of Residents and Tourists: A Data-Driven Approach Based on User-Generated Reviews
by Mikel Barrena-Herrán, Itziar Modrego-Monforte and Olatz Grijalba
ISPRS Int. J. Geo-Inf. 2025, 14(12), 456; https://doi.org/10.3390/ijgi14120456 - 22 Nov 2025
Viewed by 456
Abstract
Understanding how different population groups interact with urban environments is essential for analyzing spatial dynamics and informing urban planning, especially in cities experiencing high visitor pressure. This study presents a methodological framework for the spatial and temporal delineation of urban areas based on [...] Read more.
Understanding how different population groups interact with urban environments is essential for analyzing spatial dynamics and informing urban planning, especially in cities experiencing high visitor pressure. This study presents a methodological framework for the spatial and temporal delineation of urban areas based on user-generated location-based data. By collecting nearly 1 million Google Maps reviews in the municipality of Donostia-San Sebastián, we identify and classify user profiles based on their spatiotemporal behavior. First, we collect points of interest (POIs) and associated reviews, including profile identifiers and timestamps. Then, we perform user-level webscraping to reconstruct review histories, enabling us to infer the predominant geographical origin of each user. Users are classified as residents or tourists using both spatial prevalence and temporal activity patterns. The resulting data is aggregated onto a hexagonal grid for geostatistical analysis. Using the Getis-Ord Gi* statistic and Mann-Kendall trend tests, we identify hotspots and long-term trends of activity for different population segments. Additionally, we propose novel indicators such as predominant periods of activity and diversity of geographical origin per cell to characterize heterogeneous patterns of urban use. Our results reveal distinct behavioral patterns, highlighting a more evenly distributed use of urban space by residents, with spatially overlapping yet temporally offset activities across central areas where tourists tend to concentrate their interactions. This spatiotemporal concentration is intensified as the tourists’ origin becomes more distant, suggesting that proximity shapes urban engagement. The proposed methodology offers a replicable strategy for urban analysis using publicly accessible user-generated data and contributes to the understanding of sociospatial dynamics in tourism-intensive cities. Full article
(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)
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21 pages, 1964 KB  
Article
Urban Grid Population Inflow Prediction via POI-Enhanced Conditional Diffusion with Dual-Dimensional Attention
by Zhiming Gui, Yuanchao Zhong and Zhenji Gao
ISPRS Int. J. Geo-Inf. 2025, 14(11), 448; https://doi.org/10.3390/ijgi14110448 - 15 Nov 2025
Viewed by 459
Abstract
Accurate prediction of urban grid-scale population inflow is crucial for smart city management and emergency response. However, existing methods struggle to model spatial heterogeneity and quantify prediction uncertainty, limiting their accuracy and decision-support capabilities. This paper proposes PDCDM (POI-enhanced Dual-Dimensional Conditional Diffusion Model), [...] Read more.
Accurate prediction of urban grid-scale population inflow is crucial for smart city management and emergency response. However, existing methods struggle to model spatial heterogeneity and quantify prediction uncertainty, limiting their accuracy and decision-support capabilities. This paper proposes PDCDM (POI-enhanced Dual-Dimensional Conditional Diffusion Model), which integrates urban functional semantic awareness with conditional diffusion modeling. The model captures urban functional attributes through multi-scale Point of Interest (POI) feature representations and incorporates them into the diffusion generation process. A dual-dimensional Transformer architecture is employed to decouple the modeling of temporal dependencies and inter-grid interactions, enabling adaptive fusion of grid-level features with dynamic temporal patterns. Building upon this dual-dimensional modeling framework, the conditional diffusion mechanism generates probabilistic predictions with explicit uncertainty quantification. Real-world urban dataset validation demonstrates that PDCDM significantly outperforms existing state-of-the-art methods in prediction accuracy and uncertainty quantification. Comprehensive ablation studies validate the effectiveness of each component and confirm the model’s practicality in complex urban scenarios. Full article
(This article belongs to the Special Issue Spatial Data Science and Knowledge Discovery)
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37 pages, 16386 KB  
Article
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
Viewed by 1000
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 [...] Read more.
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  
Article
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
Cited by 2 | Viewed by 1079
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 [...] Read more.
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, 2751 KB  
Article
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
Cited by 1 | Viewed by 743
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 [...] Read more.
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  
Article
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
Cited by 1 | Viewed by 1312
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 [...] Read more.
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  
Article
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 2618
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, [...] Read more.
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  
Article
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 1305
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, [...] Read more.
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  
Article
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 730
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 [...] Read more.
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  
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 1153
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 KB  
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 1781
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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