ISPRS International Journal of Geo-Information

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

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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25 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

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, 9243 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

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)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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ISPRS International Journal of Geo-Information

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