ISPRS International Journal of Geo-Information

22 pages, 788 KB

Open AccessArticle

Geographic Uncertainty in Multimodal Logistics and Supply Chain Management: A Systematic Literature Review

by Matthias Winter, Sarah Pfoser and Johannes Scholz

ISPRS Int. J. Geo-Inf. 2026, 15(8), 361; https://doi.org/10.3390/ijgi15080361 - 11 Aug 2026

Geographic uncertainty is an underexplored but increasingly relevant dimension of uncertainty in multimodal logistics and supply chain management. This systematic literature review synthesizes research at the intersection of logistics, supply chain uncertainty, and geography, with particular attention to multimodal freight transportation. Based on [...] Read more.

Geographic uncertainty is an underexplored but increasingly relevant dimension of uncertainty in multimodal logistics and supply chain management. This systematic literature review synthesizes research at the intersection of logistics, supply chain uncertainty, and geography, with particular attention to multimodal freight transportation. Based on a PRISMA-guided search in Scopus and Web of Science, 38 peer-reviewed journal and conference articles were analyzed to examine how geographic uncertainty is conceptualized, modeled, and applied in the literature. This review shows that geographic uncertainty is predominantly represented through network-based structures, especially at the node and arc levels, rather than through continuous spatial representations. Transportation-, transshipment-, and demand-related uncertainty dominate the literature, while environmental and emission-related uncertainty remain comparatively scarce. With respect to the geographic dimension, most studies focus on individual locations and routes, whereas regions, countries, and climate- or policy-relevant spatial units are rarely considered. In addition, many models treat uncertainty homogeneously across space, limiting their ability to capture location-specific patterns. To address these gaps, this paper proposes a conceptual distinction between locational and distance-based geographic uncertainty, grounded in the notion of friction of distance. This review highlights conceptual, methodological, and empirical research gaps and provides a foundation for improved modeling and management of geographic uncertainty in logistics systems. Full article

21 pages, 4291 KB

Open AccessArticle

Social Sensing and Geospatial Visual Analytics of Tourist Destination Image and Town-Scale Gravity

by Weixing Xu, Kangkang Gu, Jinxuan Li, Zhenyu Wang, Nuojun Wang, Xiaotong Ren, Jiehui Geng and Beibei Liu

ISPRS Int. J. Geo-Inf. 2026, 15(8), 360; https://doi.org/10.3390/ijgi15080360 - 11 Aug 2026

Abstract

Tourism has become a critical pathway for town construction, everyday-life improvement, and cultural revitalization. Yet, the mechanisms through which destination image is associated with town attraction remain insufficiently understood, particularly at the fine-grained town scale. Drawing on social media photographs and check-in records [...] Read more.

Tourism has become a critical pathway for town construction, everyday-life improvement, and cultural revitalization. Yet, the mechanisms through which destination image is associated with town attraction remain insufficiently understood, particularly at the fine-grained town scale. Drawing on social media photographs and check-in records from 26 characteristic towns in Tianjin, China, this study deconstructs tourist destination image into image genes and examines their associations with town gravity. A VGG19-based image-recognition model was used to identify and aggregate 68 scene types into nine image-gene categories, while check-in data from Weibo and Little Red Book were used to measure destination gravity. The results show that uniqueness image, cultural custom genes, public space genes, sidewalk density, and POI mix are significantly and positively associated with town gravity, whereas animal genes exhibit a significant negative association. These findings provide an empirical basis for policymakers and planners to strengthen distinctive cultural representation, optimize public-space systems and service diversity, and promote the sustainable attractiveness of town destinations. Full article

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18 pages, 34635 KB

Open AccessArticle

Temporally Weighted Land Surface Temperature: From Multiyear Observations to a Target-Year-Referenced Thermal Surface

by Jiahui Qin, Qianxin Wang, Xinyu Dong, Suqin Wu and Angela Lausch

ISPRS Int. J. Geo-Inf. 2026, 15(8), 359; https://doi.org/10.3390/ijgi15080359 - 10 Aug 2026

Abstract

Accurate characterization of land surface temperature (LST) is essential for urban thermal environment analysis. However, commonly used LST representations have their inherent limitations. For example, single-year LST may be influenced by year-specific anomalies, whereas multiyear means may obscure recent changes. Neither representation addresses [...] Read more.

Accurate characterization of land surface temperature (LST) is essential for urban thermal environment analysis. However, commonly used LST representations have their inherent limitations. For example, single-year LST may be influenced by year-specific anomalies, whereas multiyear means may obscure recent changes. Neither representation addresses how relevance to the target year and persistent historical information can be balanced within a single spatial LST surface. This study therefore proposes a temporally weighted land surface temperature (TWLST) method, which uses temporal decay weights and a half-life parameter to achieve the above balance and produce a continuous LST surface for spatial analysis. The framework was applied at regional and city scales in the Beijing–Tianjin–Hebei region and Beijing city, respectively, to examine its applicability. Spatial overlap between TWLST high-temperature (HT) areas and reference LST HT areas ranged from 0.577 to 0.733 and from 0.666 to 0.863 in the two applications. Historical annual LST anomalies were higher in TWLST-specific HT areas than in target-year-specific HT areas, with median paired differences of 1.16 °C and 2.49 °C, respectively. These results indicated that TWLST preferentially identified persistently warm locations while preserving major high-temperature patterns. The modeling and interpretation analyses further indicated that changes in LST representation could influence model performance and the relative importance of explanatory variables. Full article

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

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44 pages, 24447 KB

Open AccessArticle

An Intelligent Incremental Update Method for Building Data Across Multiple Scales Supported by Categorical Boosting

by Xinyu Niu, Haizhong Qian, Xiao Wang, Limin Xie, Xianyong Gong, Chengyi Liu and Jinghan Li

ISPRS Int. J. Geo-Inf. 2026, 15(8), 358; https://doi.org/10.3390/ijgi15080358 - 9 Aug 2026

Abstract

Leveraging larger-scale data with higher currency to incrementally update smaller-scale data, thereby upholding consistency across multiple scale databases, has become a core focus of contemporary map production tasks centered on data updates. Existing methods rely on rule-based constraints to extract change information and [...] Read more.

Leveraging larger-scale data with higher currency to incrementally update smaller-scale data, thereby upholding consistency across multiple scale databases, has become a core focus of contemporary map production tasks centered on data updates. Existing methods rely on rule-based constraints to extract change information and identify update-required objects, which have notable limitations in terms of method generalization and constraints on results. To address the above issues, we propose an intelligent incremental updating method for different scale building datasets supported by Categorical Boosting (CatBoost). The proposed method forms a general incremental updating framework for buildings through three steps: change information extraction, change information classification, and change information updating. Experiments conducted on different scale datasets from Ningbo, China, demonstrate that the proposed method can effectively identify update-required objects and generate more reasonable updated smaller-scale data than the comparative methods. Full article

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30 pages, 4646 KB

Open AccessArticle

3D-Geo-Vis: A Web-Based Environment for Interactive 3D Thematic Geovisualisation and Its Usability Evaluation

by Jakub Zejdlik, Tomas Vanicek and Vit Vozenilek

ISPRS Int. J. Geo-Inf. 2026, 15(8), 357; https://doi.org/10.3390/ijgi15080357 - 8 Aug 2026

Abstract

3D geovisualisation is increasingly used to represent spatial phenomena in engaging and interactive ways. However, 3D thematic methods and their user-centred evaluation remain challenging due to issues such as view distortion, variable scale, and complex interactions. Previous research emphasises that effective 3D thematic [...] Read more.

3D geovisualisation is increasingly used to represent spatial phenomena in engaging and interactive ways. However, 3D thematic methods and their user-centred evaluation remain challenging due to issues such as view distortion, variable scale, and complex interactions. Previous research emphasises that effective 3D thematic design requires careful treatment of visual variables and interactive camera control to mitigate overlap and occlusion. Building on this foundation, we present 3D-Geo-Vis, a web-based application with open-source code. The application visualises air temperature using seven methods of 3D geovisualisation and supports real-time adjustment of method-specific visual variables through a dedicated side panel. We report a usability study with 54 participants that combines (i) eye-tracking (Tobii Pro Spark, 60 Hz), (ii) interaction logging using our MapLogger tool, and (iii) a post-test questionnaire including the User Experience Questionnaire (UEQ) and open-ended feedback. Participants completed a structured scenario comprising free exploration and targeted analytical tasks. Across all sessions, MapLogger captured 15,457 interactions. The success rate of fully completed tasks was generally high for tasks involving the search for a specific value or modification of interface parameters (Task 2: 87.0%; Task 3: 85.2%), while the voxel-based analytical task showed slightly lower completion (Task 4: 77.8%), reflecting higher cognitive and interaction demands. UEQ results indicate a slightly positive overall user experience, with speed-related items rated most negatively. Triangulating gaze behaviour, interaction logs, and subjective feedback reveals key usability issues (e.g., insufficient salience of method switching, attention concentration on the side panel, and performance limitations of voxel rendering) and yields concrete recommendations for improving onboarding, feedback, and control discoverability in interactive 3D visualisation environments. These findings and resulting recommendations informed the development of the revised 3D-Geo-Vis 2.0 application. Full article

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13 pages, 727 KB

Open AccessArticle

Controllable Spatio-Temporal Modeling of Pedestrian Spawn Dynamics for Urban Crowd Geosimulation

by Yan Lyu, Bo Ling, Weiwei Wu, Xiangxiang Xing and Peng Wang

ISPRS Int. J. Geo-Inf. 2026, 15(8), 356; https://doi.org/10.3390/ijgi15080356 - 7 Aug 2026

Abstract

Realistic modeling of pedestrian flow in dense public spaces is important for urban crowd geosimulation, mobility analysis, and indoor public-space geo-information modeling. Although prior research has emphasized microscopic agent interactions, higher-level spawn dynamics—governing when and where pedestrians appear—remain less explored, despite their fundamental [...] Read more.

Realistic modeling of pedestrian flow in dense public spaces is important for urban crowd geosimulation, mobility analysis, and indoor public-space geo-information modeling. Although prior research has emphasized microscopic agent interactions, higher-level spawn dynamics—governing when and where pedestrians appear—remain less explored, despite their fundamental role in shaping crowd density and flow. Existing approaches often decouple spatial and temporal generation, limiting their ability to capture rich spatio-temporal correlations, and they lack controllability for user-specific scenarios such as high-density environments. In this paper, we propose a Guided Joint Spatio-Temporal Diffusion framework for pedestrian spawn simulation. Our objective is to develop and evaluate a controllable joint spatio-temporal generative model that produces each pedestrian spawn event—its inter-arrival time, origin, and destination—consistent with observed spawn dynamics and a user-specified normalized local spawn-intensity condition. The model addresses the upstream initialization of a crowd simulation, rather than complete trajectory prediction or microscopic interaction simulation, and is evaluated through both next-event accuracy and fixed-horizon controllability. The method leverages spatio-temporal diffusion point processes to jointly model spatial and temporal spawn events, capturing dependencies overlooked by classical and neural point-process-based methods. To support controllable pedestrian-flow generation for geosimulation and downstream applications, we integrate a conditional denoising network with classifier-free guidance, enabling user-specified factors such as crowd density to steer generation. Experiments on the Grand Central dataset demonstrate that our method outperforms strong baselines, reducing temporal error (T-RMSE) by 38% and achieving consistent improvements in spatial and spatio-temporal accuracy. These results show the potential of diffusion-based spatio-temporal modeling for controllable urban crowd geosimulation and pedestrian mobility data generation. Full article

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24 pages, 1817 KB

Open AccessArticle

Spatial Methods for Identifying Undocumented Historical Earthquake Damage

by Adi Ofir and Motti Zohar

ISPRS Int. J. Geo-Inf. 2026, 15(8), 355; https://doi.org/10.3390/ijgi15080355 - 6 Aug 2026

Abstract

Historical earthquake records are inherently incomplete: many sites that were likely damaged were never documented, leaving spatial gaps in the macroseismic record. This study evaluates whether intensity values at unreported sites can be estimated from the spatial relationships of surrounding reports, framing the [...] Read more.

Historical earthquake records are inherently incomplete: many sites that were likely damaged were never documented, leaving spatial gaps in the macroseismic record. This study evaluates whether intensity values at unreported sites can be estimated from the spatial relationships of surrounding reports, framing the task as a spatial data imputation problem. Three spatial imputation methods, Linear regression, K-Nearest Neighbors (KNN), and Kriging, were applied to eight macroseismic datasets, comprising two historical Dead Sea Transform earthquakes (1927 Dead Sea, 1837 South Lebanon) and six instrumental events from major strike-slip fault systems. Model performance was assessed with 5-fold cross-validation under random and spatial-block designs, using Mean Squared Error (MSE) and success rate, defined as the percentage of predictions falling within ±0.5 and ±1.0 intensity units of observed values. Under random cross-validation, simple and locally focused models performed on par with the complex geostatistical approaches. For the geographically concentrated historical data, success rates reached up to 90% within ±1.0 intensity units. San Andreas events yielded the strongest results among instrumental datasets, while Caribbean events showed the weakest performance due to spatial reporting biases. Under spatial-block cross-validation, performance declined across all models, with linear regression and Universal Kriging proving most robust to spatial extrapolation. These findings provide a methodological basis for estimating intensity at undocumented sites. While continuous intensity mapping from sparse data remains inadvisable, point-based imputation offers a practical tool for enriching historical earthquake records, with direct implications for seismic research along poorly documented fault systems. Full article

(This article belongs to the Topic Natural Hazards Monitoring, Risk Assessment, Modelling and Management in the Artificial Intelligence Era)

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28 pages, 8709 KB

Open AccessArticle

Causal–Semantic Spatiotemporal Traffic Flow Forecasting for Expressway UAV Pre-Deployment Using ETC Gantry Networks

by Zeen Yang, Zhuoer Wang, Hongjuan Zhang and Bijun Li

ISPRS Int. J. Geo-Inf. 2026, 15(8), 354; https://doi.org/10.3390/ijgi15080354 - 6 Aug 2026

Abstract

Expressway unmanned aerial vehicle (UAV) pre-deployment is a geospatial decision-support task that requires reliable road-segment-level traffic flow prediction based on spatial sensing networks. However, existing spatiotemporal forecasting models remain limited in characterizing cross-segment propagation relationships, long-lag causal dependencies, and atypical traffic evolution patterns. [...] Read more.

Expressway unmanned aerial vehicle (UAV) pre-deployment is a geospatial decision-support task that requires reliable road-segment-level traffic flow prediction based on spatial sensing networks. However, existing spatiotemporal forecasting models remain limited in characterizing cross-segment propagation relationships, long-lag causal dependencies, and atypical traffic evolution patterns. In addition, complex models often fail to meet the computational requirements of edge-device deployment. Based on electronic toll collection (ETC) gantry data, this study proposes a causal–semantic spatiotemporal forecasting framework for long-term traffic flow prediction with a 24 h forecasting horizon. First, conditional Granger causality analysis is used to construct a directed causal prior graph that characterizes traffic propagation relationships among expressway segments. Second, scenario-semantic priors generated by a large language model are introduced to describe atypical traffic conditions. Then, causal structural priors and scenario-semantic priors are integrated into a teacher model and transferred to a lightweight student model through response-level and feature-level knowledge distillation. Experiments using expressway data from Hubei Province, China, show that the proposed model achieves the best overall performance in the typical scenario and competitive performance in the atypical scenario. The results indicate that the proposed framework can provide day-scale decision support for expressway law-enforcement UAV pre-deployment and enhance the spatial intelligence of traffic emergency management. Full article

(This article belongs to the Topic Digital and Intelligent Technologies and Application in Urban Construction, Operation, Maintenance, and Renewal)

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28 pages, 44648 KB

Open AccessArticle

A Terrain-Factor-Constrained GAN Model for Feature Preservation in DEM Downscaling

by Yanchen Wan, Haowen Jiang, Wenping Jiang, Yue Wang and Xinyue Lyu

ISPRS Int. J. Geo-Inf. 2026, 15(8), 353; https://doi.org/10.3390/ijgi15080353 - 4 Aug 2026

Abstract

Geographic information generalization underpins multi-scale spatial databases and cartography, and reliable DEM downscaling is critical to maintaining geomorphological consistency across map scales. Traditional DEM simplification and resampling methods rely on local geometric filtering and overlook global terrain structures, frequently causing structural distortions such [...] Read more.

Geographic information generalization underpins multi-scale spatial databases and cartography, and reliable DEM downscaling is critical to maintaining geomorphological consistency across map scales. Traditional DEM simplification and resampling methods rely on local geometric filtering and overlook global terrain structures, frequently causing structural distortions such as broken ridges and deformed slopes. This study proposes DD-GAN, a generative adversarial network constrained by terrain morphological factors for high-fidelity DEM downscaling. Built on a GAN architecture, the model embeds local relief and gradient as physical loss terms to prioritize major geomorphic skeletons and suppress trivial micro-terrain during resolution reduction, avoiding the indiscriminate over-smoothing of conventional sampling approaches. Multi-scale experiments covering downscaling factors ranging from 2× to 5× are conducted using mountainous datasets from Chongqing, Alaska, and Colorado. Quantitative and visual comparisons against raster interpolation, TIN-based simplification, and ordinary CNN show that DD-GAN mitigates terrain structural distortion and better retains elevation extremes and slope features, with more prominent strengths under large downscaling multiples. This physics-constrained deep learning paradigm provides an automated DEM downscaling solution that facilitates multi-scale terrain representation, supporting cartographic production and geomorphometric analysis. Full article

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30 pages, 4494 KB

Open AccessArticle

From Spatial Evolution to Low-Carbon Transition: Regional Heterogeneity and Stage Diagnosis of Carbon Emissions Across 19 Urban Agglomerations in China

by Ye Duan, Minghan Yang, Zhaowei Hou, Hongye Wang, Albert Fekete and Dongge Ning

ISPRS Int. J. Geo-Inf. 2026, 15(8), 352; https://doi.org/10.3390/ijgi15080352 - 4 Aug 2026

Abstract

Understanding the spatiotemporal dynamics of carbon emissions and developing differentiated governance strategies for urban agglomerations are essential for achieving regional low-carbon transformation. This study aims to identify the spatiotemporal patterns, driving mechanisms, and development-stage differences of carbon emissions across China’s urban agglomerations and [...] Read more.

Understanding the spatiotemporal dynamics of carbon emissions and developing differentiated governance strategies for urban agglomerations are essential for achieving regional low-carbon transformation. This study aims to identify the spatiotemporal patterns, driving mechanisms, and development-stage differences of carbon emissions across China’s urban agglomerations and to establish a type-specific governance framework. Based on multi-source geospatial and socioeconomic data from 19 urban agglomerations for the period 2006–2023, this study integrates spatial autocorrelation analysis, standard deviation ellipse analysis, hotspot analysis, random forest regression with SHAP interpretation, K-medoid clustering, and the Environmental Kuznets Curve (EKC) model to systematically examine emission evolution, influencing factors, and governance pathways. The results indicate the following: (1) carbon emissions in China’s urban agglomerations increased continuously during the study period and exhibited significant spatial heterogeneity, characterized by a “high east–low west” pattern, expanding eastern emission hotspots, and a gradual southwest shift in the emission centroid; (2) industrial structure and economic development level were identified as the dominant factors associated with carbon-emission differences, while energy efficiency, urbanization, and population density showed heterogeneous relationships across regions; (3) five carbon-emission development types were identified, including high-carbon high-development, transition-pressure, resource-dependent, stable-development, and low-carbon potential agglomerations, each exhibiting distinct development characteristics and governance requirements; and (4) EKC analysis revealed differentiated development stages among these types, suggesting that carbon governance should be tailored according to regional development conditions, dominant drivers, and emission-transition stages. This study provides an integrated geospatial modeling framework for understanding carbon-emission heterogeneity and offers scientific support for differentiated low-carbon planning and collaborative governance of urban agglomerations. Full article

(This article belongs to the Special Issue Novel Theories and Applications on Geo-Spatial Databases, Models and AI in Urban Science, Planning, Development and Governance)

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23 pages, 14928 KB

Open AccessArticle

A Direction-Aware Lightweight Network for Camera-Based Underground Mine Track Region Segmentation

by Haijun Li, Baolong Ma, Jianjun Gong, Dengyin Jiang, Jie Yang, Kuangang Fan and Zhichao Chen

ISPRS Int. J. Geo-Inf. 2026, 15(8), 351; https://doi.org/10.3390/ijgi15080351 - 4 Aug 2026

Abstract

Accurate localization of the visible track region is essential for perception using front-mounted cameras on underground rail-guided mine vehicles. The task is difficult because the track foreground occupies only a small image area, and its boundary appearance changes with illumination, water, dust, and [...] Read more.

Accurate localization of the visible track region is essential for perception using front-mounted cameras on underground rail-guided mine vehicles. The task is difficult because the track foreground occupies only a small image area, and its boundary appearance changes with illumination, water, dust, and scene clutter. This study formulates local perception of the track corridor as binary semantic segmentation of the surface bounded by the two visible rails. RailDLA is a lightweight encoder–decoder network. It combines track context preconditioning, RDLA directional strip propagation, context-guided feature fusion, and track axis proxy decoding. On a self-constructed dataset of underground mine vehicle imagery, RailDLA achieves 96.50% mIoU, 92.10% track IoU, 97.50% track accuracy, and 99.70% pixel accuracy. On the working split, its track IoU exceeds those of FastSCNN, PIDNet-S, DDRNet-23-slim, and SegNeXt-S by absolute margins of 6.06, 1.70, 1.56, and 0.37 percentage points, respectively. Under the unified runtime protocol, RailDLA reaches 120.00 FPS on an NVIDIA GeForce RTX 3070 Laptop GPU. These results demonstrate accurate, real-time inference for underground mine vehicle perception. Full article

(This article belongs to the Topic State-of-the-Art Object Detection, Tracking, and Recognition Techniques)

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57 pages, 7105 KB

Open AccessArticle

Fine-Grained Cultural Perception and Evaluation of Beijing’s Capital Culture Integrating Large Language Models with Higher-Order Tensor Decomposition

by Shihao Xi, Zhiyuan Ou, Bin Meng and Xiaohang Li

ISPRS Int. J. Geo-Inf. 2026, 15(8), 350; https://doi.org/10.3390/ijgi15080350 - 3 Aug 2026

Abstract

Fine-grained urban cultural perception is critical for GIScience, yet traditional social media studies struggle with complex cultural semantics and heterogeneous factor integration. Addressing Beijing’s “Capital Culture,” this study couples LLM agents with higher-order tensor decomposition. Using 2019 full-sample geotagged Sina Weibo data, we [...] Read more.

Fine-grained urban cultural perception is critical for GIScience, yet traditional social media studies struggle with complex cultural semantics and heterogeneous factor integration. Addressing Beijing’s “Capital Culture,” this study couples LLM agents with higher-order tensor decomposition. Using 2019 full-sample geotagged Sina Weibo data, we developed a four-agent collaborative architecture with Chain-of-Thought prompting and human-in-the-loop mechanisms via a locally deployed Qwen3-32B model. A four-way tensor (“Cultural Type–Evaluation Aspect–Sentiment Polarity–Spatial Carrier”) was constructed and integrated with kernel density estimation to characterize spatial differentiation. We address three questions: whether LLMs can reliably classify fine-grained cultural perceptions, how cultural types associate with evaluation dimensions, sentiments, and spatial carriers, and whether tensor decomposition reveals latent patterns beyond marginal frequencies. The agentic workflow achieves over 90% accuracy in cultural and sentiment classification, and the tensor decomposition attains a 94.87% goodness-of-fit, successfully identifying latent patterns. Spatially, Beijing’s capital culture exhibits an unbalanced hierarchical structure—“high coupling in the core area with differentiated expansion at the periphery.” This study validates the transition from “data-driven” to “AI + data dual-driven” spatial analysis, providing a quantifiable pathway for LLM-supported urban cultural governance. Full article

(This article belongs to the Special Issue LLM4GIS: Large Language Models for GIS)

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30 pages, 4122 KB

Open AccessArticle

Spatial Differentiation and Driving Mechanisms of County-Level Tourism Accessibility in Gansu Based on Multi-Dimensional Travel Cost Perspective

by Ruhu Gao, Wenkai Shi, Yuwei Wang, Zhennan Qi and Liangzhi Li

ISPRS Int. J. Geo-Inf. 2026, 15(8), 349; https://doi.org/10.3390/ijgi15080349 - 3 Aug 2026

Abstract

Tourism accessibility is an important indicator for assessing the coordinated development of transport and tourism. Using counties and districts in Gansu Province as the units of analysis, this study developed a three-dimensional evaluation framework comprising temporal accessibility, economic accessibility, and balanced accessibility, based [...] Read more.

Tourism accessibility is an important indicator for assessing the coordinated development of transport and tourism. Using counties and districts in Gansu Province as the units of analysis, this study developed a three-dimensional evaluation framework comprising temporal accessibility, economic accessibility, and balanced accessibility, based on real-world travel data between county and district centres and China’s A-rated tourist attractions obtained from the Amap API. Spatial autocorrelation analysis, the Geographical Detector, the Spatial Durbin Model (SDM), and Multiscale Geographically Weighted Regression (MGWR) were employed to systematically investigate the spatial patterns and driving mechanisms of tourism accessibility in Gansu Province. The results indicate that: (1) tourism accessibility exhibits significant spatial clustering, with high-value areas primarily concentrated in the Hexi Corridor and low-value areas mainly distributed in the mountainous regions of central and southern Gansu; (2) distance to the provincial capital, elevation, and the number of adjacent counties constitute the core determinants of tourism accessibility, while interactions among factors generally exhibit bi-factor enhancement or nonlinear enhancement effects; and (3) tourism accessibility exhibits significant spatial spillover effects and spatial heterogeneity, with the effects of different driving factors varying considerably across space. The findings provide a theoretical basis for optimising tourism transport and promoting balanced regional tourism development in Gansu Province. Full article

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64 pages, 28857 KB

Open AccessFeature PaperArticle

FCEND: A Fuzzy Cross-Efficiency GIS-DEA Framework for Equitable Logistics Network Design Under Deep Uncertainty

by Hossein Zangooei Dovom, Mir Saman Pishvaee and Hadi Sahebi

ISPRS Int. J. Geo-Inf. 2026, 15(8), 348; https://doi.org/10.3390/ijgi15080348 - 1 Aug 2026

Abstract

This study develops the Fuzzy Cross-Efficiency Network Design (FCEND) framework—an integrated Geographic Information System (GIS) and Data Envelopment Analysis (DEA) approach for logistics network design under deep uncertainty. Unlike conventional methods that ignore spatial equity and data credibility, FCEND combines GIS-based suitability mapping [...] Read more.

This study develops the Fuzzy Cross-Efficiency Network Design (FCEND) framework—an integrated Geographic Information System (GIS) and Data Envelopment Analysis (DEA) approach for logistics network design under deep uncertainty. Unlike conventional methods that ignore spatial equity and data credibility, FCEND combines GIS-based suitability mapping (30 m resolution, incorporating slope, land use, and floodplains), hybrid efficiency scores (

Φ_{j}

) integrating Cross-Efficiency DEA (CEDEA) peer evaluation with Fuzzy DEA (FDEA) uncertainty modeling, and a multi-objective function Z(S) = α·Efficiency(S) + β·H(S) − γ·Gini(S) that balances demand-weighted efficiency, portfolio-dependent criterion diversity (represented by the entropy term H(S)), and spatial equity. Applied to Iran’s staple food commodity network—85 million people across 1.65 million km²—FCEND identifies an optimal 15-node portfolio spanning 15 provinces with 74% direct population coverage within 150 km. The portfolio achieves a Gini coefficient of 0.298, and 9 of 15 nodes with excellent rail connectivity, while capturing strategically vital nodes (Borujerd, Bandar Abbas, Zahedan) overlooked by conventional approaches. Nine core sites with stability scores (f_j = 1.0) demonstrate perfect stability across all uncertainty scenarios. The framework’s modular architecture is conceptually transferable to emerging economies, as illustrated through adaptation to Vietnam (70% parameter swap). By integrating GIS-based spatial analysis, peer evaluation, fuzzy uncertainty, portfolio-dependent entropy, and equity constraints within a unified optimization framework, FCEND offers a transferable methodology for evidence-based logistics infrastructure planning—contributing directly to the United Nations Sustainable Development Goals (SDGs): SDG 2 (Zero Hunger), SDG 9 (Resilient Infrastructure), SDG 10 (Reduced Inequalities), and SDG 13 (Climate Action). Full article

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51 pages, 9273 KB

Open AccessArticle

Sustainable Urban Forms and Climate Adaptation Policy: A Sparsity-Responsiveness Framework Based on Chinese Cities

by Zhihan Zhang, Junyan Yang, Xilong Chen, Zhixiang Lin, Yuyue Huang, Qingxin Yang and Huaxing Sheng

ISPRS Int. J. Geo-Inf. 2026, 15(8), 347; https://doi.org/10.3390/ijgi15080347 - 1 Aug 2026

Abstract

Sustainability is recognized as a key driver for the formation and evolution of sustainable urban forms, serving as a critical approach for guiding and assessing urban sustainability. In the context of global warming, climate adaptation has become a pressing concern in shaping sustainable [...] Read more.

Sustainability is recognized as a key driver for the formation and evolution of sustainable urban forms, serving as a critical approach for guiding and assessing urban sustainability. In the context of global warming, climate adaptation has become a pressing concern in shaping sustainable urban forms. However, the complex mechanisms linking sustainability, urban form, and climate adaptation are difficult to identify and track due to variations in data precision, research scales, and stakeholder needs. This study first re-views the relevant literature to clarify the current state and trends in this field. It then employs locally weighted regression to analyze the relationships between sustainability principles, urban form, and climate adaptation from 2005 to 2024. Based on the “mitigation-adaptation” framework, sparsity-responsiveness indicators are constructed to define four types of climate adaptation. These types are used to classify 31 representative cities in China. Considering the cities’ developmental stages, the study proposes design strategies that prioritize sustainability. Full article

(This article belongs to the Topic Innovative Approaches in Geospatial Analysis and Modeling of Urban Environments)

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39 pages, 27151 KB

Open AccessArticle

Multi-Hazard Coastal Susceptibility Mapping Using Machine Learning and Deep Learning in Deltaic Louisiana

by Tanvir Hossain and Michael Leitner

ISPRS Int. J. Geo-Inf. 2026, 15(8), 346; https://doi.org/10.3390/ijgi15080346 - 1 Aug 2026

Abstract

Compound coastal hazards such as flooding, land subsidence, storm surge, and salinity intrusion impose accelerating risks on deltaic communities. This study presents a unified multi-hazard susceptibility mapping framework for Terrebonne Parish, Louisiana, modeling all four hazards from a common 30 m predictor stack, [...] Read more.

Compound coastal hazards such as flooding, land subsidence, storm surge, and salinity intrusion impose accelerating risks on deltaic communities. This study presents a unified multi-hazard susceptibility mapping framework for Terrebonne Parish, Louisiana, modeling all four hazards from a common 30 m predictor stack, with per-hazard exclusion of label-related predictors. Eight Machine Learning and Deep Learning algorithms were benchmarked per hazard against an ensemble meta-learner. Generalizability was assessed under three designs of increasing spatial rigor: blocked holdout, interleaved block cross-validation, and a strict contiguous-zone design with a 5 km buffer. Best holdout F1-macro ranged from 0.644 (salinity) to 0.923 (flood). Interleaved-block cross-validation was statistically indistinguishable from holdout; only the buffered contiguous-zone design revealed genuine transfer limits, with F1-macro declining 12–54 percentage points by hazard. Ensemble stacking did not improve upon cross-validation-guided single-model selection despite roughly five times the training cost. Salinity labels were derived from 21 kriged monitoring stations (RMSE = 3.40 PSU; R² = 0.82). A composite Multi-Hazard Susceptibility Index (mean = 0.675 parish-wide; 0.674 land-masked) identifies southern coastal Terrebonne as the priority zone for risk reduction, robust to reweighting of any single hazard. To our knowledge, this is the first framework to jointly map these four hazards while quantifying how validation design governs apparent model transferability. Full article

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25 pages, 21972 KB

Open AccessArticle

Dynamic Supply–Demand Matching and Spatial Mismatch Diagnosis of Emergency Beds in Designated Hospitals During Public Health Emergencies: A SEIQRDP-SG and 3SFCA-SMI Framework

by Ying Zhong, Sheng Jiao, Qingqing Zhang and Yizhe Ying

ISPRS Int. J. Geo-Inf. 2026, 15(8), 345; https://doi.org/10.3390/ijgi15080345 - 1 Aug 2026

Abstract

In the context of public health emergencies (PHEs), conventional static indicators are insufficient for capturing the dynamic supply–demand relationship of emergency medical care. Grounded in adaptive cycle theory, this study proposes a integrated analytical framework that sequentially integrates supply baseline identification, disturbance impact [...] Read more.

In the context of public health emergencies (PHEs), conventional static indicators are insufficient for capturing the dynamic supply–demand relationship of emergency medical care. Grounded in adaptive cycle theory, this study proposes a integrated analytical framework that sequentially integrates supply baseline identification, disturbance impact simulation, mismatch diagnosis, and zoning-based response optimization. Taking 475 communities and 18 major designated hospitals in the Changsha metropolitan area as the empirical case, we integrate AHP-CRITIC evaluation, the SEIQRDP-SG model, and the 3SFCA-SMI method to identify the spatial mismatch of emergency-bed supply and demand and to delineate planning response zones. The results show that: (1) emergency-bed supply exhibits marked agglomeration and quasi-Pareto polarization, with the top three districts accounting for 75.83% of effective emergency beds; (2) under the core scenario of R₀ = 5 with moderate intervention, peak bed demand in the metropolitan area reaches approximately 7835 beds around day 21, with high-demand communities emerging in high-density and high-mobility areas; and (3) although the overall supply–demand ratio is 1.34, 78% of communities cannot reach any designated hospital within 15 min, and the supply–demand pattern forms a compound spatial structure characterized by “central carrying, transitional mismatch, and peripheral weakness”. These findings indicate that the primary constraint on emergency medical resilience lies not in aggregate bed shortages alone, but in structural spatial mismatch jointly shaped by effective supply, dynamic demand, and transfer-time constraints. This study extends emergency medical facility evaluation from static assessment to dynamic matching and provides evidence for the layout of resilient “dual-use” medical facilities for routine and emergency conditions. Full article

(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T (2nd Edition))

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18 pages, 7463 KB

Open AccessArticle

From Geodata to Immersive Heritage Experiences: A Virtual Reality Case Study in Gorzów Wielkopolski, Poland

by Natalia Wicińska, Beata Medyńska-Gulij, Łukasz Halik and Anna Markowska

ISPRS Int. J. Geo-Inf. 2026, 15(8), 344; https://doi.org/10.3390/ijgi15080344 - 1 Aug 2026

Abstract

Virtual reality and geovisualization offer new ways to present, study, and experience geographic space, including urban cultural heritage. This article presents the process of creating a gamified VR application focused on selected preserved monuments in the centre of Gorzów Wielkopolski, Poland, a city [...] Read more.

Virtual reality and geovisualization offer new ways to present, study, and experience geographic space, including urban cultural heritage. This article presents the process of creating a gamified VR application focused on selected preserved monuments in the centre of Gorzów Wielkopolski, Poland, a city whose historic fabric was strongly affected by World War II. The aim of the project was not only to introduce users to the city’s cultural heritage, but also to encourage greater interest in, respect for, and appreciation of that heritage. The work was divided into four stages: conceptual design, data preparation, implementation, and publication/evaluation. The application was developed using geospatial data, orthophotography, LoD1 building models, field photographs, archival postcards, manual 3D modelling, interface design, and implementation in Unity. The final VR environment allows users to explore part of the city, view information about monuments, match historical postcards with buildings, receive feedback, collect points, and move between stations. The application was additionally evaluated through an online questionnaire completed by 20 students. The results indicated a generally positive perception of its educational and heritage-communication potential, while the realism of the vegetation appears to be an area that could benefit from further improvement. The case study shows that immersive geovisualization can support spatial understanding, engagement, and heritage communication, and presents a workflow that may be adapted for similar cultural heritage projects. Full article

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27 pages, 46923 KB

Open AccessArticle

Spatio-Temporal Dynamics of Bicycle Accidents in the Lisbon Metropolitan Area: An Integrated Emerging Hotspot Analysis

by Jonathan Sandoval and Bertha Santos

ISPRS Int. J. Geo-Inf. 2026, 15(8), 343; https://doi.org/10.3390/ijgi15080343 - 28 Jul 2026

Abstract

The growing adoption of cycling as part of the transition toward sustainable urban mobility, driven by climate change concerns and increasing congestion, has heightened the need to ensure cyclist safety in metropolitan areas. This study proposes an integrated spatio-temporal analytical framework to examine [...] Read more.

The growing adoption of cycling as part of the transition toward sustainable urban mobility, driven by climate change concerns and increasing congestion, has heightened the need to ensure cyclist safety in metropolitan areas. This study proposes an integrated spatio-temporal analytical framework to examine the evolution of reported bicycle–vehicle injury accidents in the Lisbon Metropolitan Area (LMA). The framework combines Geographic Information Systems (GIS)-based spatial statistics with Emerging Hotspot Analysis (EHA) to identify and track changes in accident clustering over time, across pre-, during-, and post-COVID-19 containment periods. This study contributes by applying Emerging Hotspot Analysis to bicycle accident data, an approach still largely unexplored, and by proposing a sequential and integrated framework that links traditional spatial analysis methods with dynamic hotspot detection and machine learning techniques, enabling a shift from static pattern identification to enhanced interpretation of evolving accident occurrence patterns and hotspot dynamics. Results reveal evidence of spatial consolidation and changing hotspot distributions over time, with emerging hotspots increasingly located in suburban transition zones and at the edges of existing cycling infrastructure. These patterns may reflect changes in mobility demand and infrastructure provision, although the absence of exposure data prevents a direct assessment of this relationship. Complementary analysis using forest-based machine learning models identifies key factors associated with hotspot formation and accident severity, including crash type, temporal patterns (e.g., day of the week), and environmental conditions such as slope and lighting. These findings highlight the value of combining spatio-temporal analysis with predictive modelling to support data-driven urban planning and targeted safety interventions. Lisbon provides a relevant case study for cities undergoing similar transitions toward sustainable transport systems. Full article

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