ISPRS International Journal of Geo-Information

33 pages, 14142 KB

Open AccessArticle

Leveraging Geospatial Techniques and Publicly Available Datasets to Develop a Cost-Effective, Digitized National Sampling Frame: A Case Study of Armenia

by Saida Ismailakhunova, Avralt-Od Purevjav, Tsenguunjav Byambasuren and Sarchil H. Qader

ISPRS Int. J. Geo-Inf. 2026, 15(4), 145; https://doi.org/10.3390/ijgi15040145 - 26 Mar 2026

Abstract

The lack of a reliable national sampling frame poses a major challenge for conducting representative population and household surveys, particularly in developing countries affected by displacement and rapid territorial change. This study addresses this gap by developing Armenia’s first digitized national sampling frame, [...] Read more.

The lack of a reliable national sampling frame poses a major challenge for conducting representative population and household surveys, particularly in developing countries affected by displacement and rapid territorial change. This study addresses this gap by developing Armenia’s first digitized national sampling frame, where accessible survey frames are severely limited. We introduce an innovative pre-EA tool to semi-automatically construct the digital sampling frame using publicly available datasets. Compared with traditional approaches, this method outperforms in several ways: it enables rapid, semi-automated frame construction, minimizes resource requirements, eliminates geometric errors associated with manual digitization, and produces pre-census EAs (pre-EAs) that both nest within administrative boundaries and align with visible ground features. The approach also integrates gridded population data to reflect recent urbanization and migration, generating pre-census EAs and urban–rural classifications suitable for national surveys. The sampling frame was successfully applied in the World Bank’s “Listening to Armenia” survey. Overall, the study demonstrates that automated, data-driven approaches can efficiently produce accurate, scalable, and adaptable national sampling frames, offering potential utility in other countries facing similar constraints. Full article

15 pages, 72663 KB

Open AccessArticle

LLM-Based Map Conflation: Performance Assessment on Matching Embedded Road Lines

by Müslüm Hacar and Özge Öztürk Hacar

ISPRS Int. J. Geo-Inf. 2026, 15(4), 144; https://doi.org/10.3390/ijgi15040144 - 25 Mar 2026

Abstract

Map conflation is essential for integrating heterogeneous road datasets, but it often requires region- and data-specific algorithm design to automate the complex identification of feature-to-feature correspondences. This effort is increased when only cartographic products are available instead of GIS-ready vectors since both digitization [...] Read more.

Map conflation is essential for integrating heterogeneous road datasets, but it often requires region- and data-specific algorithm design to automate the complex identification of feature-to-feature correspondences. This effort is increased when only cartographic products are available instead of GIS-ready vectors since both digitization or matching corresponding features manually are labor-intensive. In this study, we assess the performance of a multimodal LLM, GPT-5 “thinking” mode for map conflation directly on a PDF map where road networks from TomTom and OpenStreetMap are embedded as colored polylines. We instruct the LLM to interpret the PDF, extract road geometries and their identifiers, and generate both strict 1:1 and flexible M:N matches. In any hybrid-patterned network cases located around Bosphorus, Istanbul, while M:N matching process increased the number of matches, it also increased false positives and lowered overall F1 scores. In contrast, 1:1 matching produced more balanced correctness-completeness results. The model achieves its highest performance in the cellular-patterned networks. The results show that LLM-based matching can detect a substantial share of true correspondences in such a challenging hybrid setting, but performance clearly depends on the matching strategy: strict or flexible. It highlights both the potential promise and the current limitations of matching embedded road lines. Full article

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33 pages, 40370 KB

Open AccessArticle

Jewelry Store Cluster Forms and Characteristics of Urban Commercial Spaces in Macau

by Jingwei Liang, Liang Zheng, Qingnian Deng, Yufei Zhu, Jiahai Liang and Yile Chen

ISPRS Int. J. Geo-Inf. 2026, 15(4), 143; https://doi.org/10.3390/ijgi15040143 - 25 Mar 2026

Abstract

As a world-renowned tourist and gaming city, Macau’s jewelry industry has formed significant spatial clustering driven by the integration of the tourism and gaming industries. However, existing research has not thoroughly explored the coupling mechanism between the agglomeration of this high-value industry and [...] Read more.

As a world-renowned tourist and gaming city, Macau’s jewelry industry has formed significant spatial clustering driven by the integration of the tourism and gaming industries. However, existing research has not thoroughly explored the coupling mechanism between the agglomeration of this high-value industry and tourism potential circulation characteristics. Meanwhile, the industry confronts practical challenges, including an unbalanced layout between high-end and local brands, intense competition in core areas, and distinct service coverage blind spots in non-core areas. To fill these research gaps, this study takes the Macau Special Administrative Region as the research scope, integrates POI kernel density estimation, Voronoi diagram analysis, and space syntax to construct a three-dimensional analytical framework encompassing agglomeration intensity, service scope, and tourism flow matching, and systematically investigates the spatial clustering pattern of jewelry stores and its coupling mechanism with tourism potential circulation. The study reveals the following findings: (1) Jewelry stores exhibit a dual-segment, four-core clustering pattern. Among these, 38 high-end brands are concentrated in casino complexes and their surrounding areas, 34 comprehensive brands are evenly distributed across core and residential areas, and 300 local brands are mainly scattered in residential areas of the Macau Peninsula. (2) The service scope of jewelry stores is negatively correlated with agglomeration density. The Voronoi diagram area in core areas is 62% smaller than that in non-core areas, accompanied by a high degree of overlap—35% for high-end brands—and intense competition. In contrast, non-core areas have coverage blind spots accounting for 18% of Macau’s total land area. (3) Under a 300 m walking radius, high-integration paths identified by space syntax demonstrate an 85% matching degree with tourist routes, and the four core areas form differentiated coupling types. This study is the first to quantify the differentiated coupling mechanism between multi-level jewelry brands and tourism potential circulation. It further improves the GIS analysis framework for the coupling between commercial agglomeration and tourist behavior. The revealed negative correlation between service scope and agglomeration density, and the adaptive principle between brand spatial layout and regional functional attributes, provide universal references for similar business formats in tourist cities, including cultural and creative retail and characteristic catering. In practice, this research optimizes the spatial layout of Macau’s jewelry industry and increases the coverage rate of service blind spots to over 85%. It also provides scientific support for tourism route planning and the coordinated development of tourism and commerce in high-density tourist destinations. Full article

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

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19 pages, 29486 KB

Open AccessArticle

Mapping Mental Wellbeing and Air Pollution: A Geospatial Data Approach

by Morgan Ecclestone and Thomas Johnson

ISPRS Int. J. Geo-Inf. 2026, 15(4), 142; https://doi.org/10.3390/ijgi15040142 (registering DOI) - 25 Mar 2026

Abstract

Urban air pollution is increasingly recognised as a determinant of mental wellbeing, yet most existing studies rely on static exposure estimates and lack spatial granularity. This limits understanding of how pollutant-specific patterns influence psychological states in real-world settings. To address this gap, we [...] Read more.

Urban air pollution is increasingly recognised as a determinant of mental wellbeing, yet most existing studies rely on static exposure estimates and lack spatial granularity. This limits understanding of how pollutant-specific patterns influence psychological states in real-world settings. To address this gap, we integrate real-time environmental and physiological data from 40 participants using the DigitalExposome dataset, applying multivariate and spatial analysis techniques. Our findings confirm that Particulate Matter (PM_2.5) exerts the strongest negative association with mental wellbeing while extending prior work by establishing a preliminary ranking of other pollutants Particulate Matter (PM₁₀), Particulate Matter (PM₁), Carbon Monoxide (CO), Nitrogen Dioxide (NO₂), Ammonia (NH₃). We applied statistical and spatial analysis methods, including heatmaps and Voronoi diagrams, to explore links between pollutants and wellbeing and compare the relative influence of air pollution and noise. This enabled identification of pollutant-specific hotspots and multi-level wellbeing patterns across individual, accumulated, and collective scales. These results demonstrate the value of spatial analysis for environmental health research and support targeted urban interventions, such as green space placement and traffic re-routing, to mitigate mental wellbeing risks. Full article

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

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24 pages, 13559 KB

Open AccessArticle

Where Matters: Geographic Influences on Emergency Response—A Case Study of Dallas, Texas

by Yanan Wu, Yalin Yang and May Yuan

ISPRS Int. J. Geo-Inf. 2026, 15(4), 141; https://doi.org/10.3390/ijgi15040141 - 25 Mar 2026

Abstract

Does where an incident happens affect how quickly first responders arrive? Timely emergency responses are important to urban safety. However, the combined influence of street-level environments, operational conditions, and neighborhood contexts on dispatch performance remains unclear. We examined such geographical complexity by modeling [...] Read more.

Does where an incident happens affect how quickly first responders arrive? Timely emergency responses are important to urban safety. However, the combined influence of street-level environments, operational conditions, and neighborhood contexts on dispatch performance remains unclear. We examined such geographical complexity by modeling geographic predictors for whether emergency vehicles successfully arrived at incidents in the city of Dallas within the city’s eight-minute benchmark. Using 250,647 incidents and 56 million GPS points along emergency dispatch routes in 2016, we compiled fourteen spatial and operational variables for every incident to train a Bayesian-optimized random forest classifier. The fourteen variables characterized street network topology, roadway attributes, land use, and socioeconomic status, and the model achieved an accuracy of 77.26% in predicting whether emergency response arrived at an incident within eight minutes. A longer distance to dispatch stations, dispatching from non-nearest stations, and low street–network integration were the strongest predictors of unsuccessful responses. Higher-income areas showed slightly elevated unsuccessful rates linked to frequent construction-related disruptions. These findings highlight emergency response as a coupled spatial–operational–temporal process and underscore the need for context-sensitive dispatch strategies and coordinated urban planning. Full article

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

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23 pages, 11145 KB

Open AccessArticle

DiffLiGS: Diffusion-Guided LiDAR-Enhanced 3D Gaussian Splatting

by Shucheng Gong, Hong Xie, Jiang Song, Longze Zhu and Hongping Zhang

ISPRS Int. J. Geo-Inf. 2026, 15(4), 140; https://doi.org/10.3390/ijgi15040140 - 24 Mar 2026

Abstract

Multi-view 3D reconstruction is essential for smart city, supporting applications such as smart city planning and autonomous navigation. While traditional reconstruction pipelines and recent neural implicit methods, such as NeRF, achieve high visual fidelity, they often struggle with geometric accuracy and sparse-view scenarios. [...] Read more.

Multi-view 3D reconstruction is essential for smart city, supporting applications such as smart city planning and autonomous navigation. While traditional reconstruction pipelines and recent neural implicit methods, such as NeRF, achieve high visual fidelity, they often struggle with geometric accuracy and sparse-view scenarios. To address this challenge, we present DiffLiGS, a novel multi-modal 3D reconstruction framework that integrates LiDAR point clouds and LiDAR-guided diffusion-based priors into the 3D Gaussian Splatting (3DGS) pipeline, enabling high-fidelity and geometrically accurate models. Our method first densifies sparse LiDAR depths using a diffusion model and refines them through multi-view geometric constraints, producing dense LiDAR depth maps that provide robust supervision for 3DGS optimization. Leveraging these dense depth maps, we guide a Stable Video Diffusion model to synthesize novel view images, which are incorporated into training to enhance reconstruction completeness and visual realism. By jointly fusing rich appearance cues from multi-view images with precise LiDAR-derived geometry and diffusion priors, DiffLiGS achieves unified, geometry-aware 3D scene representations. Our extensive experiments demonstrate that our approach significantly improves both geometric accuracy and rendering quality compared to existing 3D reconstruction methods, enabling real-time, high-precision modeling of complex urban environments. Full article

(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 3rd Edition)

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27 pages, 6869 KB

Open AccessArticle

Pedestrian Routing and Walkability Inference Using Realized WiFi Connectivity

by Tun Tun Win, Thanisorn Jundee and Santi Phithakkitnukoon

ISPRS Int. J. Geo-Inf. 2026, 15(3), 139; https://doi.org/10.3390/ijgi15030139 - 23 Mar 2026

Abstract

Traditional pedestrian routing algorithms typically minimize physical distance or travel time, often overlooking contextual factors that influence route choice in digitally connected environments. As public WiFi infrastructure becomes increasingly prevalent in smart-city districts and university campuses, digital connectivity may influence pedestrian mobility decisions. [...] Read more.

Traditional pedestrian routing algorithms typically minimize physical distance or travel time, often overlooking contextual factors that influence route choice in digitally connected environments. As public WiFi infrastructure becomes increasingly prevalent in smart-city districts and university campuses, digital connectivity may influence pedestrian mobility decisions. This study introduces P-WARP, a multi-factor routing and inference framework that reconstructs latent pedestrian preferences by integrating physical effort, environmental walkability, and WiFi connectivity within a unified semantic graph. The empirical analysis is conducted on the Chiang Mai University campus, a digitally connected environment serving as a smart campus testbed. The framework integrates heterogeneous spatial datasets, including OpenStreetMap topology, Shuttle Radar Topography Mission elevation data, environmental walkability grids, and WiFi roaming logs collected via a custom mobile sensing application from 21 volunteers across 71 verified walking trips. Two routing strategies are evaluated: a Global Static Model, representing infrastructure-based connectivity assumptions, and a Trip-Centric Dynamic Model, incorporating realized connectivity histories. Model parameters are calibrated using Bayesian Optimization with five-fold cross-validation. Results show that incorporating realized connectivity reduces trajectory reconstruction error by 6.84% relative to the baseline. The learned parameters reveal a notable detour tolerance, suggesting that stable digital connectivity can influence pedestrian route choice in digitally instrumented environments. Full article

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21 pages, 4034 KB

Open AccessArticle

Does GDP Drive Urban Well-Being? Evidence from China’s Urban Physical Examination Survey

by Jincheng Cai and Ju He

ISPRS Int. J. Geo-Inf. 2026, 15(3), 138; https://doi.org/10.3390/ijgi15030138 - 23 Mar 2026

Abstract

The relationship between economic development and residents’ perceived urban well-being remains an important question in urban research. This study examines whether the relationship between GDP and city-level satisfaction exhibits non-linear patterns or plateau effects. Using the 2024 nationwide Urban Physical Examination (UPE) resident [...] Read more.

The relationship between economic development and residents’ perceived urban well-being remains an important question in urban research. This study examines whether the relationship between GDP and city-level satisfaction exhibits non-linear patterns or plateau effects. Using the 2024 nationwide Urban Physical Examination (UPE) resident survey in China, this study assesses how city economic level relates to perceived urban well-being, proxied by city-level overall satisfaction. The survey was conducted in April–June 2024 in the main urban districts of 47 cities, using 499,500 valid questionnaires. We aggregate satisfaction to the city level, match it with GDP and key city characteristics, and estimate the GDP–satisfaction association using restricted cubic splines (RCS) to test for potential non-linearity. Across unadjusted and covariate-adjusted models (accounting for population scale and density, industrial structure, fiscal capacity, and regional effects), results show a robust positive association between economic level and satisfaction, while nested-model tests provide no evidence that spline terms improve fit over a linear specification within the observed GDP range. Substantial dispersion around the fitted curve indicates that GDP is an enabling capacity rather than a sufficient condition, pointing to cross-city differences in how effectively resources are converted into lived urban quality. We propose using GDP-adjusted satisfaction benchmarking within the UPE cycle to identify underperforming cities and prioritize targeted governance and renewal actions. Full article

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

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19 pages, 4258 KB

Open AccessArticle

Uneven Paths to Health: A Spatial Analysis of Sidewalk Conditions and Healthcare Access for Older Adults

by Nikolaos Stasinos, Kleomenis Kalogeropoulos, Andreas Tsatsaris and Marianna Mantzorou

ISPRS Int. J. Geo-Inf. 2026, 15(3), 137; https://doi.org/10.3390/ijgi15030137 - 23 Mar 2026

Abstract

As urban populations age, the built environment becomes a vital determinant of health equity. This research evaluates the sidewalk infrastructure, surrounding the Health Center in Egaleo, Greece, in order to quantify its impact on healthcare accessibility for older adults. Using a GIS-based approach [...] Read more.

As urban populations age, the built environment becomes a vital determinant of health equity. This research evaluates the sidewalk infrastructure, surrounding the Health Center in Egaleo, Greece, in order to quantify its impact on healthcare accessibility for older adults. Using a GIS-based approach to simulate realistic navigation, a routing algorithm prioritized the “easiest” path over the shortest distance by transforming accessibility scores into traversal costs. The results revealed a significant disadvantage in healthcare access, with routes to the Health Center scoring lower than the average accessibility of the greater study area. In addition, the negative correlation (r = −0.20, p < 0.001) confirms the pattern of accessibility disparity, where neighborhoods with the highest older adult density consistently face the poorest infrastructure. Eventually, Global Moran’s I of 0.912 confirms strong spatial autocorrelation, Local Indicators of Spatial Association (LISA) identifies “Accessibility Deserts” which comprise a 92.5% absence of crosswalks and an 81.7% rate of obstructions. This study outlines that those who depend most on the sidewalk network are disproportionately affected by inadequate urban planning conditions. By underscoring the necessity to remediate these low-accessibility clusters, public health is improved, ensuring equitable healthcare access and supporting healthy aging. Full article

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

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20 pages, 3850 KB

Open AccessArticle

Optimization of Indoor Pedestrian Counting Based on Target Detection and Tracking

by Laihao Song, Litao Han, Jiayan Wang, Hengjian Feng and Ran Ji

ISPRS Int. J. Geo-Inf. 2026, 15(3), 136; https://doi.org/10.3390/ijgi15030136 - 21 Mar 2026

Abstract

Real-time, precise monitoring of the number and distribution of indoor personnel is crucial for building safety management, operational optimization, and personnel scheduling. However, narrow entrances and high-density passageways often lead to missed detections, false positives, and tracking failures in pedestrian detection, thereby reducing [...] Read more.

Real-time, precise monitoring of the number and distribution of indoor personnel is crucial for building safety management, operational optimization, and personnel scheduling. However, narrow entrances and high-density passageways often lead to missed detections, false positives, and tracking failures in pedestrian detection, thereby reducing cross-line counting accuracy. Additionally, edge devices deployed in practical scenarios frequently process multiple video streams simultaneously, resulting in computational resource constraints. To address these challenges, this paper proposes a lightweight, enhanced multi-object pedestrian tracking and counting method tailored for indoor scenarios by optimizing deep learning models. Firstly, modular optimizations are applied to the YOLOv8n model to construct a more lightweight detector, RL_YOLOv8, reducing computational overhead while maintaining accuracy. Secondly, correlated pedestrian auxiliary prediction and pedestrian position change constraints are employed to mitigate ID switching, tracking interruptions, and trajectory jumps in dense scenes. Finally, a buffer zone auxiliary counting strategy is designed to further reduce missed detections of pedestrians crossing lines. Experimental results demonstrate that compared to the original detection-and-tracking-based line-crossing counting method, the improved approach effectively enhances counting accuracy and real-time performance, better meeting the requirements of practical intelligent security and crowd monitoring systems. Full article

(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 3rd Edition)

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33 pages, 31833 KB

Open AccessArticle

Spherical Geodesic Bounds and a k-Circle Coverage Formulation

by Josiah Lansang and Faramarz F. Samavati

ISPRS Int. J. Geo-Inf. 2026, 15(3), 135; https://doi.org/10.3390/ijgi15030135 - 18 Mar 2026

Abstract

In this article, we introduce analogues of classic Euclidean bounds, including spherical caps, geodesic axis-aligned bounding boxes (AABBs), geodesic oriented bounding boxes (OBBs), and geodesic k-discrete oriented polytopes (k-DOPs). We also formulate k-circle coverage, a union of variable-radius caps [...] Read more.

In this article, we introduce analogues of classic Euclidean bounds, including spherical caps, geodesic axis-aligned bounding boxes (AABBs), geodesic oriented bounding boxes (OBBs), and geodesic k-discrete oriented polytopes (k-DOPs). We also formulate k-circle coverage, a union of variable-radius caps solved by a binary integer program over candidates generated from Discrete Global Grid System (DGGS)-based rasterization. As all constructions run directly on the spherical surface,

S^{2}

, they preserve geodesic distances and avoid projection distortion. We benchmark these methods on seven country boundary polygons consisting of thousands of points, and report construction time, memory, tightness, and query throughput. Results show our analytic geodesic bounds deliver orders of magnitude improvements over exact tests, with trade-offs in tightness: spherical caps are fastest but loosest; geodesic OBBs are a strong balance; geodesic k-DOPs consistently have the tightest bounds. k-circle coverage has spherical cap query speed while also having locally adaptive fits; construction time increases with DGGS resolution. Altogether, these bounds specific to the sphere provide practical, conservative filters for globe-scale Digital Earth queries. Full article

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28 pages, 7055 KB

Open AccessArticle

Fine-Scale and Population-Weighted PM_2.5 Modeling in Melbourne: Towards Detailed Urban Exposure Mapping

by Jun Gao, Xuying Ma, Qian Chayn Sun, Wenhui Cai, Xiaoqi Wang, Yifan Wang, Zelei Tan, Danyang Li, Yuanyuan Fan, Leshu Zhang, Yixin Xu, Xueyao Liu and Yuxin Ma

ISPRS Int. J. Geo-Inf. 2026, 15(3), 134; https://doi.org/10.3390/ijgi15030134 - 17 Mar 2026

Abstract

Despite concern over air pollution, fine-scale spatial and demographic disparities in exposure remain largely unquantified in Australian cities due to sparse monitoring and coarse models. In Greater Melbourne, this gap limits neighbourhood-level assessment of PM_2.5 exposure and associated environmental inequalities. To address [...] Read more.

Despite concern over air pollution, fine-scale spatial and demographic disparities in exposure remain largely unquantified in Australian cities due to sparse monitoring and coarse models. In Greater Melbourne, this gap limits neighbourhood-level assessment of PM_2.5 exposure and associated environmental inequalities. To address this gap, we integrated 6-month averaged PM_2.5 observations (October 2023 to March 2024) from 5 regulatory monitoring stations and 13 low-cost sensors (LCSs) to develop a land use regression (LUR) model estimating concentrations at a 100 m resolution. These estimates were used to calculate population-weighted PM_2.5 exposure (PWE) at the mesh block level across Melbourne. To examine factors associated with spatial heterogeneity in PWE, we applied a hybrid modeling framework combining Spatially Explicit Random Forest (Spatial-RF) and Geographically Weighted Regression (GWR), incorporating physical, built-environment, and socio-demographic variables from the Synthesized Multi-Dimensional Environmental Exposure Database (SEED). The Spatial-RF model initially exhibited an R² of 0.56. After multicollinearity diagnostics using the Variance Inflation Factor (VIF), three key explanatory variables were selected for GWR modeling: the Normalized Difference Vegetation Index (NDVI), the Index of Education and Occupation (IEO), and the proportion of culturally and linguistically diverse populations (CALDP). The developed GWR model achieved higher model performance (R² = 0.65) than Spatial-RF and global Ordinary Least Squares (OLS) regression (R² = 0.38), revealing strong spatial non-stationarity. Results show that PWE generally ranged from 5 to 7 µg/m³, exceeding the 2021 WHO air quality guideline, with hotspots in the urban core and along major transport corridors. Elevated exposure occurred in both socioeconomically disadvantaged areas and residents in urban centers with higher socio-economic status, reflecting complex, spatially contingent exposure inequalities. These findings support fine-scale, equity-oriented air quality management. Full article

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

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19 pages, 1537 KB

Open AccessArticle

Impact of Spatial Aggregation Level on Environmental Epidemiology Analyses: A Case Study of Combined Heat and Ozone Effects on Cardiovascular Emergencies

by Lorenzo Gianquintieri, Amruta Umakant Mahakalkar and Enrico Gianluca Caiani

ISPRS Int. J. Geo-Inf. 2026, 15(3), 133; https://doi.org/10.3390/ijgi15030133 - 17 Mar 2026

Abstract

Background: Spatial granularity plays a central role in the analysis of environmental hazards, yet its influence on health impact assessment remains overlooked. This study explicitly treats spatial aggregation level as a methodological variable and examines how different spatial aggregation strategies affect the association [...] Read more.

Background: Spatial granularity plays a central role in the analysis of environmental hazards, yet its influence on health impact assessment remains overlooked. This study explicitly treats spatial aggregation level as a methodological variable and examines how different spatial aggregation strategies affect the association between high temperature, ozone, and out-of-hospital cardiovascular emergencies recorded by emergency medical services. Methods: A distribution thresholding approach is applied to both the environmental hazard and the health outcome. The analysis is conducted at three spatial levels: a fully aggregated region-wide level, population-based districts, and a combined strategy that cumulates district level results. The model estimates the Odds Ratio for each configuration. Results: The combined district-based strategy provides the most robust association, with an Odds Ratio of 1.13 (95% confidence interval 1.10 to 1.17). The region-wide and single district approaches show weaker or inconsistent significance. The findings indicate that the spatial level of analysis heavily impacts both the significance and the interpretability of the statistical results. Conclusions: The study demonstrates that the spatial structure of data strongly influences the detection of short-term health effects linked to environmental stressors. This contributes to the geomatics field by explicitly isolating spatial aggregation as an analytical dimension, demonstrating how spatial aggregation choices and explicit consideration of the Modifiable Areal Unit Problem can enhance methodological accuracy, support clearer spatial reasoning, and guide the development of more reliable territorial health indicators. Full article

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

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20 pages, 2595 KB

Open AccessArticle

OFF-SETT: A Semantic Framework for Annotating Trends in Spatiotemporal Data

by Camille Bernard, Jérôme Gensel, Daniela F. Milon-Flores, Gregory Giuliani and Marlène Villanova

ISPRS Int. J. Geo-Inf. 2026, 15(3), 132; https://doi.org/10.3390/ijgi15030132 - 17 Mar 2026

Abstract

The world is undergoing rapid transformations driven by climate change, socio-economic pressures, and geopolitical tensions. Monitoring these dynamics is essential to understand and anticipate territorial change. Although initiatives such as the European Union’s Open Data program promote spatiotemporal datasets (e.g., population, land use), [...] Read more.

The world is undergoing rapid transformations driven by climate change, socio-economic pressures, and geopolitical tensions. Monitoring these dynamics is essential to understand and anticipate territorial change. Although initiatives such as the European Union’s Open Data program promote spatiotemporal datasets (e.g., population, land use), analyzing and interpreting these data over time remains complex and requires technical expertise, limiting their accessibility. This research proposes Semantic Web-based methods to detect and annotate trends in spatiotemporal series, thereby assisting in the systematic analysis of temporal patterns. We introduce the SETT ontology (SEmantic Trajectory of Territory) and its OFF-SETT framework (Ontological Framework For SETT), enabling the formal description of territorial trends and their publication as semantic trajectories in the Linked Open Data cloud. The study delivers (i) a generic methodology for detecting and describing trajectories in spatiotemporal datasets; (ii) a framework for automatically generating knowledge graphs capturing these trajectories; (iii) a knowledge graph describing trajectories of demographic and satellite-derived variables (e.g., temperature, water, vegetation) for study areas in France and Switzerland; and (iv) a web-based geovisualization platform. The approach shows that Semantic Web technologies bridge complex spatiotemporal analysis and public accessibility. By publishing territorial trajectories as knowledge graphs, it fosters transparency, interoperability, and reuse of data, supporting informed decision-making and citizen engagement. Full article

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35 pages, 19390 KB

Open AccessArticle

Dense Local Azimuth–Elevation Map for the Integration of GIS Data and Camera Images

by Gilbert Maître

ISPRS Int. J. Geo-Inf. 2026, 15(3), 131; https://doi.org/10.3390/ijgi15030131 - 16 Mar 2026

Abstract

The integration of outdoor camera images with three-dimensional (3D) geographic information on the observed scene is of interest for many video acquisition applications. To solve this data fusion problem, camera images have to be matched with the 3D geometry provided by a geographic [...] Read more.

The integration of outdoor camera images with three-dimensional (3D) geographic information on the observed scene is of interest for many video acquisition applications. To solve this data fusion problem, camera images have to be matched with the 3D geometry provided by a geographic information system (GIS). Considering a camera with a known geographical position, this paper proposes the use of a dense local azimuth–elevation map (LAEM) derived from a gridded digital elevation model (DEM) to represent the data and thus facilitate the matching of GIS and image data. To each regularly sampled azimuth and elevation angle pair, this map assigns the geographic point derived from the DEM viewed in this direction. The problem of computing the LAEM from the DEM is closely related to that of surface rendering, for which solutions exist in computer graphics. However, rendering software cannot be used directly in this case, since their view directions are constrained by the pinhole camera model and the apparent colour, rather than the position of the viewed point, is assigned to the viewing direction. Therefore, this paper also proposes a specific algorithm for the computation of the LAEM from the DEM. A MATLAB^® implementation of the algorithm is also provided, which is tailored to process the DEM dataset swissALTI3D from the Swiss Federal Office of Topography swisstopo. Full article

(This article belongs to the Topic Advances in Sensor Data Fusion and AI for Environmental Monitoring)

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18 pages, 3237 KB

Open AccessArticle

GIS-MCDA-Based Assessment of Groundwater Abstraction Potential Under Data Constraints: A Case Study from the Rzeszów Region, Poland

by Wojciech Wałachowski, Kamil Maciuk, Ugo Falchi and Artur Krawczyk

ISPRS Int. J. Geo-Inf. 2026, 15(3), 130; https://doi.org/10.3390/ijgi15030130 - 16 Mar 2026

Abstract

This study presents a comprehensive GIS-based multicriteria decision analysis (MCDA) framework for identifying prospective groundwater abstraction sites in a 9 municipality region of South-East Poland (Podkarpackie Voivodeship), covering approximately 830 km². The analysis integrated hydrogeological parameters (aquifer thickness, quality, productivity, water [...] Read more.

This study presents a comprehensive GIS-based multicriteria decision analysis (MCDA) framework for identifying prospective groundwater abstraction sites in a 9 municipality region of South-East Poland (Podkarpackie Voivodeship), covering approximately 830 km². The analysis integrated hydrogeological parameters (aquifer thickness, quality, productivity, water table depth, protection degree, recharge zones) with spatial risk factors (contamination sources, exclusion zones) and population density patterns. The MCDA approach provides a decision support tool for municipal authorities tasked with water infrastructure planning under conditions of limited baseline data. The framework demonstrates the utility of a carefully specified GIS-MCDA framework to provide such support, while highlighting the need for improved data sharing to enable full statistical validation. Full article

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29 pages, 6295 KB

Open AccessArticle

Machine Learning Framework for Evaluating the Cooling Performance of Wetlands in a Tropical Coastal City

by Nhat-Duc Hoang

ISPRS Int. J. Geo-Inf. 2026, 15(3), 129; https://doi.org/10.3390/ijgi15030129 - 15 Mar 2026

Abstract

This study investigates the cooling effects of coastal wetland systems in Hue City, Vietnam. The analysis focuses on their riparian buffer zones, defined here as areas within 600 m of the wetland boundary. Landsat 8 imagery was used to derive land surface temperature [...] Read more.

This study investigates the cooling effects of coastal wetland systems in Hue City, Vietnam. The analysis focuses on their riparian buffer zones, defined here as areas within 600 m of the wetland boundary. Landsat 8 imagery was used to derive land surface temperature (LST) from 1 March to 31 July 2025—a recent period marked by multiple heatwaves across the region. To assess the cooling performance of wetlands, data samples were collected within the buffer zones. A Light Gradient Boosting Machine was trained to characterize the relationship between cooling intensity and a set of influencing factors (e.g., distance to wetland boundary, land use/land cover, built-up density, and green space density). The model explains approximately 91% of the variation in cooling intensity around wetlands. Notably, a machine-learning-based simulation framework was proposed to attain insights into the cooling characteristics of the riparian zone. The result indicates a mean cooling effect of about 2 °C and an effective cooling distance of 210 m from the wetland boundary. Partial dependence analysis further reveals that increasing built-up density substantially weakens cooling performance and implies that, for the conditions observed in Hue City, maintaining built-up density near wetlands below roughly 45% is favorable for sustaining effective cooling of the blue space, as indicated by the model-based partial dependence analysis. Overall, the research findings provide a data-driven basis for informing urban planning and wetland management in Hue City to mitigate heat stress. Full article

(This article belongs to the Special Issue Spatial Information for Improved Living Spaces (2nd Edition))

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25 pages, 10373 KB

Open AccessArticle

Ionospheric Scintillation Anomalies from COSMIC-2 GNSS-RO from 2019 and 2024 as Potential Earthquake Precursors

by Badr-Eddine Boudriki Semlali, Carlos Molina, Hyuk Park and Adriano Camps

ISPRS Int. J. Geo-Inf. 2026, 15(3), 128; https://doi.org/10.3390/ijgi15030128 - 15 Mar 2026

Abstract

Currently, there are no consistent earthquake precursors for early warning. However, the correlation between earthquakes and ionospheric scintillation, measured using the S₄ index via GNSS-RO, is under active study. This research analyzes S₄ anomalies as a potential earthquake proxy, using GNSS-RO [...] Read more.

Currently, there are no consistent earthquake precursors for early warning. However, the correlation between earthquakes and ionospheric scintillation, measured using the S₄ index via GNSS-RO, is under active study. This research analyzes S₄ anomalies as a potential earthquake proxy, using GNSS-RO data from COSMIC-2/TGRS (Tri-GNSS Radio Occultation System) collected from 2019 to 2024. It examines over 71,000 global earthquakes within ±60° of the equator with magnitudes greater than 4. The quality of S₄ anomalies has been enhanced by filtering out space-weather-induced disturbances using the daily planetary geomagnetic index (Kp) and the solar activity flag collected from ground stations. The S₄ anomalies were calculated using robust statistical methods, such as the standard deviation and the interquartile range. This study evaluated the correlation with a confusion matrix, a receiver operating characteristic curve, and various figures of merit. The results demonstrated a promising positive S₄ anomaly between 1 and 7 days before the analyzed earthquakes, indicating the potential of ionospheric scintillation as an earthquake precursor, with the robust statistical methods employed instilling confidence in the validity of our findings. Full article

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

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26 pages, 11061 KB

Open AccessArticle

CTSTSpace: A Framework for Behavior Pattern Recognition and Perturbation Analysis Based on Campus Traffic Semantic Trajectories

by Lin Lin, Mengjie Jin, Zhiju Chen, Wenhao Men, Yefei Shi and Guoqing Wang

ISPRS Int. J. Geo-Inf. 2026, 15(3), 127; https://doi.org/10.3390/ijgi15030127 - 14 Mar 2026

Abstract

In smart campus construction, behavior pattern recognition and perturbation analysis serve as the cornerstones for achieving a transition from passive response to dynamic regulation, with intelligent perception and anomaly diagnosis methods based on campus traffic flow underpinning transportation system resilience. Traditional research methods [...] Read more.

In smart campus construction, behavior pattern recognition and perturbation analysis serve as the cornerstones for achieving a transition from passive response to dynamic regulation, with intelligent perception and anomaly diagnosis methods based on campus traffic flow underpinning transportation system resilience. Traditional research methods suffer from issues such as privacy risks, coarse modeling, and limitations from single data formats, labeling difficulties, and coverage gaps. This study proposes a refined semantic trajectory construction method that integrates multi-source data (e.g., mobile signaling data, maps and weather conditions), known as the Campus Transportation Semantic Trajectories Space (CTSTSpace) framework. It enables the precise identification of semantic origin–destination points from dynamic personnel trajectories, quantifies service performance through real-time road network mapping, and models multidimensional perturbations, achieving full campus coverage without complex labeling while ensuring robust privacy protection. Under clear weather conditions, the analysis demonstrates accurate recognition of travel behavior patterns (dwelling, aggregation, mobility, and congestion) that synchronize with class schedules, where vehicle speeds drop by over 50% during peak hours. Under rainy weather perturbations, it captured demand shifts (e.g., peak hour offsets of 30–60 min and a 6.8–9.2% reduction in long-distance dining trips) and speed reductions (52.15–73.74%). This approach provides critical insights for resilient smart campus traffic management. Full article

(This article belongs to the Topic Applications of Intelligent Technologies in the Life Cycle of Transportation Infrastructure)

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