ISPRS International Journal of Geo-Information

28 pages, 3816 KB

Open AccessArticle

Multi-Size Facility Allocation Under Competition: A Model with Competitive Decay and Reinforcement Learning-Enhanced Genetic Algorithm

by Zixuan Zhao, Shaohua Wang, Cheng Su and Haojian Liang

ISPRS Int. J. Geo-Inf. 2025, 14(9), 347; https://doi.org/10.3390/ijgi14090347 - 9 Sep 2025

Abstract

In modern urban planning, the problem of bank location requires not only considering geographical factors but also integrating competitive elements to optimize resource allocation and enhance market competitiveness. This study addresses the multi-size bank location problem by incorporating competitive factors into the optimization [...] Read more.

In modern urban planning, the problem of bank location requires not only considering geographical factors but also integrating competitive elements to optimize resource allocation and enhance market competitiveness. This study addresses the multi-size bank location problem by incorporating competitive factors into the optimization process through a novel reinforcement learning-enhanced genetic algorithm (RL-GA) framework. Building upon an attraction-based model with competitive decay functions, we propose an innovative hybrid optimization approach that combines evolutionary computation with intelligent decision-making capabilities. The RL-GA framework employs Q-learning principles to adaptively select optimal genetic operators based on real-time population states and search progress, enabling meta-learning where the algorithm learns how to optimize rather than simply optimizing. Unlike traditional genetic algorithms with fixed operator probabilities, our approach dynamically adjusts its search strategy through an

ε

-greedy exploration mechanism and multi-objective reward functions. Experimental results demonstrate that the RL-GA achieves improvements in early-stage convergence speed while maintaining solution quality comparable to traditional methods. The algorithm exhibits enhanced convergence characteristics in the initial optimization phases and demonstrates consistent performance across multiple optimization trials. These findings provide evidence for the potential of intelligence-guided evolutionary computation in facility location optimization, offering moderate computational efficiency gains and adaptive strategic guidance for banking facility deployment in competitive environments. Full article

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35 pages, 30285 KB

Open AccessArticle

Geological Disaster Risk Assessment Under Extreme Precipitation Conditions in the Ili River Basin

by Xinxu Li, Jinghui Liu, Zhiyong Zhang, Xushan Yuan, Yanmin Li and Zixuan Wang

ISPRS Int. J. Geo-Inf. 2025, 14(9), 346; https://doi.org/10.3390/ijgi14090346 - 7 Sep 2025

Viewed by 1032

Abstract

Geological Disasters (Geo-disasters) are common in the Ili River Basin, with extreme precipitation being a major triggering factor. As the frequency and intensity of these events increase, the associated risks also rise. This study proposes a hazard assessment framework that integrates extreme precipitation [...] Read more.

Geological Disasters (Geo-disasters) are common in the Ili River Basin, with extreme precipitation being a major triggering factor. As the frequency and intensity of these events increase, the associated risks also rise. This study proposes a hazard assessment framework that integrates extreme precipitation recurrence periods with Geo-disaster susceptibility. Furthermore, based on a comprehensive risk assessment model encompassing hazard, exposure, vulnerability, and disaster mitigation capacity, the study evaluates Geo-disaster risk in the Ili River Basin under extreme precipitation conditions. Hazard levels are assessed by integrating geo-disaster susceptibility with recurrence periods of extreme precipitation, resulting in hazard and risk maps under various conditions. The susceptibility indicator system is refined using K-means clustering, the certainty factor (CF) model, and Pearson correlation to reduce redundancy. Key findings include: (a) Geo-disasters are influenced by a combination of factors. High-susceptibility areas are typically found in moderately sloped terrain (8.5–17.64°) at elevations between 1412 m and 2234 m, especially on east- and southeast-facing slopes. Lithology, soil, hydrology, fault proximity, and the topographic wetness index (TWI) are the primary influences, while high NDVI values reduce susceptibility. (b) The hazard pattern varies with the recurrence period of extreme precipitation. Shorter periods lead to broader high-hazard zones, while longer periods concentrate hazards, particularly in Yining City. (c) Exposure is higher in the east, vulnerability aligns with transportation networks, and disaster mitigation capacity is stronger in the north, particularly in Yining. (d) Low-risk areas are found in valleys and flat terrains, while medium to high-risk zones concentrate in southeastern Zhaosu, Tekes, and Gongliu counties. Some economically active regions require special attention due to their high exposure and vulnerability. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Monitoring and Management)

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22 pages, 6560 KB

Open AccessArticle

MART: Ship Trajectory Prediction Model Based on Multi-Dimensional Attribute Association of Trajectory Points

by Senyang Zhao, Wei Guo and Yi Liu

ISPRS Int. J. Geo-Inf. 2025, 14(9), 345; https://doi.org/10.3390/ijgi14090345 - 7 Sep 2025

Viewed by 168

Abstract

Ship trajectory prediction plays an important role in numerous maritime applications and services. With the development of deep learning technology, the deep learning prediction method based on Automatic Identification System (AIS) data has become one of the hot topics in current maritime traffic [...] Read more.

Ship trajectory prediction plays an important role in numerous maritime applications and services. With the development of deep learning technology, the deep learning prediction method based on Automatic Identification System (AIS) data has become one of the hot topics in current maritime traffic research. However, as current models always concatenate dynamic information with distinct meanings (such as position, ship speed, and heading) into a single integrated input when processing trajectory point information as input, it becomes difficult for the models to grasp the correlations between different types of dynamic information of trajectory points and the specific information contained in each type of dynamic information itself. Aiming at the problem of insufficient modeling of the relationships among dynamic information in ship trajectory prediction, we propose the Multi-dimensional Attribute Relationship Transformer (MART) model. This model introduces a simulated trajectory training strategy to obtain the Association Loss (AssLoss) for learning the associations among different types of dynamic information; and it uses the Distance Loss (DisLoss) to integrate the relative distance information of the attribute embedding encoding to assist the model in understanding the relationships among different values in the dynamic information. We test the model on two AIS datasets, and the experiments show this model outperforms existing models. In the 15 h long-term prediction task, compared with other models, the MART model improves the prediction accuracy by 9.5% on the Danish Waters Dataset and by 15.4% on the Northern European Dataset. This study reveals the importance of the relationship between attributes and the relative distance of attribute values in spatiotemporal sequence modeling. Full article

(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications)

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17 pages, 6488 KB

Open AccessArticle

A Spatial Analysis of the Association Between Urban Heat and Coronary Heart Disease

by Kyle Lucas, Ben Dewitt, Donald J. Biddle and Charlie H. Zhang

ISPRS Int. J. Geo-Inf. 2025, 14(9), 344; https://doi.org/10.3390/ijgi14090344 - 7 Sep 2025

Viewed by 240

Abstract

Heart disease remains the leading cause of death in both the United States and globally. Urban heat is increasingly recognized as a significant public health challenge, particularly in its connection to cardiovascular conditions. This study, conducted in Jefferson County, Kentucky, examines the distribution [...] Read more.

Heart disease remains the leading cause of death in both the United States and globally. Urban heat is increasingly recognized as a significant public health challenge, particularly in its connection to cardiovascular conditions. This study, conducted in Jefferson County, Kentucky, examines the distribution of coronary heart disease rates and develops an urban heat risk index to examine underlying socioeconomic and environmental factors. We applied bivariate spatial association (Lee’s L), Global Moran’s I, and multiple linear regression methods to examine the relationships between key variables and assess model significance. Global Moran’s I revealed clustered distributions of both coronary heart disease rates and land surface temperature across census tracts. Bivariate spatial analysis identified clusters of high heart disease rates and temperatures within the West End, while clusters of contiguous suburban tracts exhibited lower heart disease rates and temperatures. Regression analyses yielded significant results for both the ordinary least squares (OLS) model and the spatial regression model; however, the spatial error model explained a greater proportion of the variation in coronary heart disease rates across tracts compared to the OLS model. This study offers new insights into spatial disparities in coronary heart disease rates and their associations with environmental risk factors including urban heat, underscoring the challenges faced by many urban communities. Full article

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

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34 pages, 16240 KB

Open AccessArticle

The Toponym Co-Occurrence Index: A New Method to Measure the Co-Occurrence Characteristics of Toponyms

by Gaimei Wang, Fei He and Li Wang

ISPRS Int. J. Geo-Inf. 2025, 14(9), 343; https://doi.org/10.3390/ijgi14090343 - 5 Sep 2025

Viewed by 173

Abstract

Toponym groups are fundamental units of quantitative spatial analysis of toponyms. Using suitable technical methods to investigate the spatial distribution and co-occurrence characteristics of these groups has significant implications for identifying cultural regions within geographical spaces and elucidating spatial differentiation and integration of [...] Read more.

Toponym groups are fundamental units of quantitative spatial analysis of toponyms. Using suitable technical methods to investigate the spatial distribution and co-occurrence characteristics of these groups has significant implications for identifying cultural regions within geographical spaces and elucidating spatial differentiation and integration of regional cultural characteristics underlying toponyms. Existing research has mainly relied on traditional spatial distribution models such as standard deviation ellipse (SDE) and kernel density estimation (KDE) to analyse the characters used in toponyms. In addition, few quantitative studies exist on the co-occurrence of multiple types of toponym groups from the perspective of words used in toponyms. This study introduced methods, including the local co-location quotient, to propose a general framework for toponymic co-occurrence research and a new toponymic co-occurrence index (TCOI). Data from 64,981 village toponyms in Liaoning Province, China, were used to analyse spatial co-occurrence characteristics of five high-frequency two-character village toponym groups. In addition, two high-frequency single-character toponym groups and three low-frequency two-character toponym groups were used for verification, with a simultaneous comparison of the SDE and KDE methods. The findings indicated that: (1) the proposed general framework and TCOI effectively support toponymic spatial measurement and have good applicability and expansibility; (2) the TCOI enables a more accurate scientific assessment of co-occurrence characteristics of toponymic groups at different scales, thereby enhancing the technical level of toponymic spatial measurement; (3) the TCOI for Liaoning Province was 28.63%, indicating that toponym groups exhibited a partially integrated yet relatively exclusive spatial distribution pattern. The spatial differentiation patterns of rural toponym cultural landscapes in Liaoning Province provide a scientific basis for promoting cultural geography research and strengthening toponym protection. Full article

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27 pages, 5718 KB

Open AccessArticle

A Geospatial Framework for Retail Suitability Modelling and Opportunity Identification in Germany

by Cristiana Tudor

ISPRS Int. J. Geo-Inf. 2025, 14(9), 342; https://doi.org/10.3390/ijgi14090342 - 5 Sep 2025

Viewed by 313

Abstract

This study develops an open, reproducible geospatial workflow to identify high-potential retail locations across Germany using a 1 km census grid and OpenStreetMap points of interest. It combines multi-criteria suitability modelling with spatial autocorrelation and Geographically Weighted Regression (GWR). Using fine-scale demographic and [...] Read more.

This study develops an open, reproducible geospatial workflow to identify high-potential retail locations across Germany using a 1 km census grid and OpenStreetMap points of interest. It combines multi-criteria suitability modelling with spatial autocorrelation and Geographically Weighted Regression (GWR). Using fine-scale demographic and retail data, the results show clear regional differences in how drivers operate. Population density is most influential around large metropolitan areas, while the role of points of interest is stronger in smaller regional towns. A separate gap analysis identified forty grid cells with high suitability but no existing retail infrastructure. These locations are spread across both rural and urban contexts, from peri-urban districts in Baden-Württemberg to underserved municipalities in Brandenburg and Bavaria. The pattern is consistent under different model specifications and echoes earlier studies that reported supply deficits in comparable communities. The results are useful in two directions. Retailers can see places with demand that has gone unnoticed, while planners gain evidence that service shortages are not just an urban issue but often show up in smaller towns as well. Taken together, the maps and diagnostics give a grounded picture of where gaps remain, and suggest where investment could bring both commercial returns and community benefits. This study develops an open, reproducible geospatial workflow to identify high-potential retail locations across Germany using a 1 km census grid and OpenStreetMap points of interest. A multi-criteria suitability surface is constructed from demographic and retail indicators and then subjected to spatial diagnostics to separate visually high values from statistically coherent clusters. “White-spots” are defined as cells in the top decile of suitability with zero (strict) or ≤1 (relaxed) existing shops, yielding actionable opportunity candidates. Global autocorrelation confirms strong clustering of suitability, and Local Indicators of Spatial Association isolate hot- and cold-spots robust to neighbourhood size. To explain regional heterogeneity in drivers, Geographically Weighted Regression maps local coefficients for population, age structure, and shop density, revealing pronounced intra-urban contrasts around Hamburg and more muted variation in Berlin. Sensitivity analyses indicate that suitability patterns and priority cells stay consistent with reasonable reweighting of indicators. The comprehensive pipeline comprising suitability mapping, cluster diagnostics, spatially variable coefficients, and gap analysis provides clear, code-centric data for retailers and planners. The findings point to underserved areas in smaller towns and peri-urban districts where investment could both increase access and business feasibility. Full article

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18 pages, 1886 KB

Open AccessArticle

The Integrated Choice and Latent Variable Model for Exploring the Mechanisms of Pedestrian Route Choice

by Cheng-Jie Jin, Ningxuan Li, Chenyang Wu, Dawei Li and Yifan Lin

ISPRS Int. J. Geo-Inf. 2025, 14(9), 341; https://doi.org/10.3390/ijgi14090341 - 5 Sep 2025

Viewed by 246

Abstract

The Integrated Choice and Latent Variable (ICLV) model has been widely applied in travel behavior studies, yet its use in understanding pedestrian route choice remains very limited. This paper seeks to address this gap by analyzing data from a series of controlled pedestrian [...] Read more.

The Integrated Choice and Latent Variable (ICLV) model has been widely applied in travel behavior studies, yet its use in understanding pedestrian route choice remains very limited. This paper seeks to address this gap by analyzing data from a series of controlled pedestrian route choice experiments. Four groups of experimental runs were designed, each involving two route options. The first three groups introduced specific controls: bottlenecks, distance constraints, and extra rewards, while the fourth group, without any imposed control, focused on the influence of route geometry (lengths and widths). For each group, we developed measurement and structural models, followed by three comparative models: a binary logit model using only measured variables (MV model), a model using only latent variables (LV model), and the ICLV model that integrates both. Across all the four scenarios, the adjusted R² values have been improved from 0.286/0.135/0.108/0.035 (MV model) to 0.329/0.161/0.111/0.056 (ICLV model), and the ICLV model can provide interpretable results. These findings highlight the value of incorporating latent constructs based on Structural Equation Modelling (SEM), which enhances the explanatory power of pedestrian route choice models. Moreover, the differences in significant latent variables across various experimental settings offers further insights into the distinct mechanisms underlying pedestrian decision-making under varying conditions. Full article

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27 pages, 5349 KB

Open AccessArticle

Proportional Symbol Maps: Value-Scale Types, Online Value-Scale Generator and User Perspectives

by Radek Barvir, Martin Holub and Alena Vondrakova

ISPRS Int. J. Geo-Inf. 2025, 14(9), 340; https://doi.org/10.3390/ijgi14090340 - 1 Sep 2025

Viewed by 612

Abstract

Proportional symbol maps are a frequently used method of thematic cartography. Using an intuitive principle—the larger, the more—provides a simple and precise way of visualizing quantity in maps using geographic information systems (GIS). However, none of the current GIS software provides a proper [...] Read more.

Proportional symbol maps are a frequently used method of thematic cartography. Using an intuitive principle—the larger, the more—provides a simple and precise way of visualizing quantity in maps using geographic information systems (GIS). However, none of the current GIS software provides a proper map legend that could be used to interpret exact phenomenon quantity values from the map in reverse. Cartographers have been designing value scales manually for such a possibility of interpretation. Eventually, they preferred to resign to the accuracy of the interpretation and use the legend offered by the software. The paper describes the development of an easy-to-use online value scale generator for static maps, aiming to eliminate the time-consuming process to make map design more efficient while preserving the precision of cartographic visualization and its subsequent interpretation. The tool consists of a free web platform performing all necessary calculations and rendering an appropriate value scale based on user-defined input parameters. This functionality is performed for most typically used symbol shapes as well as for custom-design shapes provided by the user in SVG vector graphics. The output is then returned in a vector SVG and PDF file format to be used directly in a map legend or possibly edited in graphic software before such a step. The presented tool is therefore independent of which software was used for map design. Within the research, two user experiments were performed to compare generated value scales with simple legends generated in GIS and to gather insights from cartography experts. Full article

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20 pages, 11319 KB

Open AccessArticle

Using Certainty Factor as a Spatial Sample Filter for Landslide Susceptibility Mapping: The Case of the Upper Jinsha River Region, Southeastern Tibetan Plateau

by Xin Zhou, Ke Jin, Xiaohui Sun, Yunkai Ruan, Yiding Bao, Xiulei Li and Li Tang

ISPRS Int. J. Geo-Inf. 2025, 14(9), 339; https://doi.org/10.3390/ijgi14090339 - 1 Sep 2025

Viewed by 372

Abstract

Landslide susceptibility mapping (LSM) faces persistent challenges in defining representative stable samples as conventional random selection often includes unstable areas, introducing spatial bias and compromising model accuracy. To address this, we redefine the certainty factor (CF) method—traditionally for factor weighting—as a spatial screening [...] Read more.

Landslide susceptibility mapping (LSM) faces persistent challenges in defining representative stable samples as conventional random selection often includes unstable areas, introducing spatial bias and compromising model accuracy. To address this, we redefine the certainty factor (CF) method—traditionally for factor weighting—as a spatial screening tool for stable zone delineation and apply it to the tectonically active upper Jinsha River (937 km², southeastern Tibetan Plateau). Our approach first generates a preliminary susceptibility map via CF, using the natural breaks method to define low- and very low-susceptibility zones (CF < 0.1) as statistically stable regions. Non-landslide samples are exclusively selected from these zones for support vector machine (SVM) modeling with five-fold cross-validation. Key results: CF-guided sampling achieves training/testing AUC of 0.924/0.920, surpassing random sampling (0.882/0.878) by 4.8% and reducing ROC standard deviation by 32%. The final map shows 88.49% of known landslides concentrated in 25.70% of high/very high-susceptibility areas, aligning with geological controls (e.g., 92% of high-susceptibility units in soft lithologies within 500 m of faults). Despite using a simpler SVM, our framework outperforms advanced models (ANN: AUC, 0.890; RF: AUC, 0.870) in the same region, proving physical heuristic sample curation supersedes algorithmic complexity. This transferable framework embeds geological prior knowledge into machine learning, offering high-precision risk zoning for disaster mitigation in data-scarce mountainous regions. Full article

(This article belongs to the Topic Applications of Algorithms in Risk Assessment and Evaluation)

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24 pages, 23275 KB

Open AccessArticle

Developing a Replicable ESG-Based Framework for Assessing Community Perception Using Street View Imagery and POI Data

by Jingxue Xie, Zhewei Liu and Jue Wang

ISPRS Int. J. Geo-Inf. 2025, 14(9), 338; https://doi.org/10.3390/ijgi14090338 - 31 Aug 2025

Viewed by 425

Abstract

Urban livability and sustainability are increasingly studied at the neighborhood scale, where built, social, and governance conditions shape residents’ everyday experiences. Yet existing assessment frameworks often fail to integrate subjective perceptions with multi-dimensional environmental indicators in replicable and scalable ways. To address this [...] Read more.

Urban livability and sustainability are increasingly studied at the neighborhood scale, where built, social, and governance conditions shape residents’ everyday experiences. Yet existing assessment frameworks often fail to integrate subjective perceptions with multi-dimensional environmental indicators in replicable and scalable ways. To address this gap, this study develops an Environmental, Social, and Governance (ESG)-informed framework for evaluating perceived environmental quality in urban communities. Using Baidu Street View imagery—selected due to its comprehensive coverage of Chinese urban areas—and Point of Interest (POI) data, we analyze seven communities in Shenyang, China, selected for their diversity in built form and demographic context. Kernel Density Analysis and Exploratory Factor Analysis (EFA) are applied to derive latent ESG-related spatial dimensions. These are then correlated with Place Pulse 2.0 perception scores using Spearman analysis to assess subjective livability. Results show that environmental and social factors—particularly greenery visibility—are strongly associated with favorable perceptions, while governance-related indicators display weaker or context-specific relationships. The findings highlight the differentiated influence of ESG components, with environmental openness and walkability emerging as key predictors of perceived livability. By integrating pixel-level spatial features with perception metrics, the proposed framework offers a scalable and transferable tool for human-centered neighborhood evaluation, with implications for planning strategies that align with how residents experience urban environments. Full article

(This article belongs to the Special Issue Spatial Information for Improved Living Spaces)

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22 pages, 5263 KB

Open AccessArticle

Educational Facility Site Selection Based on Multi-Source Data and Ensemble Learning: A Case Study of Primary Schools in Tianjin

by Zhenhui Sun, Ying Xu, Junjie Ning, Yufan Wang and Yunxiao Sun

ISPRS Int. J. Geo-Inf. 2025, 14(9), 337; https://doi.org/10.3390/ijgi14090337 - 30 Aug 2025

Viewed by 498

Abstract

To achieve the objective of a “15 min living circle” for educational services, this study develops an integrated method for primary school site selection in Tianjin, China, by combining multi-source data and ensemble learning techniques. At a 500 m grid scale, a suitability [...] Read more.

To achieve the objective of a “15 min living circle” for educational services, this study develops an integrated method for primary school site selection in Tianjin, China, by combining multi-source data and ensemble learning techniques. At a 500 m grid scale, a suitability prediction model was constructed based on the existing distribution of primary schools, utilizing Random Forest (RF) and Extreme Gradient Boosting (XGBoost) models. Comprehensive evaluation, feature importance analysis, and SHAP (SHapley Additive exPlanations) interpretation were conducted to ensure model reliability and interpretability. Spatial overlay analysis, incorporating population structure and the education supply–demand ratio, identified highly suitable areas for primary school construction. The results demonstrate: (1) RF and XGBoost achieved evaluation metrics exceeding 85%, outperforming traditional single models such as Logistic Regression, SVM, KNN, and CART. Validation against actual primary school distributions yielded accuracies of 84.70% and 92.41% for RF and XGBoost, respectively. (2) SHAP analysis identified population density, proximity to other educational institutions, and accessibility to transportation facilities as the most critical factors influencing site suitability. (3) Suitable areas for primary school construction are concentrated in central Tianjin and surrounding areas, including Baoping Street (Baodi District), Huaming Street (Dongli District), and Zhongbei Town (Xiqing District), among others, to meet high-quality educational service demands. Full article

(This article belongs to the Special Issue Spatial Information for Improved Living Spaces)

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16 pages, 1329 KB

Open AccessArticle

Vector Data Rendering Performance Analysis of Open-Source Web Mapping Libraries

by Dániel Balla and Mátyás Gede

ISPRS Int. J. Geo-Inf. 2025, 14(9), 336; https://doi.org/10.3390/ijgi14090336 - 30 Aug 2025

Viewed by 565

Abstract

Nowadays, various technologies exist with differing rendering performance for interactive web maps. These maps are consumed on devices with varying capabilities; therefore, choosing the best-performing library for a dataset is emphasized. Unlike existing research, this study presents a comparative analysis on libraries’ native [...] Read more.

Nowadays, various technologies exist with differing rendering performance for interactive web maps. These maps are consumed on devices with varying capabilities; therefore, choosing the best-performing library for a dataset is emphasized. Unlike existing research, this study presents a comparative analysis on libraries’ native performance for rendering large amounts of GeoJSON vector data, partially extracted from OpenStreetMap (OSM). Four libraries were analyzed. Results showed that regardless of feature types, Leaflet and OpenLayers excelled for features up to 10,000. Up to 5000 points, these two were the fastest, above which the libraries’ performance converged. For 50,000 or more, Mapbox GL JS rendered them the quickest, followed by OpenLayers, MapLibre GL JS and Leaflet. For up to 50,000 lines and 10,000 polygons, Leaflet and OpenLayers were the fastest in all scenarios. For 100,000 lines, OpenLayers was almost twice as fast as the others, while Mapbox rendered 50,000 polygons the quickest. The performance of Leaflet and OpenLayers scales with the increasing feature quantities, yet for Mapbox and MapLibre, any performance impact is offset to 1000 features and beyond. Slow initalization of map elements makes Mapbox and MapLibre less suitable for rapid rendering of small feature quantities. Other behavioural differences affecting user experience are also explored. Full article

(This article belongs to the Special Issue Cartography and Geovisual Analytics)

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16 pages, 6515 KB

Open AccessArticle

Application of 3D Ray Tracing for Water Surface Visibility Analysis

by Rafał Wróżyński, Magdalena Wróżyńska and Krzysztof Pyszny

ISPRS Int. J. Geo-Inf. 2025, 14(9), 335; https://doi.org/10.3390/ijgi14090335 - 30 Aug 2025

Viewed by 486

Abstract

Visibility of the sea plays a significant role in shaping spatial perception, property value, and planning decisions in coastal areas. While traditional GIS-based viewshed analysis provides useful tools for modeling visibility, it remains limited by its 2.5D nature and simplified representations of terrain [...] Read more.

Visibility of the sea plays a significant role in shaping spatial perception, property value, and planning decisions in coastal areas. While traditional GIS-based viewshed analysis provides useful tools for modeling visibility, it remains limited by its 2.5D nature and simplified representations of terrain and vegetation. This study presents a 3D ray-tracing-based method for analyzing water surface visibility using high-resolution LIDAR data and physically based rendering techniques within a fully 3D environment. The methodology allows for realistic modeling of visibility from a human perspective, accounting for complex occlusions caused by buildings, terrain, and vegetation. Unlike conventional GIS tools, the proposed approach identifies visible areas beneath tree canopies and enables vertical exploration of visibility from different elevations and building floors. The method was applied in a case study of the coastal city of Świnoujście, Poland. The resulting viewshed was validated through photographic field verification from observer height (1.7 m), confirming the accuracy of visibility predictions. This research demonstrates the potential of ray-tracing methods in landscape and urban visibility analysis, offering a flexible and perceptually accurate alternative to traditional GIS-based approaches. Future work will focus on quantifying the visible extent of the water surface to support more detailed assessments of visual exposure in planning and conservation context. Full article

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16 pages, 2224 KB

Open AccessArticle

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

by Abhilasha Kashyap and Seong-Yun Hong

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

Viewed by 648

Abstract

This study examines how hotel characteristics and urban spatial context influence tourist satisfaction in Seoul, South Korea, by integrating sentiment analysis of online reviews with regression modeling. Drawing on 4500 TripAdvisor reviews from 75 hotels, sentiment scores were extracted using aspect-based sentiment analysis, [...] Read more.

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

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

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41 pages, 3940 KB

Open AccessArticle

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

by Haolong Yang, Chen Feng and Chao Gao

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

Viewed by 630

Abstract

As bike-sharing systems become increasingly integral to sustainable urban mobility, understanding their economic viability requires moving beyond conventional linear models to capture complex operational dynamics. This study develops an interpretable analytical framework to uncover non-linear relationships governing bike-sharing economic performance in Xi’an, China, [...] Read more.

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

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

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16 pages, 2015 KB

Open AccessArticle

LTVPGA: Distilled Graph Attention for Lightweight Traffic Violation Prediction

by Yingzhi Wang, Yuquan Zhou and Feng Zhang

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

Viewed by 391

Abstract

Traffic violations, the primary cause of road accidents, threaten public safety by disrupting traffic flow and causing substantial casualties and economic losses. Accurate spatiotemporal prediction of violations offers critical insights for proactive traffic management. While Graph Attention Network (GAT) methods excel in spatiotemporal [...] Read more.

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

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

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23 pages, 13164 KB

Open AccessArticle

A Spatial Co-Location Pattern Mining Method Based on Hausdorff Distance Alignment

by Xichen Liu, Yajie Li and Muquan Zou

ISPRS Int. J. Geo-Inf. 2025, 14(9), 331; https://doi.org/10.3390/ijgi14090331 - 26 Aug 2025

Viewed by 575

Abstract

Spatial co-location patterns are used to describe the spatial associations between features, finding wide applications in geographic information systems, urban planning, and other fields. Traditional frameworks for mining spatial features typically consist of two stages: constructing spatial proximity relationships and discovering frequent patterns. [...] Read more.

Spatial co-location patterns are used to describe the spatial associations between features, finding wide applications in geographic information systems, urban planning, and other fields. Traditional frameworks for mining spatial features typically consist of two stages: constructing spatial proximity relationships and discovering frequent patterns. However, existing methods have limitations: the construction of proximity relationships relies on fixed distance thresholds or clustering centers, making it difficult to adapt to spatial density heterogeneity; meanwhile, frequency metrics overly depend on participation indices, lacking quantitative analysis of the strength of geometric associations between features. To address these issues, a spatial co-location pattern mining method based on Hausdorff distance is proposed. Drawing on the concept of Hausdorff distance, this method employs Voronoi tessellation to achieve data-adaptive partitioning of the spatial domain. Combined with a K-dimensional tree, it adopts an iterative strategy of direct allocation, proportional allocation, and residual allocation to align instances, generating a spatial proximity relationship graph. Additionally, a new frequency metric based on instance distribution—alignment rate—is introduced, leveraging the decreasing trend of alignment rate in conjunction with a pruning optimization algorithm. Experimental results demonstrate that this method excels in handling noise points, effectively addressing the challenges of uneven data density distribution while enhancing the identification of weakly associated yet potentially valuable patterns. Full article

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26 pages, 57687 KB

Open AccessArticle

Assessing the Available Landslide Susceptibility Map and Inventory for the Municipality of Rio de Janeiro, Brazil: Potentials and Challenges for Data-Driven Applications

by Pedro Henrique Muniz Lima, Luiz Carlos Teixeira Coelho, Guilherme Damasceno Raposo, Irving da Silva Badolato, Raquel Batista Medeiros da Fonseca, Sonia Maria Lima Silva and Jonatas Goulart Marinho Falcão

ISPRS Int. J. Geo-Inf. 2025, 14(9), 330; https://doi.org/10.3390/ijgi14090330 - 26 Aug 2025

Viewed by 1069

Abstract

This study presents an initial evaluation of the heuristic landslide susceptibility map for the Municipality of Rio de Janeiro by comparing it with the official landslide inventory. The objective is to provide a first analysis of the accuracy of the current map (Reference [...] Read more.

This study presents an initial evaluation of the heuristic landslide susceptibility map for the Municipality of Rio de Janeiro by comparing it with the official landslide inventory. The objective is to provide a first analysis of the accuracy of the current map (Reference Map), which was developed using heuristic methods, in contrast with a basic predictive model based on Generalized Additive Models (GAMs). The study includes a critical review of the existing inventory and examines landslide records from 2010 to 2016, using georeferenced data provided by the GeoRio Foundation. Data from 2017 and 2018 are used for a preliminary test of the model. Rather than proposing a replacement, this study suggests that even simple data-driven models can offer useful insights into potential improvements in the reference susceptibility map. The results are exploratory and intended to inform future, more detailed analyses. While limited in scope, this work illustrates how quantitative approaches may complement existing methods in landslide prediction assessment. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and GIS for Natural Hazards Monitoring and Management)

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22 pages, 11655 KB

Open AccessArticle

An Analysis of the Spatiotemporal Evolution, Key Control Features, and Driving Mechanisms of Carbon Source/Sink in the Continental Ecosystem of China’s Shandong Province from 2001 to 2020

by Xiaolong Xu, Fang Han, Junxin Zhao, Youheng Li, Ziqiang Lei, Shan Zhang and Hui Han

ISPRS Int. J. Geo-Inf. 2025, 14(9), 329; https://doi.org/10.3390/ijgi14090329 - 26 Aug 2025

Viewed by 482

Abstract

Continental ecosystems are crucial constituents of the worldwide carbon process, and their carbon source and sink processes are highly sensitive to human-induced climate change. However, the spatiotemporal changes and principal determinants of carbon source/sink in Shandong Province remain unclear. This study constructs six [...] Read more.

Continental ecosystems are crucial constituents of the worldwide carbon process, and their carbon source and sink processes are highly sensitive to human-induced climate change. However, the spatiotemporal changes and principal determinants of carbon source/sink in Shandong Province remain unclear. This study constructs six dominant control modes of carbon sources/sinks based on three carbon sink indicators (gross primary production (GPP), net primary production (NPP), and net ecosystem productivity (NEP)) and three carbon source indicators (autotrophic respiration (Ra), heterotrophic respiration (Rh), and total ecosystem respiration (Rs)), revealing the main control characteristics of the spatiotemporal dynamics of carbon source/sink in the continental ecosystems of Shandong Province. Additionally, the principal determinants of carbon sources and sinks are quantitatively analyzed using cloud models. The research findings are as follows: (1) From 2001 to 2020, the continental ecosystem of Shandong Province demonstrated a weak carbon sink overall, with both carbon sinks and sources showing fluctuating growth trends (growth rate: GPP, NEP, NPP, Rs, Ra, and Rh consist of 15.55, 6.14, 6.09, 9.59, 9.47, and 0.07 gCm⁻²a⁻¹). (2) The dominant control characteristics of carbon source/sink in Shandong Province exhibit significant spatial differentiation, which can be classified into absolute carbon sink cities (Jinan, Zibo, Rizhao, Jining, Liaocheng, Zaozhuang, Binzhou, Dezhou, Tai’an) and relative carbon source cities (Weifang, Yantai, Weihai, Linyi, Qingdao, Heze, and Dongying). GPP is the dominant control factor in carbon sink areas and is widely distributed across the province, while Rs and GPP are the dominant control factors in carbon source fields, focused on the eastern coastal and southwestern inland sites. (3) Landscape modification and rainfall are the main driving elements influencing the carbon sink and source variations in Shandong Province’s continental ecosystems. (4) The spatial differentiation of the driving factors of carbon producers and reservoirs is significant. In absolute carbon sink cities, land-use change and vegetation cover are the dominant factors for carbon sinks and sources, with significant changes in both range and spatial differentiation. In relative carbon source cities, land-use change is the leading factor for carbon source/sink, and the range of changes and spatial differentiation is most notable. The observations from this study supply scientific underpinnings and reference for enhancing carbon sequestration in continental ecosystems, urban ecological safety management, and achieving carbon neutrality goals. Full article

(This article belongs to the Topic Dynamic Monitoring and Estimation of Coastal Wetland Blue Carbon Ecosystems)

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22 pages, 7818 KB

Open AccessArticle

Representation of 3D Land Cover Data in Semantic City Models

by Per-Ola Olsson, Axel Andersson, Matthew Calvert, Axel Loreman, Erik Lökholm, Emma Martinsson, Karolina Pantazatou, Björn Svensson, Alex Spielhaupter, Maria Uggla and Lars Harrie

ISPRS Int. J. Geo-Inf. 2025, 14(9), 328; https://doi.org/10.3390/ijgi14090328 - 26 Aug 2025

Viewed by 777

Abstract

A large number of cities have created semantic 3D city models, but these models are rarely used as input data for simulations, such as noise and flooding, in the urban planning process. Reasons for this are that many simulations require detailed land cover [...] Read more.

A large number of cities have created semantic 3D city models, but these models are rarely used as input data for simulations, such as noise and flooding, in the urban planning process. Reasons for this are that many simulations require detailed land cover (LC) and elevation data that are often not included in the 3D city models, and that there is no linkage between the elevation and land cover data. In this study, we design, implement and evaluate methods to handle LC and elevation data in a 3D city model. The LC data is stored in 2.5D or 3D in the CityGML modules Transportation, Vegetation, WaterBody, CityFurniture and LandUse, and a complete 3D LC partition is created by combining data from these modules. The entire workflow is demonstrated in the paper: creating 2D LC data, extending CityGML, creating 2.5D/3D data from the 2D LC data, dividing the LC data into CityGML modules, storing it in a database (3DCityDB) and finally visualizing the data in Unreal Engine. The study is part of the 3CIM project where a national profile of CityGML for Sweden is created as an Application Domain Extension (ADE), but the result is generally applicable for CityGML implementations. Full article

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25 pages, 5228 KB

Open AccessArticle

Digital Relations in

Z^{1}

: Discretized Time and Rasterized Lines

by Matthew P. Dube

ISPRS Int. J. Geo-Inf. 2025, 14(9), 327; https://doi.org/10.3390/ijgi14090327 - 25 Aug 2025

Viewed by 467

Abstract

There is voluminous literature concerning the scope of topological relations that span various embedding spaces from

R^{1}

to

R^{2}

,

Z^{2}

,

S^{1}

and

S^{2}

, and

T^{2}

. In the case of the

*^{1}

spaces, [...] Read more.

There is voluminous literature concerning the scope of topological relations that span various embedding spaces from

R^{1}

to

R^{2}

,

Z^{2}

,

S^{1}

and

S^{2}

, and

T^{2}

. In the case of the

*^{1}

spaces, those relations have been considered as conceptualizations of both spatial relations and temporal relations. Missing from that list are the set of digital relations that exist within

Z^{1}

, representing discretized time, discretized ordered line segments, or discretized linear features as embedding spaces. Discretized time plays an essential role in timeseries data, spatio-temporal information systems, and geo-foundation models where time is represented in layers of consecutive spatial rasters and/or spatial vector objects colloquially referred to as space–time cubes or spatio-temporal stacks. This paper explores the digital relations that exist in

Z^{1}

interpreted as a regular topological space under the digital Jordan curve model as well as a folded-over temporal interpretation of that space for use in spatio-temporal information systems and geo-foundation models. The digital Jordan curve model represents the maximum expressive power between discretized objects, making it the ideal paradigm for a decision support system model. It identifies 34 9-intersection relations in

Z^{1}

, 42 9-intersection + margin relations in

Z^{1}

, and 74 temporal relations in

Z^{1}

, utilizing the 9+-intersection, the commercial standard for spatial information systems for querying topological relations. This work creates opportunities for better spatio-temporal reasoning capacity within spatio-temporal stacks and a more direct interface with intuitive language concepts, instrumental for effective utilization of spatial tools. Three use cases are demonstrated in the discussion, representing each of the utilities of

Z^{1}

within the spatial data science community. Full article

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27 pages, 20171 KB

Open AccessArticle

An Approach to Selecting an E-Commerce Warehouse Location Based on Suitability Maps: The Case of Samara Region

by Sergey Sakulin, Alexander Alfimtsev and Nikita Gavrilov

ISPRS Int. J. Geo-Inf. 2025, 14(9), 326; https://doi.org/10.3390/ijgi14090326 - 24 Aug 2025

Viewed by 632

Abstract

In the context of the rapid development of e-commerce, the selection of optimal land plots for the construction of warehouse complexes that meet environmental, technical, and political requirements has become increasingly relevant. This task requires a comprehensive approach that accounts for a wide [...] Read more.

In the context of the rapid development of e-commerce, the selection of optimal land plots for the construction of warehouse complexes that meet environmental, technical, and political requirements has become increasingly relevant. This task requires a comprehensive approach that accounts for a wide range of factors, including transportation accessibility, environmental conditions, geographic features, legal constraints, and more. Such an approach enhances the efficiency and sustainability of decision-making processes. This article presents a solution to the aforementioned problem that employs the use of land suitability maps generated by aggregating multiple evaluation criteria. These criteria represent the degree to which each land plot satisfies the requirements of various stakeholders and are expressed as suitability functions based on attribute values. Attributes describe different characteristics of the land plots and are represented as layers on a digital terrain map. The criteria and their corresponding attributes are classified as either quantitative or binary. Binary criteria are aggregated using the minimum operator, which filters out plots that violate any constraints by assigning them a suitability score of zero. Quantitative criteria are aggregated using the second-order Choquet integral, a method that accounts for interdependencies among criteria while maintaining computational simplicity. The criteria were developed based on statistical and environmental data obtained from an analysis of the Samara region in Russia. The resulting suitability maps are visualized as gradient maps, where land plots are categorized according to their degree of suitability—from completely unsuitable to highly suitable. This visual representation facilitates intuitive interpretation and comparison of different location options. These maps serve as an effective tool for planners and stakeholders, providing comprehensive and objective insights into the potential of land plots while incorporating all relevant factors. The proposed approach supports spatial analysis and land use planning by integrating mathematical modeling with modern information technologies to address pressing challenges in sustainable development. Full article

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27 pages, 8196 KB

Open AccessArticle

Enhancing Electric Vehicle Charging Infrastructure Planning with Pre-Trained Language Models and Spatial Analysis: Insights from Beijing User Reviews

by Yanxin Hou, Peipei Wang, Zhuozhuang Yao, Xinqi Zheng and Ziying Chen

ISPRS Int. J. Geo-Inf. 2025, 14(9), 325; https://doi.org/10.3390/ijgi14090325 - 24 Aug 2025

Viewed by 524

Abstract

With the growing adoption of electric vehicles, optimizing the user experience of charging infrastructure has become critical. However, extracting actionable insights from the vast number of user reviews remains a significant challenge, impeding demand-driven operational planning for charging stations and degrading the user [...] Read more.

With the growing adoption of electric vehicles, optimizing the user experience of charging infrastructure has become critical. However, extracting actionable insights from the vast number of user reviews remains a significant challenge, impeding demand-driven operational planning for charging stations and degrading the user experience. This study leverages three pre-trained language models to perform sentiment classification and multi-level topic identification on 168,129 user reviews from Beijing, facilitating a comprehensive understanding of user feedback. The experimental results reveal significant task-model specialization: RoBERTa-WWM excels in sentiment analysis (accuracy = 0.917) and fine-grained topic identification (Micro-F1 = 0.844), making it ideal for deep semantic extraction. Conversely, ELECTRA, after sufficient training, demonstrates a strong aptitude for coarse-grained topic summarization, highlighting its strength in high-level semantic generalization. Notably, the models offer capabilities beyond simple classification, including autonomous label normalization and the extraction of valuable information from comments with low information density. Furthermore, integrating textual and spatial analyses revealed striking patterns. We identified an urban–rural emotional gap—suburban users are more satisfied despite fewer facilities—and used geographically weighted regression (GWR) to quantify the spatial differences in the factors affecting user satisfaction in Beijing’s districts. We identified three types of areas requiring differentiated strategies, as follows: the northwestern region is highly sensitive to equipment quality, the central urban area has a complex relationship between supporting facilities and satisfaction, and the emerging adoption area is more sensitive to accessibility and price factors. These findings offer a data-driven framework for charging infrastructure planning, enabling operators to base decisions on real-world user feedback and tailor solutions to specific local contexts. Full article

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33 pages, 4109 KB

Open AccessArticle

National Spatial Data Infrastructure as a Catalyst for Good Governance and Policy Improvements in Pakistan

by Munir Ahmad, Asmat Ali, Muhammad Nawaz, Farha Sattar and Hammad Hussain

ISPRS Int. J. Geo-Inf. 2025, 14(9), 324; https://doi.org/10.3390/ijgi14090324 - 24 Aug 2025

Viewed by 802

Abstract

This study explores the potential of National Spatial Data Infrastructure (NSDI) to strengthen governance and policy processes in Pakistan. Drawing on the UNESCAP principles of good governance and the EGU policy cycle model, this research applies a dual-method approach combining thematic document analysis [...] Read more.

This study explores the potential of National Spatial Data Infrastructure (NSDI) to strengthen governance and policy processes in Pakistan. Drawing on the UNESCAP principles of good governance and the EGU policy cycle model, this research applies a dual-method approach combining thematic document analysis of 23 national policy frameworks and a stakeholder survey (n = 28). The results reveal that while many policies reference spatial data conceptually, critical components such as standardised datasets, spatial dashboards, and institutional coordination mechanisms remain underdeveloped. Spatial references are largely confined to early policy stages, with limited integration in evaluation and maintenance, thereby limiting adaptive governance. Conversely, survey findings reflect strong recognition of NSDI’s value across governance principles, policy integration, and spatial awareness dimensions. The composite endorsement score highlights institutional demand for geospatial tools, data standards, and capacity-building platforms. The study concludes that embedding NSDI within policy and planning systems can bridge critical governance gaps, enhance implementation fidelity, and support inter-agency coordination for long-term policy effectiveness. Full article

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16 pages, 2578 KB

Open AccessArticle

Determination of the Solar Angle of Incidence Using an Equivalent Surface and the Possibility of Applying This Approach in Geosciences and Engineering

by Marián Jenčo

ISPRS Int. J. Geo-Inf. 2025, 14(9), 323; https://doi.org/10.3390/ijgi14090323 - 23 Aug 2025

Viewed by 606

Abstract

The solar angle of incidence is the angle between the sunlight and the normal on the impact surface. The lower the angle of incidence, the more sun radiation the surface can absorb. There are several methods for calculating of this angle. Determining the [...] Read more.

The solar angle of incidence is the angle between the sunlight and the normal on the impact surface. The lower the angle of incidence, the more sun radiation the surface can absorb. There are several methods for calculating of this angle. Determining the geographical location of the equivalent surface is one of the lesser-known options. The equivalent surface is a tangential plane to the Earth that is parallel to a reference inclined surface. The geographical coordinates of the point of tangency are clearly determined by the slope and aspect. Since the equivalent surface is horizontal, basic solar geometry equations apply. Unlike the conventional equations commonly used today, they provide easily interpretable results. The sunrise and sunset times for an inclined surface and the time of an extreme incidence angle can be calculated directly. Approximate calculations are not necessary. In addition, the geographical approach allows for the hour angle to be determined, as well as the tilt for a given azimuth of the solar panel that is perpendicular to direct sunlight. This new procedure sets the time for regular changes in the horizontal direction of the sun-tracker. The renaissance of the geographical approach for calculating the temporal characteristics, which allows for the use of simple equations and the interpretation of their results, can also benefit agriculture, forestry, land management, botany, architecture, and other sectors and sciences. Full article

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20 pages, 4720 KB

Open AccessArticle

Dynamic Optimization of Emergency Infrastructure Layouts Based on Population Influx: A Macao Case Study

by Zhen Wang, Zheyu Wang, On Kei Yeung, Mengmeng Zheng, Yitao Zhong and Sanqing He

ISPRS Int. J. Geo-Inf. 2025, 14(9), 322; https://doi.org/10.3390/ijgi14090322 - 23 Aug 2025

Viewed by 521

Abstract

This study investigates the spatiotemporal optimization of small-scale emergency infrastructure in high-density urban environments, using nucleic acid testing sites in Macao as a case study. The objective is to enhance emergency responsiveness during future public health crises by aligning infrastructure deployment with dynamic [...] Read more.

This study investigates the spatiotemporal optimization of small-scale emergency infrastructure in high-density urban environments, using nucleic acid testing sites in Macao as a case study. The objective is to enhance emergency responsiveness during future public health crises by aligning infrastructure deployment with dynamic patterns of population influx. A behaviorally informed spatial decision-making framework is developed through the integration of kernel density estimation, point-of-interest (POI) distribution, and origin–destination (OD) path simulation based on an Ant Colony Optimization (ACO) algorithm. The results reveal pronounced temporal fluctuations in testing demand—most notably with crowd peaks occurring around 12:00 and 18:00—and highlight spatial mismatches between existing facility locations and key residential or functional clusters. The proposed approach illustrates the feasibility of coupling infrastructure layout with real-time mobility behavior and offers transferable insights for emergency planning in compact urban settings. Full article

(This article belongs to the Topic The Use of Big Data in Public Health Research and Practice)

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21 pages, 2655 KB

Open AccessArticle

A Hybrid Approach for Geo-Referencing Tweets: Transformer Language Model Regression and Gazetteer Disambiguation

by Thomas Edwards, Padraig Corcoran and Christopher B. Jones

ISPRS Int. J. Geo-Inf. 2025, 14(9), 321; https://doi.org/10.3390/ijgi14090321 - 22 Aug 2025

Viewed by 579

Abstract

Recent approaches to geo-referencing X posts have focused on the use of language modelling techniques that learn geographic region-specific language and use this to infer geographic coordinates from text. These approaches rely on large amounts of labelled data to build accurate predictive models. [...] Read more.

Recent approaches to geo-referencing X posts have focused on the use of language modelling techniques that learn geographic region-specific language and use this to infer geographic coordinates from text. These approaches rely on large amounts of labelled data to build accurate predictive models. However, obtaining significant volumes of geo-referenced data from Twitter, recently renamed X, can be difficult. Further, existing language modelling approaches can require the division of a given area into a grid or set of clusters, which can be dataset-specific and challenging for location prediction at a fine-grained level. Regression-based approaches in combination with deep learning address some of these challenges as they can assign coordinates directly without the need for clustering or grid-based methods. However, such approaches have received only limited attention for the geo-referencing task. In this paper, we adapt state-of-the-art neural network models for the regression task, focusing on geo-referencing wildlife Tweets where there is a limited amount of data. We experiment with different transfer learning techniques for improving the performance of the regression models, and we also compare our approach to recently developed Large Language Models and prompting techniques. We show that using a location names extraction method in combination with regression-based disambiguation, and purely regression when names are absent, leads to significant improvements in locational accuracy over using only regression. Full article

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21 pages, 6814 KB

Open AccessArticle

Urban Land Subsidence Analyzed Through Time-Series InSAR Coupled with Refined Risk Modeling: A Wuhan Case Study

by Lv Zhou, Liqi Liang, Quanyu Chen, Haotian He, Hongming Li, Jie Qin, Fei Yang, Xinyi Li and Jie Bai

ISPRS Int. J. Geo-Inf. 2025, 14(9), 320; https://doi.org/10.3390/ijgi14090320 - 22 Aug 2025

Viewed by 590

Abstract

Due to extensive soft soil and high human activities, Wuhan is a hotspot for land subsidence. This study used the time-series InSAR to calculate the spatial and temporal distribution map of subsidence in Wuhan and analyze the causes of subsidence. An improved fuzzy [...] Read more.

Due to extensive soft soil and high human activities, Wuhan is a hotspot for land subsidence. This study used the time-series InSAR to calculate the spatial and temporal distribution map of subsidence in Wuhan and analyze the causes of subsidence. An improved fuzzy analytic hierarchy process (GD-FAHP) was proposed and integrated with the Entropy Weight Method (EWM) to assess the hazard and vulnerability of land subsidence using multiple evaluation factors, thereby deriving the spatial distribution characteristics of subsidence risk in Wuhan. Results indicated the following: (1) Maximum subsidence rates reached −49 mm/a, with the most severe deformation localized in Hongshan District, exhibiting a cumulative displacement of −135 mm. Comparative validation between InSAR results and leveling was conducted, demonstrating the reliability of InSAR monitoring. (2) Areas with frequent urban construction largely coincided with subsidence locations. In addition, the analysis indicated that rainfall and hydrogeological conditions were also correlated with land subsidence. (3) The proposed risk assessment model effectively identified high-risk areas concentrated in central urban zones, particularly the Hongshan and Wuchang Districts. This research establishes a methodological framework for urban hazard mitigation and provides actionable insights for subsidence risk reduction strategies. Full article

(This article belongs to the Topic Geotechnics for Hazard Mitigation, 2nd Edition)

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ISPRS Int. J. Geo-Inf., Volume 14, Issue 9 (September 2025) – 28 articles

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