ISPRS International Journal of Geo-Information

23 pages, 1653 KB

Open AccessArticle

A Spatial Semantic-Guided Online Crime Spatiotemporal Prediction Model

by Huan Jiang, Miaoxuan Shan, Licheng Hao, Jinguang Sui and Peng Chen

ISPRS Int. J. Geo-Inf. 2026, 15(8), 340; https://doi.org/10.3390/ijgi15080340 (registering DOI) - 24 Jul 2026

Accurate crime spatiotemporal prediction is crucial for crime prevention. However, crime occurrences are influenced by diverse and interacting social factors, resulting in dynamically evolving distributions with non-stationarity and spatial heterogeneity. Most existing methods focus on data preprocessing or architectural enhancements and remain offline [...] Read more.

Accurate crime spatiotemporal prediction is crucial for crime prevention. However, crime occurrences are influenced by diverse and interacting social factors, resulting in dynamically evolving distributions with non-stationarity and spatial heterogeneity. Most existing methods focus on data preprocessing or architectural enhancements and remain offline models, which limits their generalization capability. To address these challenges, we propose a novel spatial semantic-guided online learning framework. Specifically, we first compute the spatial semantic similarity between urban regions using points of interest. Based on this, we then introduce a contrastive learning objective guided by this similarity during training. This design aims to enhance the model’s ability to capture both the similarities and discrepancies among regions. During the prediction process, an iterative online learning strategy is employed to adapt to dynamically changing crime patterns. By continuously fine-tuning the model with streaming data, the proposed framework improves robustness and generalization under non-stationary crime spatiotemporal distributions. Finally, extensive experiments on real-world crime datasets indicate the effectiveness and stability of our proposed approach. Full article

18 pages, 2304 KB

Open AccessArticle

Exploring Effects of Boundaries on Path Integration—An Approach to Link Spatial Navigation Performance to Brain Activity Concepts

by Denise O’Meara, Julian Keil, Cara Oster, Dennis Edler, Annika Korte and Frank Dickmann

ISPRS Int. J. Geo-Inf. 2026, 15(8), 339; https://doi.org/10.3390/ijgi15080339 - 24 Jul 2026

Abstract

Digital maps increasingly replace paper maps because they are accessible, regularly updated, and customizable. Yet they often weaken spatial orientation skills and create technological dependency. One possible solution is supporting navigation without extra cognitive effort by designing maps that address spatially responsive brain [...] Read more.

Digital maps increasingly replace paper maps because they are accessible, regularly updated, and customizable. Yet they often weaken spatial orientation skills and create technological dependency. One possible solution is supporting navigation without extra cognitive effort by designing maps that address spatially responsive brain cells. These cells are thought to be involved in the construction of an internal spatial representation. As animal studies have shown that the perception of environmental boundaries contributes to the stabilization of firing behavior, we examined boundary effects on path integration (PI) in screen-based and virtual reality (VR) settings. This allowed us to test whether the effect is robust across formats with different immersion and self-motion feedback. Participants completed PI tasks in a virtual arena while viewing a briefly displayed elevated line, wall, or no artificial boundary. It was expected that perceived boundaries would stabilize the activity of spatially responsive cells, such as grid cells. This is likely to contribute to a reduction in PI errors. Results showed a supportive tendency for the line condition, whereas the wall condition produced the highest errors. This pattern was comparable across both media. The findings suggest that the effect of spatial boundary cues depends on their design and perceptual properties. Full article

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31 pages, 2922 KB

Open AccessArticle

Geospatial Analysis of the Evolution of European Tourism in Spain Using Mobile Phone Data, the Space–Time Cube, and Emerging Hot Spot Analysis

by José Manuel Sánchez-Martín, Felipe Leco-Berrocal and Ana Beatriz Mateos-Rodriguez

ISPRS Int. J. Geo-Inf. 2026, 15(8), 338; https://doi.org/10.3390/ijgi15080338 - 24 Jul 2026

Abstract

In Spain, inbound European tourism exhibits marked territorial imbalances whose evolution is difficult to characterize using aggregate indicators. This study analyzes its spatiotemporal patterns at the municipal level between July 2019 and December 2025 based on experimental statistics from the National Institute of [...] Read more.

In Spain, inbound European tourism exhibits marked territorial imbalances whose evolution is difficult to characterize using aggregate indicators. This study analyzes its spatiotemporal patterns at the municipal level between July 2019 and December 2025 based on experimental statistics from the National Institute of Statistics compiled using mobile phone data. The objective is to identify processes of growth, persistence, and spatial intensification using a geospatial methodology based on the Space–Time Cube (STC) and Emerging Hot Spot Analysis (EHSA). The analysis covers the 1000 municipalities with the highest cumulative volume of European tourists, which account for most of the flows recorded during the period. The results show positive and statistically significant temporal trends in 911 municipalities, although the formation of persistent spatial clusters is considerably less widespread. EHSA identified 48 municipalities classified as hot spots when applying a one-month temporal neighborhood and 77 when using a three-month configuration. The two classifications showed an observed agreement of 96.0% and, for the four shared categories, a Cohen’s kappa coefficient of 0.660. The post-pandemic recovery in tourism did not, therefore, result in a homogeneous territorial consolidation of stable spatial patterns. We identify persistent hubs, areas undergoing intensification, and destinations with episodic behavior, located primarily in metropolitan, coastal, and island areas. The main contribution of the study lies in the development of a reproducible workflow based on the STC–EHSA integration, capable of distinguishing between temporal growth, persistence, intensification, and spatial intermittency, and of evaluating the stability of the results under different temporal neighborhood configurations. Full article

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35 pages, 30465 KB

Open AccessArticle

A Policy-Derived Multi-Tiered Analytical Framework for Assessing the Beautiful China Goals (BCGs) Implementation at the Urban Agglomeration Scale

by Yuxuan Wang, Ze Tian, Xiaodong Jing and Mengyao Li

ISPRS Int. J. Geo-Inf. 2026, 15(7), 337; https://doi.org/10.3390/ijgi15070337 - 22 Jul 2026

Abstract

To advance environmental sustainability, China proposed the Beautiful China Goals (BCGs) as its localized strategy, with urban agglomerations serving as the key implementation scale. To address the limitations of difficulty in identifying key tasks and insufficient regional applicability, this study develops a multi-goal [...] Read more.

To advance environmental sustainability, China proposed the Beautiful China Goals (BCGs) as its localized strategy, with urban agglomerations serving as the key implementation scale. To address the limitations of difficulty in identifying key tasks and insufficient regional applicability, this study develops a multi-goal evaluation system comprising 21 goals and 52 indicators rooted in the policy framework. Methodologically, a three-tiered assessment framework—goal, city, and region—is constructed for urban agglomerations, integrating spatial-temporal analysis, city-level two-dimensional diagnostics, and regional synergy quantification. The framework is applied to the Yangtze River Delta Urban Agglomeration (YRDUA), a national-level pilot area for the BCGs, over the period 2015–2023. Results indicate that: (1) progress toward the BCGs in the YRDUA increased by 5.7%, but full achievement by 2035 remains unlikely. Significant structural imbalances exist among the 21 goals, with infrastructure-related goals scoring higher than those related to institutional development, innovation, and carbon neutrality. Spatially, BCGs’ performance follows a “high southeast, low northwest” pattern, although distribution varied by goal, and regional equity has improved. (2) Fewer than half of the 41 cities had achieved “double high” states in both development magnitude and evenness by 2023, with cities following four distinct development pathways that reflect differing priorities and strategies for goal attainment. (3) Intercity cooperation in advancing the BCGs remains limited. Synergistic effects are relatively stronger for green production goals but weaker for ecological, technological, and institutional goals, with Ningbo, Suzhou, and Shaoxing emerging as key contributors to regional synergy. This framework offers a replicable tool for regional environmental planning and provides evidence for BCGs implementation strategies in China and beyond. Full article

(This article belongs to the Topic Sustainable Development and Coordinated Governance of Urban and Rural Areas Under the Guidance of Ecological Wisdom—3rd Edition)

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16 pages, 1727 KB

Open AccessArticle

The Moderating Role of Street-View Greenery in the Relationship Between Mental Health and Life Satisfaction: An Exploratory Case Study Across Contrasting Community Contexts in Korea

by Yoohyung Joo, Jaeyoung Jung, Jiwan Hong, Sangyoon Park, Jaelim Cho, Juyeon Ko, Changsoo Kim and Joon Heo

ISPRS Int. J. Geo-Inf. 2026, 15(7), 336; https://doi.org/10.3390/ijgi15070336 - 22 Jul 2026

Abstract

Growing evidence suggests that urban greenery is associated with improved mental health, yet how eye-level exposure functions within specific socio-environmental contexts remains underexplored. This study presents an exploratory case study utilizing semantic segmentation of street-view imagery to quantify eye-level greenery (“open greenery”) across [...] Read more.

Growing evidence suggests that urban greenery is associated with improved mental health, yet how eye-level exposure functions within specific socio-environmental contexts remains underexplored. This study presents an exploratory case study utilizing semantic segmentation of street-view imagery to quantify eye-level greenery (“open greenery”) across two contrasting community contexts: a densely developed area (Region 1) and a less developed area (Region 2) in Korea. Using interaction models reinforced by 5000-iteration bootstrap analyses, we identified the moderating role of greenery in the relationship between mental health (depression and cognitive function) and life satisfaction. Our findings indicate that the psychological benefits of greenery are highly contingent upon the interplay between individual vulnerability and regional context. Specifically, in Region 1, greenery moderated well-being for the low-cognitive function subgroup, while in Region 2, the moderating effect was most pronounced among individuals with depressive symptoms. Despite the inherent limitations of small subgroup samples, the stability of these patterns across repeated bootstrap iterations highlights meaningful “spatial intersections” where greenery plays a role in shaping psychological well-being. By adopting a case-centric approach, this study highlights that the benefits of street-view greenery are not uniform but context-dependent. These results underscore the necessity of context-aware green infrastructure strategies tailored to the specific environmental needs of vulnerable populations in diverse community settings. Full article

(This article belongs to the Topic Applications of Spatial Science and Technology in Health Research, 2nd Edition)

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23 pages, 7971 KB

Open AccessArticle

Geo-InkGAN: An Adaptive Generative Framework for Topographically Faithful Ink-Wash Style Transfer in Terrain Mapping

by Songyuan Gao and Daping Xi

ISPRS Int. J. Geo-Inf. 2026, 15(7), 335; https://doi.org/10.3390/ijgi15070335 - 21 Jul 2026

Abstract

The compelling visualization of Digital Elevation Models (DEMs) constitutes a vital intersection between Geographic Information Science (GIS) and the digital humanities. Nevertheless, traditional Generative Adversarial Networks (GANs) frequently demonstrate a “geography-blind” characteristic, resulting in structural “topographic drift” by dissociating geomorphic complexity from cartographic [...] Read more.

The compelling visualization of Digital Elevation Models (DEMs) constitutes a vital intersection between Geographic Information Science (GIS) and the digital humanities. Nevertheless, traditional Generative Adversarial Networks (GANs) frequently demonstrate a “geography-blind” characteristic, resulting in structural “topographic drift” by dissociating geomorphic complexity from cartographic constraints. To overcome this limitation, we propose Geo-InkGAN, a geo-heuristic framework that integrates geographic principles with generative processes to achieve high-fidelity ink-wash style synthesis. A key component of our approach is an adaptive optimization strategy grounded in the Slope Standard Deviation (SSD). By establishing a quantitative relationship between geomorphological entropy and the cycle-consistency loss weight (

λ_{c y c}

), we effectively address the Pareto trade-off between geomorphic accuracy and esthetic representation. Our results indicate that alluvial plains benefit from low-intensity constraints to facilitate fluid ink diffusion, whereas rugged terrains require high-intensity constraints to maintain the integrity of the topological framework. Additionally, the HCEG-SE mechanism (Hillshade-Contour Edge-Guided Stroke Enhancement) narrows the semantic divide between terrain skeletons and artistic textures by combining multi-directional non-photorealistic rendering with precise edge extraction techniques. Evaluated across five geomorphologically diverse regions—from karst towers to loess plateaus—Geo-InkGAN demonstrably surpasses existing benchmarks in Geomorphological Structure Correlation (GSC). This geomorphology-aware approach advances the scientific rigor of AI-driven cartography and offers a refined methodology for the cultural representation of digital twin landscapes. Full article

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22 pages, 16612 KB

Open AccessArticle

Paleoenvironmental Changes and Human Adaptation: A Multidisciplinary Investigation of Site Abandonment at Qusayrat Aad Archaeological Site, Central Saudi Arabia

by Mohamed Metwaly and Abdullah Alshami

ISPRS Int. J. Geo-Inf. 2026, 15(7), 334; https://doi.org/10.3390/ijgi15070334 - 21 Jul 2026

Abstract

This study examines the critical relationship between geoenvironmental changes and human occupation patterns in the central Arabian Peninsula, focusing on the Al-Aflaj region. By integrating geospatial modeling with preliminary archeological excavation results that indicated the site is dated to 5th century BCE to [...] Read more.

This study examines the critical relationship between geoenvironmental changes and human occupation patterns in the central Arabian Peninsula, focusing on the Al-Aflaj region. By integrating geospatial modeling with preliminary archeological excavation results that indicated the site is dated to 5th century BCE to 6th century CE, we evaluate how climatic stressors dictated human adaptation and eventual site abandonment. Late Quaternary climatic fluctuations, particularly the Early Holocene pluvial phases, initially created favorable conditions for settlement through sustained freshwater resources. Subsequent aridification triggered significant migration and settlement contraction, demonstrating a high degree of human resilience. The site of Qusayrat Aad serves as a compelling case study of sophisticated adaptation, evidenced by mudbrick architecture and advanced irrigation systems. Integration of geological, topographic, and paleoclimatic factors indicates that the porous sedimentary layers of the Heet and Al Biyadh Formations were essential for groundwater recharge and spring formation. The geospatial analysis reveals that the settlement was strategically localized on a stable surface with a mean slope of 1.51°. Furthermore, the Topographic Wetness Index (TWI) identifies the site as a significant hydrological function, with a mean value of 8.79 (reaching a 90th percentile of 12.37), which is markedly higher than the regional average of 7.96. These quantitative findings establish a causal necessity for the site’s advanced subsurface canal systems as an engineered response to minimize evaporative losses in high-potential moisture zones. Ultimately, the correlation between the archeological record and climatic proxies suggests that the intensification of late Holocene aridification depleted these specific water resources beyond adaptive capacity, serving as the primary driver for the site’s abandonment and the migration of populations toward the eastern and southern parts of the Peninsula. Full article

(This article belongs to the Topic Climate Change Impacts and Adaptation: Interdisciplinary Perspectives, 2nd Edition)

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20 pages, 1231 KB

Open AccessArticle

Meta-FedGeo: Adaptive Federated Learning with Spatiotemporal Transformers for Urban GeoAI in Smart Cities

by Rosny Jean and Stabak Roy

ISPRS Int. J. Geo-Inf. 2026, 15(7), 333; https://doi.org/10.3390/ijgi15070333 - 20 Jul 2026

Abstract

This study introduces Meta-FedGeo, a federated learning framework that integrates meta-learning and spatiotemporal transformers to address key challenges in urban GeoAI for smart cities. Data streams in smart city environments are inherently non-stationary and heterogeneous, limiting the adaptability of traditional federated learning approaches. [...] Read more.

This study introduces Meta-FedGeo, a federated learning framework that integrates meta-learning and spatiotemporal transformers to address key challenges in urban GeoAI for smart cities. Data streams in smart city environments are inherently non-stationary and heterogeneous, limiting the adaptability of traditional federated learning approaches. Meta-FedGeo overcomes these limitations through a hybrid centralised–decentralised architecture that pre-trains a global model using meta-learning to capture cross-city spatiotemporal patterns and dynamically refines it through federated updates. The framework incorporates performance-aware client selection and temporally weighted aggregation to enhance model robustness and convergence. To model complex urban dynamics, the proposed system employs a Spatio-Temporal Transformer (ST-Transformer). In addition, an Uncertainty-Calibrated Decision Engine (UCDE) is introduced to align model predictions with accessibility and urban planning constraints. Unlike static federated methods, Meta-FedGeo can dynamically identify and filter malicious or low-quality clients using local validation loss, while Shapley value-based mechanisms support efficient and fair knowledge transfer across distributed nodes. To clarify the scope of the present study, Meta-FedGeo is reported as a partially implemented research prototype: the ST-Transformer backbone, the meta-learning initialisation, the validation-loss-based client filtering and the temporal-weighted aggregation were implemented and evaluated on partitioned real-world datasets, whereas the Shapley-value contribution assessment, the Lightweight Data Harmonisers (LDHs) and the UCDE are presented as architectural components with proof-of-concept implementations whose full empirical validation is identified as future work. The framework is designed for seamless integration with existing urban infrastructure without requiring major modifications. Experimental results using real-world urban datasets partitioned into non-IID federated clients indicate improved predictive performance and faster convergence relative to the federated baselines considered here. Overall, Meta-FedGeo advances GeoAI toward scalable, adaptive, and practical applications in smart city environments. Full article

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20 pages, 3589 KB

Open AccessArticle

Research on Incremental Geometrical Reconstruction Method of Building Structures Based on Point Clouds

by Xian Cao, Changyu Qian, Hanqiang Deng, Xiangrong Ni, Hao Chen, Lun Zhang and Jian Huang

ISPRS Int. J. Geo-Inf. 2026, 15(7), 332; https://doi.org/10.3390/ijgi15070332 - 20 Jul 2026

Abstract

With the development of intelligent unmanned systems, it is important for indoor mobile mapping and structural perception to reconstruct building structures in a timely manner from sequential LiDAR point clouds. However, many existing reconstruction methods rely on complete or accumulated point clouds, making [...] Read more.

With the development of intelligent unmanned systems, it is important for indoor mobile mapping and structural perception to reconstruct building structures in a timely manner from sequential LiDAR point clouds. However, many existing reconstruction methods rely on complete or accumulated point clouds, making them less suitable for partial observations, occlusions, and continuous updates. This paper proposes an incremental geometric reconstruction framework for building structures based on LiDAR point clouds. The method combines temporal state inheritance and orthogonal projection to transform 3D point-cloud processing into 2D plane-based contour updating. A transmissive relationship-based hole detection strategy is introduced to preserve real openings such as doors and windows while completing partially unobserved regions. Simulation and real-world experiments show that the proposed method can recover major planar building structures. In the simulation scene, the proposed method achieves a CD-L1 of 0.088 m, a CD-L2 of 0.007 m², and an F1-score of 0.901, with an average single-frame processing time of 1.02 s. The experimental results indicate that the proposed method provides a compact and interpretable plane-based structural representation for near-real-time incremental reconstruction of building structures from sequential LiDAR point clouds. Full article

(This article belongs to the Special Issue Indoor Mobile Mapping and Location-Based Knowledge Services)

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18 pages, 7251 KB

Open AccessArticle

A GIS-Based Analysis of the Spatiotemporal Evolution and Driving Mechanisms of Rural Settlements in an Ethnic Minority Region: Evidence from Fuxin Mongolian Autonomous County, China

by Xinshuang Zhang, Sihan Li and Jun Yang

ISPRS Int. J. Geo-Inf. 2026, 15(7), 331; https://doi.org/10.3390/ijgi15070331 - 18 Jul 2026

Abstract

Understanding the spatiotemporal evolution of rural settlements in ethnic minority regions is essential for coordinated rural development, cultural landscape conservation, and rural revitalization. Taking Fuxin Mongolian Autonomous County in Northeast China as a case study, this study examined rural settlement patterns and their [...] Read more.

Understanding the spatiotemporal evolution of rural settlements in ethnic minority regions is essential for coordinated rural development, cultural landscape conservation, and rural revitalization. Taking Fuxin Mongolian Autonomous County in Northeast China as a case study, this study examined rural settlement patterns and their driving mechanisms from 2000 to 2024 using GIS-based spatial analysis, landscape pattern metrics, and the optimal parameter-based geographical detector (OPGD) model. A multidimensional indicator system was constructed from four dimensions: natural environment, production-resource environment, ethnic–cultural environment, and socioeconomic environment. The results show that rural settlements remained significantly clustered, although clustering gradually weakened, with average nearest-neighbor ratios increasing from 0.7729 in 2000 to 0.8370 in 2024. High agglomeration was mainly concentrated in the southern and southeastern areas, whereas low agglomeration occurred in the western and northwestern areas. Annual average temperature had the strongest explanatory power (q = 0.2492), followed by road network density (q = 0.1786) and elevation (q = 0.1716), indicating that thermal conditions, transportation accessibility, and topographic constraints were dominant drivers. All two-factor interactions showed enhancement effects, suggesting a coupled rather than single-factor mechanism. Ethnic–cultural variables had relatively lower q-values but remain important for interpreting cultural continuity, heritage conservation value, and differentiated rural development. Full article

(This article belongs to the Topic Sustainable Development and Coordinated Governance of Urban and Rural Areas Under the Guidance of Ecological Wisdom—3rd Edition)

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34 pages, 6573 KB

Open AccessArticle

Exploratory Vulnerability Assessment of the Urban Ecological Security Pattern in Bogotá: Static and Dynamic Attack Simulations and Cascading-Failure Modelling in a Global South City

by Jose David Martinez Otalora, Jie Shen and Anyela Piedad Rojas Celis

ISPRS Int. J. Geo-Inf. 2026, 15(7), 330; https://doi.org/10.3390/ijgi15070330 - 18 Jul 2026

Abstract

The Ecological Security Pattern (ESP), composed of ecological sources, resistance surfaces, and corridors, provides a spatial basis for mitigating urban landscape fragmentation and sustaining ecological security. However, most urban ESP studies have focused on its spatial delimitation, while the assessment of network vulnerability [...] Read more.

The Ecological Security Pattern (ESP), composed of ecological sources, resistance surfaces, and corridors, provides a spatial basis for mitigating urban landscape fragmentation and sustaining ecological security. However, most urban ESP studies have focused on its spatial delimitation, while the assessment of network vulnerability under disturbance remains limited. This study applies an integrated, exploratory, and model-based methodological framework that combines ESP mapping, ecological network analysis, attack simulation, and load–capacity cascading failure modelling to generate simulated indications of the potential vulnerability of the urban ecological network of Bogota. The results identified 58 ecological sources with a combined area of 123.02 km² (19.58% of the study area) and 107 active corridors. In the simulations, sources N540, N433, and N847 showed the highest topological relevance, whereas sources N933, N337, and N847 concentrated the greatest functional importance. In the disturbance simulations, the network showed greater relative robustness to random removals; in contrast, degree- and betweenness-targeted removals produced a more accelerated loss of the connected component, whereas degree- and PageRank-based perturbations accelerated simulated functional degradation. In the dynamic scenario analyzed, the model organized the network into four risk levels and suggested indirect and multi-stage trajectories of simulated potential failure propagation. These findings contribute to the exploratory diagnosis of ESP functional vulnerability and provide preliminary, simulation-based spatial criteria to guide exploratory ecological prioritization analyses and scenario assessment in Bogotá and dense Global South metropolises. Full article

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

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24 pages, 20538 KB

Open AccessArticle

Application of an Interpretable Machine Learning Model to Archaeological Site Prediction: A Case Study of the Three Gorges Region in Chongqing

by Xiaoli Wang, Houxi Zou, Hao Chen and Yani Cao

ISPRS Int. J. Geo-Inf. 2026, 15(7), 329; https://doi.org/10.3390/ijgi15070329 - 18 Jul 2026

Abstract

The construction of the Archaeological Site Prediction Model (ASPM) and quantitative research on the driving mechanisms of influencing factors are key to better understanding the multidimensional interactions between ancient humans and the environment. They also constitute an essential technical approach for guiding field [...] Read more.

The construction of the Archaeological Site Prediction Model (ASPM) and quantitative research on the driving mechanisms of influencing factors are key to better understanding the multidimensional interactions between ancient humans and the environment. They also constitute an essential technical approach for guiding field archaeological survey and excavation. This study aims to develop a highly stable, accurate, and interpretable predictive model for archaeological sites in the Chongqing Three Gorges region (CQTGR). BP neural network prediction (BPNN) has been applied in various fields, but its random initial weights and thresholds often lead to suboptimal accuracy and weak interpretability. To address these issues, this study constructs BPNN optimized with a Bayesian algorithm to enhance its accuracy. Additionally, it integrates the SHAP model to quantitatively identify nonlinear interactions and threshold effects of influencing factors, thereby improving its interpretability. The results indicate that: (1) The BPNN-based prediction model outperforms other conventional models. After hyperparameter optimization using the Bayesian algorithm, the AUC (area under the ROC curve) on the test set increases by 0.0812, reaching a final value of 0.8815. This indicates that the optimization model is effective and that the model exhibits strong predictive capability. (2) Archaeological sites exhibit a tiered and linear corridor distribution pattern along the Yangtze River and its major tributaries. This pattern can be divided into five concentric tiers radiating outward from the core of the main river systems. High-probability zones are particularly clustered in the low-lying and flat river valleys. (3) The distribution of archaeological sites is comprehensively influenced by both the natural environment and human activities. Elevation and river systems are key driving factors, with NDVI and land use also exerting significant influence. The major factors demonstrate notable threshold and interaction effects. Areas where the interaction between factors exhibits positive enhancement are often the core areas of archaeological site distribution. This research not only provides a precise scientific basis for the preventive protection and monitoring of potential distribution areas of archaeological sites but also offers decision support for the spatial conservation planning of these sites. Full article

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24 pages, 19595 KB

Open AccessArticle

Context-Aware Modeling of Morphology–Performance Associations and Cross-Temporal Generalization Using Variational Autoencoder Latent Representations

by Chengyu Sun, Xinru Wang and Yu Meng

ISPRS Int. J. Geo-Inf. 2026, 15(7), 328; https://doi.org/10.3390/ijgi15070328 - 17 Jul 2026

Abstract

A better understanding of the associations between urban morphology and performance can support more evidence-based urban governance and design evaluation. However, conventional hand-crafted metrics may omit important configurational information, making it difficult to model morphology–performance relationships consistently across multiple performance domains and under [...] Read more.

A better understanding of the associations between urban morphology and performance can support more evidence-based urban governance and design evaluation. However, conventional hand-crafted metrics may omit important configurational information, making it difficult to model morphology–performance relationships consistently across multiple performance domains and under changing urban conditions. To address this issue, this study tests a modeling pathway based on variational autoencoder (VAE) latent representations of urban morphology, combined with neighborhood de-averaging to reduce the influence of locational context, and evaluates it through a four-phase design covering explanatory gain, cross-dimensional response, contextual-scale and spatial robustness, and temporal generalizability. Using Shanghai as the empirical case, the results show that the latent-representation modeling pathway consistently outperforms multiple hand-crafted metric-based baselines. This relative advantage remained evident across multiple contextual scales, under spatially grouped validation, and in temporal validation. The pathway enables complex urban morphology to enter multidimensional performance models through a unified representation and has practical application potential for data-driven urban evaluation and performance-oriented planning support. Full article

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24 pages, 9228 KB

Open AccessArticle

Short-Term Metro Passenger OD Demand Forecasting Based on Low-Rank Tensor Network Extended Kalman Filter

by Aijing Su, Bing Wu and Xiaoxing Fang

ISPRS Int. J. Geo-Inf. 2026, 15(7), 327; https://doi.org/10.3390/ijgi15070327 - 17 Jul 2026

Abstract

Accurate short-term metro origin–destination (OD) demand forecasting is essential for intelligent passenger flow management and urban rail transit operation. However, forecasting large-scale metro OD demand remains challenging due to its high dimensionality, nonlinear spatiotemporal dependencies, and demand uncertainty. To address these challenges, this [...] Read more.

Accurate short-term metro origin–destination (OD) demand forecasting is essential for intelligent passenger flow management and urban rail transit operation. However, forecasting large-scale metro OD demand remains challenging due to its high dimensionality, nonlinear spatiotemporal dependencies, and demand uncertainty. To address these challenges, this paper proposes a Tensor Network Extended Kalman Filter (TNEKF) framework for short-term metro OD demand forecasting. First, metro OD demand is formulated as a nonlinear dynamic state-space prediction problem, where a multi-input multi-output Volterra model is adopted to characterize the nonlinear relationship between historical passenger demand and future OD flows. An Extended Kalman Filter (EKF) is then developed to recursively estimate the latent model parameters and continuously refine demand prediction using newly available observations. To improve computational efficiency for high-dimensional OD systems, both the latent state vector and covariance matrix are represented using low-rank tensor network structures, and all recursive filtering operations are implemented through tensor-network contractions without explicitly constructing large-scale matrices. Experiments on real-world smart-card data from the Hangzhou metro system demonstrate that the proposed method consistently outperforms ARIMA, conventional EKF, and several state-of-the-art spatiotemporal prediction models in terms of MAE, RMSE, and MAPE. Compared with the best-performing baseline of the whole-day scenario, the proposed method reduces MAE, RMSE, and MAPE by 30.2%, 9.8%, and 6.3%, respectively. Furthermore, the proposed framework exhibits strong robustness under disruption scenarios, demonstrating its effectiveness and scalability for large-scale metro OD demand forecasting. Full article

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32 pages, 1953 KB

Open AccessArticle

User Experience-Based Evaluation of Tactile Map Production Methods for Wayfinding Among People with Visual Impairments

by Ayça Eraslan, Ahmet Özgür Doğru and Nesibe Necla Uluğtekin

ISPRS Int. J. Geo-Inf. 2026, 15(7), 326; https://doi.org/10.3390/ijgi15070326 - 16 Jul 2026

Abstract

Tactile maps play a critical role in supporting spatial learning and independent mobility for people with visual impairments, particularly in complex environments such as university campuses. This study evaluates the effects of two tactile map production methods, 3D printing and heat-sensitive embossed paper, [...] Read more.

Tactile maps play a critical role in supporting spatial learning and independent mobility for people with visual impairments, particularly in complex environments such as university campuses. This study evaluates the effects of two tactile map production methods, 3D printing and heat-sensitive embossed paper, through a user-centered mixed-methods experimental design. The research was conducted on the North Campus of Boğaziçi University and involved 15 university students with visual impairments. The experimental process consisted of two stages: (1) controlled tactile map perception and evaluation, and (2) real-world wayfinding and on-site navigation experience. Quantitative data were collected through structured questionnaires and analyzed using descriptive statistics, while qualitative data were obtained through observations, open-ended feedback, and thematic analysis. The findings suggest that tactile map production methods may influence tactile perception, mental map formation, and user confidence. Participants generally reported that 3D-printed maps provided clearer tactile spatial organization and supported mental mapping in complex environments, whereas embossed paper maps offered advantages in portability and rapid accessibility. Full article

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32 pages, 45084 KB

Open AccessArticle

A Multidimensional Spatial–Temporal and Econometric Framework for Pedestrian Safety and Injury Severity Analysis in Amman, Jordan

by Haitham A. Al Hasanat, Omar Alharasees, Lafee Alshamaileh and Rana Al-Matarneh

ISPRS Int. J. Geo-Inf. 2026, 15(7), 325; https://doi.org/10.3390/ijgi15070325 - 16 Jul 2026

Abstract

This study presents a comprehensive multidimensional analysis of pedestrian accidents in Amman, Jordan, from 2014 to 2023. By integrating spatial, temporal, and statistical techniques, the research identifies critical risk patterns to inform evidence-based safety interventions. Characterizing a decade-long database of 14,821 cases, the [...] Read more.

This study presents a comprehensive multidimensional analysis of pedestrian accidents in Amman, Jordan, from 2014 to 2023. By integrating spatial, temporal, and statistical techniques, the research identifies critical risk patterns to inform evidence-based safety interventions. Characterizing a decade-long database of 14,821 cases, the study utilizes radar graphs, Kernel Density Estimation (KDE), and DBSCAN cluster analysis to delineate high-risk zones and temporal peaks. Temporal findings indicate that Thursdays recorded the highest accident frequency (2382 cases), with peak occurrences between 17:00 and 23:00. Spatial clustering identified five significant high-risk zones, with Central Amman emerging as the primary critical area. The study’s novelty lies in being the first in the Jordanian context to bridge accident frequency with severity mechanisms by integrating advanced spatial clustering and KDE with a robust Ordered Logit Model. Severity analysis reveals that while 59.34% of incidents resulted in minimal injuries, fatalities accounted for 5.02%. The model demonstrates that injury outcomes are systematically associated with traffic dynamics and behavior rather than environmental factors. Speed-related driver error was identified as the strongest predictor of severe outcomes (OR = 81.3). Significant dependencies were confirmed between vehicle category and road type (χ² = 2182.20, p < 0.001), lighting and road surface (χ² = 76.21, p < 0.001), and vehicle type and lighting (χ² = 148.52, p < 0.001). The study proposes a multi-layered framework combining site-specific nodal improvements with corridor-level strategies to enhance urban safety in Amman City. Full article

(This article belongs to the Special Issue Innovative Mobility Services for Smart Cities)

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18 pages, 2704 KB

Open AccessArticle

A GIS Based Spatio-Temporal Analysis of Socioeconomic and Environmental Determinants of Child Malnutrition in Pakistan

by Muhammad Usman, Katarzyna Kopczewska and Mudassar Rashid

ISPRS Int. J. Geo-Inf. 2026, 15(7), 324; https://doi.org/10.3390/ijgi15070324 - 16 Jul 2026

Abstract

Child malnutrition remains a critical global health challenge, yet most existing studies rely on static risk estimates and overlook the spatial–temporal nature of environmental exposures and localized socioeconomic disparities. To address this gap, we integrated Earth observation-derived environmental indicators, geolocated conflict events, socioeconomic [...] Read more.

Child malnutrition remains a critical global health challenge, yet most existing studies rely on static risk estimates and overlook the spatial–temporal nature of environmental exposures and localized socioeconomic disparities. To address this gap, we integrated Earth observation-derived environmental indicators, geolocated conflict events, socioeconomic variables, and child health outcomes, and applied a Fixed Effects Two-Stage Least Squares Spatial Durbin Error Model (FE-2SLS-SDEM). We found distinct hotspots of joint vulnerability, where areas experiencing both high conflict intensity and recurrent droughts show significantly higher rates of childhood stunting. High conflict intensity, drought severity, diarrheal prevalence, and inadequate sanitation significantly increase stunting, while maternal and paternal education, improved sanitation, economic development (proxied by nighttime light intensity), and agricultural productivity reduce it. Among these determinants, female education demonstrated the most pronounced inverse relationship with childhood stunting. Additionally, exposure to both drought severity and high conflict intensity independently and in combination worsens childhood stunting not only within affected regions but also in nearby localities. Our results underscore the urgency of geographically targeted, multisectoral, and action-oriented policies aimed at strengthening community and health system capacities to mitigate the converging risks of climate change and conflict on child malnutrition. Full article

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

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16 pages, 281 KB

Open AccessArticle

Immersive Ocean: A Virtual Twin for Participatory Decision Support in Maritime Spatial Planning

by Xavier Fonseca, Carlos Pereira Santos, Kevin Hutchinson, Jens Hagen, Phil De Groot, Joey Relouw and Igor Mayer

ISPRS Int. J. Geo-Inf. 2026, 15(7), 323; https://doi.org/10.3390/ijgi15070323 - 16 Jul 2026

Abstract

Current Digital Twins of the Ocean rely predominantly on two-dimensional geoportal interfaces that constrain the spatial comprehension of complex marine environments. This paper presents Immersive Ocean, a Virtual Twin platform developed under the EU-ILIAD Digital Twins of the Ocean initiative that procedurally transforms [...] Read more.

Current Digital Twins of the Ocean rely predominantly on two-dimensional geoportal interfaces that constrain the spatial comprehension of complex marine environments. This paper presents Immersive Ocean, a Virtual Twin platform developed under the EU-ILIAD Digital Twins of the Ocean initiative that procedurally transforms standardised European geospatial data (EMODnet, Copernicus Marine Service) into interactive three-dimensional maritime environments via Unreal Engine 5 (Epic Games, Cary, NC, USA). The platform supports both desktop and immersive virtual reality modes, enabling users to visualise and manipulate spatial scenarios involving offshore wind farms, shipping corridors, aquaculture installations, and species distributions. System performance testing confirmed stable rendering across PC and VR configurations (61 FPS and 42 FPS, respectively). A user evaluation with 31 participants across three hardware configurations revealed that core geovisualisation capabilities—ease of use, immersion, and procedural generation utility—remained robust, regardless of hardware, whilst feedback responsiveness (H = 5.99, p = 0.04995) and perceived realism (H = 6.31, p = 0.04258) were significantly affected. These findings inform deployment strategies for immersive geospatial tools: minimum specification systems preserve functional access, whilst recommended hardware enhances the perceptual fidelity critical for spatial decision support. This evaluation establishes usability among digitally proficient users; efficacy with domain stakeholders in authentic planning contexts remains for future work. Full article

21 pages, 5389 KB

Open AccessArticle

The Link Between Urban Resilience and Sustainable Development: Research Trends in Global Nature-Based Solutions Based on Bibliometric Analysis Using CiteSpace and VOSviewer

by Li Zhu, Meihua Song, Lien-Chieh Lee, Wei Zhou, Junjun Liu, Ting Wu and Xudong Yuan

ISPRS Int. J. Geo-Inf. 2026, 15(7), 322; https://doi.org/10.3390/ijgi15070322 - 16 Jul 2026

Abstract

Rapid urbanization and climate change have intensified environmental pressures and social inequalities, making the integration of urban resilience and sustainable development a critical global challenge, with Nature-based Solutions (NbS) emerging as a promising pathway; however, the knowledge structure, collaboration patterns, and evolutionary trends [...] Read more.

Rapid urbanization and climate change have intensified environmental pressures and social inequalities, making the integration of urban resilience and sustainable development a critical global challenge, with Nature-based Solutions (NbS) emerging as a promising pathway; however, the knowledge structure, collaboration patterns, and evolutionary trends of NbS research remain fragmented and insufficiently understood. This study conducts a comprehensive bibliometric analysis of 1261 publications from the Web of Science Core Collection (2005–2025), employing tools including VOSviewer 1.6.20, CiteSpace 6.4.R1, and Bibliometrix 4.1.3 to map publication trends, collaboration networks, knowledge bases, and thematic evolution. The results reveal a rapid expansion of NbS research since 2013, characterized by strong interdisciplinarity and a multicentric yet uneven geographical distribution dominated by China, the United States, and Europe. Four major research clusters are identified, encompassing policy governance, environmental benefits, ecosystem services, and social equity, reflecting a shift from ecological performance to integrated socio-ecological frameworks. Additionally, thematic evolution indicates growing emphasis on governance mechanisms, public health, and environmental justice. Overall, NbS research is transitioning toward a multi-scale, multi-objective, and governance-oriented paradigm. These findings highlight the need for strengthened international collaboration, standardized evaluation frameworks, and inclusive policy design to enhance the effectiveness and global applicability of NbS in advancing urban sustainable development. 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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