ISPRS International Journal of Geo-Information

27 pages, 1096 KB

Open AccessArticle

Automated Identification and Spatial Pattern Analysis of Urban Slow-Moving Traffic Bottlenecks Using Street View Imagery and Deep Learning

by Zixuan Guo, Hong Xu and Qiushuang Lin

ISPRS Int. J. Geo-Inf. 2025, 14(9), 351; https://doi.org/10.3390/ijgi14090351 - 15 Sep 2025

Abstract

With rapid urbanization and increasing emphasis on sustainable mobility, slow-moving traffic systems, including pedestrian and cycling infrastructure, have become critical to urban transportation and quality of life. Conventional assessment methods are labor-intensive, time-consuming, and limited in coverage. Leveraging advances in deep learning and [...] Read more.

With rapid urbanization and increasing emphasis on sustainable mobility, slow-moving traffic systems, including pedestrian and cycling infrastructure, have become critical to urban transportation and quality of life. Conventional assessment methods are labor-intensive, time-consuming, and limited in coverage. Leveraging advances in deep learning and computer vision, this study develops a framework for bottleneck detection using street-level imagery and the You Only Look Once version 5 (YOLOv5) model. An evaluation system comprising 15 indicators across continuity, safety, and comfort is established. In a case study of Wuhan’s Third Ring Road, the YOLOv5 model achieved 98.9% mean Average Precision (mAP)@0.5, while spatial hotspot analysis (p < 0.05) identified severe demand–infrastructure mismatches in southeastern Wuhan, contrasted with fewer problems in the northern region due to stronger management. To ensure adaptability, a dynamic optimization mechanism integrating temporal imagery updates, transfer learning, and collaborative training is proposed. The findings demonstrate the effectiveness of street-level remote sensing for large-scale urban diagnostics, extend the application of deep learning in mobility research, and provide practical insights for data-driven planning and governance of slow-moving traffic systems in high-density cities. Full article

38 pages, 15532 KB

Open AccessArticle

Lightweight Deep Learning Approaches for Lithological Mapping in Vegetated Terrains of the Vălioara Valley, Romania

by Valentin Árvai and Gáspár Albert

ISPRS Int. J. Geo-Inf. 2025, 14(9), 350; https://doi.org/10.3390/ijgi14090350 - 15 Sep 2025

Abstract

Mapping lithology in areas with dense vegetation remains a major challenge for remote sensing, as plant cover tends to obscure the spectral signatures of underlying rock formations. This study tackles that issue by comparing the performance of three custom-built lightweight deep learning models [...] Read more.

Mapping lithology in areas with dense vegetation remains a major challenge for remote sensing, as plant cover tends to obscure the spectral signatures of underlying rock formations. This study tackles that issue by comparing the performance of three custom-built lightweight deep learning models in the mixed-vegetation terrain of the surroundings of the Vălioara Valley, Romania. We used time-series data from Sentinel-2 and elevation data from the SRTM, with preprocessing techniques such as the Principal Component Analysis (PCA) and the Forced Invariance Method (FIM) to reduce the spectral interference caused by vegetation. Predictions were made with a Multi-Layer Perceptron (MLP), a Convolutional Neural Network (CNN), and a Vision Transformer (ViT). In addition to measuring the classification accuracy, we assessed how the different models handled vegetation coverage. We also explored how vegetation density (NDVI) correlated with the classification results. Tests show that the Vision Transformer outperforms the other models by 6%, offering a stronger resilience to vegetation interference, while FIM doubled the model confidence in specific (locally rare) lithologies and decorrelated vegetation in multiple measures. These findings highlight both the potential of ViTs for remote sensing in complex environments and the importance of applying vegetation suppression techniques like FIM to improve geological interpretation from satellite data. 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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25 pages, 4316 KB

Open AccessArticle

Distribution, Dynamics and Drivers of Asian Active Fire Occurrences

by Xu Gao, Wenzhong Shi and Min Zhang

ISPRS Int. J. Geo-Inf. 2025, 14(9), 349; https://doi.org/10.3390/ijgi14090349 - 12 Sep 2025

Abstract

As the world’s most populous and geographically diverse continent, active fire occurrence in Asia exhibits pronounced spatiotemporal heterogeneity, driven by climactic and anthropogenic factors. However, systematic analyses of Asian fire occurrence characteristics are still scarce, the quantitative and spatial relationship between fire dynamics [...] Read more.

As the world’s most populous and geographically diverse continent, active fire occurrence in Asia exhibits pronounced spatiotemporal heterogeneity, driven by climactic and anthropogenic factors. However, systematic analyses of Asian fire occurrence characteristics are still scarce, the quantitative and spatial relationship between fire dynamics and drivers remain poorly understood. Here, utilizing active fire and land cover products alongside climate and human footprint datasets, we explored the spatiotemporal distribution and dynamics of active fire counts (FC) over 20 years (2003–2022) in Asia, quantifying the effects of climate and human management. Results analyzed over 10 million active fires, with cropland fires predominating (25.6%) and Southeast Asia identified as the hotspot. FC seasonal dynamics were governed by temperature and precipitation, while spring was the primary burning season. A continental inter-annual FC decline (mean slope: −8716 yr⁻¹) was identified, primarily attributed to forest fire reduction. Subsequently, we further clarified the drivers of FC dynamics. Time series decomposition attributed short-term FC fluctuations to extreme climate events (e.g., 2015 El Niño), while long-term trends reflected cumulative human interventions (e.g., cropland management). The trend analysis revealed that woody vegetation fires in the Indochina Peninsula shifted to herbaceous fires, Asian cropland FC primarily increased but were restricted in eastern China and Thailand by strict policies. Spatially, hydrometeorological factors dominated 58.1% of FC variations but exhibited opposite effects between arid and humid regions, followed by human factor, where human activities shifted from fire promotion to suppression through land-use transitions. These driving mechanism insights establish a new framework for adaptive fire management amid escalating environmental change. 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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21 pages, 5662 KB

Open AccessArticle

Study on Spatial Equity of Greening in Historical and Cultural Cities Based on Multi-Source Spatial Data

by Huiqi Sun, Xuemin Shi, Bichao Hou and Huijun Yang

ISPRS Int. J. Geo-Inf. 2025, 14(9), 348; https://doi.org/10.3390/ijgi14090348 - 12 Sep 2025

Abstract

Urban green space, a vital part of urban ecosystems, offers inhabitants essential ecosystem services, and ensuring its fair distribution is essential to preserving their ecological well-being. This study uses Kaifeng City in Henan Province as the research object and aims to address the [...] Read more.

Urban green space, a vital part of urban ecosystems, offers inhabitants essential ecosystem services, and ensuring its fair distribution is essential to preserving their ecological well-being. This study uses Kaifeng City in Henan Province as the research object and aims to address the unique conflict between the preservation of well-known historical and cultural cities and the development of greening. It does this by integrating streetscape big data (2925 sampling points) and point of interest (POI) density data (57,266 records) and using the DeepLab-ResNeSt269 semantic segmentation model in conjunction with spatial statistical techniques (Moran’s Index, Locational Entropy and Theil Index Decomposition) to quantitatively analyze the spatial equity of the green view index (GVI) in Kaifeng City. The results of the study show that (1) The Theil Index reveals that the primary contradiction in Kaifeng City’s distribution pattern—low GVI in the center and high in the periphery—is the micro-street scale difference, suggesting that the spatial imbalance of the GVI is primarily reflected at the micro level rather than the macro urban area difference. (2) The distribution of the GVI in Kaifeng City exhibits a significant spatial polarization phenomenon, with the proportion of low-value area (35.40%) being significantly higher than that of high-value area (25.10%) and the spatial clustering being evident (Moran’s Index 0.3824). Additionally, the ancient city area and the new city area exhibit distinct spatial organization patterns. (3) POI density and GVI had a substantial negative correlation (r = −0.085), suggesting a complicated process of interaction between green space and urban functions. The study reveals that the fairness of green visibility in historical and cultural cities presents the characteristics of differentiated distribution in different spatial scales, which provides a scientific basis for the optimization of greening spatial layouts in historical and cultural cities while preserving the traditional landscape. Full article

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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

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

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

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

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

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

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

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

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

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

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

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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0 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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