ISPRS International Journal of Geo-Information

23 pages, 3631 KB

Open AccessArticle

Modeling Spatial Determinants of Blue School Certification: A Maxent Approach in Mallorca

by Christian Esteva-Burgos and Maurici Ruiz-Pérez

ISPRS Int. J. Geo-Inf. 2025, 14(10), 378; https://doi.org/10.3390/ijgi14100378 - 26 Sep 2025

The Blue Schools initiative integrates the ocean into classroom learning through project-based approaches, cultivating environmental awareness and a deeper sense of responsibility toward marine ecosystems and human–ocean interactions. Although the European Blue School initiative has grown steadily since its launch in 2020, its [...] Read more.

The Blue Schools initiative integrates the ocean into classroom learning through project-based approaches, cultivating environmental awareness and a deeper sense of responsibility toward marine ecosystems and human–ocean interactions. Although the European Blue School initiative has grown steadily since its launch in 2020, its uneven uptake raises important questions about the territorial factors that influence certification. This study examines the spatial determinants of Blue School certification in Mallorca, Spain, where a bottom-up pilot initiative successfully certified 100 schools. Using Maximum Entropy (MaxEnt) modeling, we estimated the spatial probability of certification based on 16 geospatial variables, including proximity to Blue Economy actors, hydrological networks, transport accessibility, and socio-economic indicators. The model achieved strong predictive performance (AUC = 0.84) and revealed that features such as freshwater ecosystems, traditional economic structures, and sustainable public transport play a greater role in school engagement than coastal proximity alone. The resulting suitability map identifies over 30 high-potential, non-certified schools, offering actionable insights for targeted outreach and educational policy. This research highlights the potential of presence-only modeling to guide the strategic expansion of Blue Schools networks. Full article

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27 pages, 21314 KB

Open AccessArticle

Integrating Remote Sensing and Geospatial-Based Comprehensive Multi-Criteria Decision Analysis Approach for Sustainable Coastal Solar Site Selection in Southern India

by Constan Antony Zacharias Grace, John Prince Soundranayagam, Antony Johnson Antony Alosanai Promilton, Shankar Karuppannan, Wafa Saleh Alkhuraiji, Viswasam Stephen Pitchaimani, Faten Nahas and Yousef M. Youssef

ISPRS Int. J. Geo-Inf. 2025, 14(10), 377; https://doi.org/10.3390/ijgi14100377 - 26 Sep 2025

Abstract

Rapid urbanization across Southern Asia’s coastal regions has significantly increased electricity demand, driving India’s solar sector expansion under the National Solar Mission and positioning the country as the world’s fourth-largest solar market. Nonetheless, methodological limitations remain in applying GIS-based multi-criteria decision analysis (MCDA) [...] Read more.

Rapid urbanization across Southern Asia’s coastal regions has significantly increased electricity demand, driving India’s solar sector expansion under the National Solar Mission and positioning the country as the world’s fourth-largest solar market. Nonetheless, methodological limitations remain in applying GIS-based multi-criteria decision analysis (MCDA) frameworks to coastal urban microclimates, which involve intricate land-use dynamics and resilience constraints. To address this gap, this study proposes a multi-criteria GIS- based Analytical Hierarchy Process (AHP) framework, incorporating remote sensing and geospatial data, to assess Solar Farm Sites (SFSs) suitability, supplemented by sensitivity analysis in Thoothukudi coastal city, India. Ten parameters—covering photovoltaic, climatic, topographic, environmental, and accessibility factors—were used, with Global Horizontal Irradiance (18%), temperature (11%), and slope (11%) identified as key drivers. Results show that 9.99% (13.61 km²) of the area has excellent suitability, mainly in the southwest, while 28.15% (38.33 km²) exhibits very high potential along the southeast coast. Additional classifications include good (22.29%), moderate (32.41%), and low (7.16%) suitability zones. Sensitivity analysis confirmed photovoltaic variables as dominant, with GHI (0.25) and diffuse radiation (0.23) showing the highest impact. The largest excellent zone could support approximately 390 MW, with excellent and very high zones combined offering up to 2080 MW capacity. The findings also underscore opportunities for dual-use solar deployment, particularly on salt pans (17.1%), as well as elevated solar installations in flood-prone areas. Overall, the proposed framework provides robust, spatially explicit insights to support sustainable energy planning and climate-resilient infrastructure development in coastal urban settings. Full article

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19 pages, 2731 KB

Open AccessArticle

Exploring the Spatial Relationship Between Severe Depression, COVID-19 Case Rates, and Vaccination Rates in US Counties: A Spatial Analysis Across Two Time Periods

by Yuqing Wang and Wencong Cui

ISPRS Int. J. Geo-Inf. 2025, 14(10), 376; https://doi.org/10.3390/ijgi14100376 - 25 Sep 2025

Abstract

Severe depression is shaped by complex interactions between public health crises and socioeconomic conditions, yet the spatial and temporal dynamics of these factors remain underexplored. This study investigates the impact of COVID-19 case rates, vaccination rates, and socioeconomic factors on severe depression rates [...] Read more.

Severe depression is shaped by complex interactions between public health crises and socioeconomic conditions, yet the spatial and temporal dynamics of these factors remain underexplored. This study investigates the impact of COVID-19 case rates, vaccination rates, and socioeconomic factors on severe depression rates across 1470 counties in the contiguous USA in 2021 and 2022. We combined Ordinary Least Squares (OLS) regression with Multiscale Geographically Weighted Regression (MGWR) to capture both global associations and local geographic variability. Results show that higher COVID-19 case rates in 2021 were associated with increased rates of severe depression in 2022, while higher vaccination rates during the same period were associated with decreased rates of severe depression. However, these associations weakened when using 2022 data, suggesting a temporal lag in the impact on mental health. MGWR analyses revealed regional disparities: COVID-19 case rates had a stronger impact in the Midwest, while vaccination benefits were more pronounced on the West Coast. Additional factors, such as unemployment, limited sunlight exposure, and the availability of mental health resources, also influenced outcomes. These findings underscore the importance of temporally and geographically nuanced approaches to public mental health interventions and support the need for region-specific strategies to address mental health disparities in the wake of public health crises. Full article

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

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37 pages, 16383 KB

Open AccessArticle

Generating Realistic Urban Patterns: A Controllable cGAN Approach with Hybrid Loss Optimization

by Amgad Agoub and Martin Kada

ISPRS Int. J. Geo-Inf. 2025, 14(10), 375; https://doi.org/10.3390/ijgi14100375 - 25 Sep 2025

Abstract

This study explores the use of conditional Generative Adversarial Networks (cGANs) for simulating urban morphology, a domain where such models remain underutilized but have significant potential to generate realistic and controllable city patterns. To explore this potential, this research includes several contributions: a [...] Read more.

This study explores the use of conditional Generative Adversarial Networks (cGANs) for simulating urban morphology, a domain where such models remain underutilized but have significant potential to generate realistic and controllable city patterns. To explore this potential, this research includes several contributions: a bespoke model architecture that integrates attention mechanisms with visual reasoning through a generalized conditioning layer. A novel mechanism that enables the steering of urban pattern generation through the use of statistical input distributions, the development of a novel and comprehensive training dataset, meticulously derived from open-source geospatial data of Berlin. Our model is trained using a hybrid loss function, combining adversarial, focal and L1 losses to ensure perceptual realism, address challenging fine-grained features, and enforce pixel-level accuracy. Model performance was assessed through a combination of qualitative visual analysis and quantitative evaluation using metrics such as Kullback–Leibler Divergence (KL Divergence), Structural Similarity Index (SSIM), and Dice Coefficient. The proposed approach has demonstrated effectiveness in generating realistic and spatially coherent urban patterns, with promising potential for controllability. In addition to showcasing its strengths, we also highlight the limitations and outline future directions for advancing future work. Full article

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

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22 pages, 15568 KB

Open AccessArticle

Adversarial Obstacle Placement with Spatial Point Processes for Optimal Path Disruption

by Li Zhou, Elvan Ceyhan and Polat Charyyev

ISPRS Int. J. Geo-Inf. 2025, 14(10), 374; https://doi.org/10.3390/ijgi14100374 - 25 Sep 2025

Abstract

We investigate the Optimal Obstacle Placement (OOP) problem under uncertainty, framed as the dual of the Optimal Traversal Path problem in the Stochastic Obstacle Scene paradigm. We consider both continuous domains, discretized for analysis, and already discrete spatial grids that form weighted geospatial [...] Read more.

We investigate the Optimal Obstacle Placement (OOP) problem under uncertainty, framed as the dual of the Optimal Traversal Path problem in the Stochastic Obstacle Scene paradigm. We consider both continuous domains, discretized for analysis, and already discrete spatial grids that form weighted geospatial networks using 8-adjacency lattices. Our unified framework integrates OOP with stochastic geometry, modeling obstacle placement via Strauss (regular) and Matérn (clustered) processes, and evaluates traversal using the Reset Disambiguation algorithm. Through extensive Monte Carlo experiments, we show that traversal cost increases by up to 40% under strongly regular placements, while clustered configurations can decrease traversal costs by as much as 25% by leaving navigable corridors compared to uniform random layouts. In mixed (with both true and false obstacles) scenarios, increasing the proportion of true obstacles from 30% to 70% nearly doubles the traversal cost. These findings are further supported by statistical analysis and stochastic ordering, providing rigorous insights into how spatial patterns and obstacle compositions influence navigation under uncertainty. Full article

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23 pages, 2229 KB

Open AccessArticle

Optimization of Electric Vehicle Charging Station Location Distribution Based on Activity–Travel Patterns

by Qian Zhang, Guiwu Si and Hongyi Li

ISPRS Int. J. Geo-Inf. 2025, 14(10), 373; https://doi.org/10.3390/ijgi14100373 - 25 Sep 2025

Abstract

With the rapid expansion of the electric vehicle (EV) market, optimizing the distribution of charging stations has attracted increasing attention. Unlike internal combustion engine vehicles, EVs are typically charged at the end of a trip rather than during transit. Therefore, analyzing EV users’ [...] Read more.

With the rapid expansion of the electric vehicle (EV) market, optimizing the distribution of charging stations has attracted increasing attention. Unlike internal combustion engine vehicles, EVs are typically charged at the end of a trip rather than during transit. Therefore, analyzing EV users’ charging preferences based on their activity–travel patterns is essential. This study seeks to improve the operational efficiency and accessibility of EV charging stations in Lanzhou City by optimizing their spatial distribution. To achieve this, a novel multi-objective optimization model integrating NSGA-III and TOPSIS is proposed. The methodology consists of two key steps. First, the NSGA-III algorithm is applied to optimize three objective functions: minimizing construction costs, maximizing user satisfaction, and maximizing user convenience, thereby identifying charging station locations that address diverse needs. Second, the TOPSIS method is employed to rank and evaluate various location solutions, ultimately determining the final sitting strategy. The results show that the 232 locations obtained by the optimization model are reasonably distributed, with good operational efficiency and convenience. Most of them are distributed in urban centers and commercial areas, which is consistent with the usage scenarios of EV users. In addition, this study demonstrates the superiority in determining the distribution of charging station locations of the proposed method. In summary, this study determined the optimal distribution of 232 EV charging stations in Lanzhou City using multi-objective optimization and ranking methods. The results are of great significance for improving the operational efficiency and convenience of charging station location optimization and offer valuable insights for other cities in northwestern China in planning their charging infrastructure. Full article

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22 pages, 4522 KB

Open AccessArticle

Mobilities in the Heat: Identifying Travel-Related Urban Heat Exposure and Its Built Environment Drivers Using Remote Sensing and Mobility Data in Chengdu, China

by Yue Zhang, Xiaojiang Xia, Yang Zhang and Ling Jian

ISPRS Int. J. Geo-Inf. 2025, 14(10), 372; https://doi.org/10.3390/ijgi14100372 - 24 Sep 2025

Abstract

Urban heat exposure, which intensifies with climate change, poses serious threats to public health in rapidly growing cities. Traditional assessments rely on static land surface temperature, often overlooking the role of human mobility in exposure frequency. This study introduces a travel-related heat exposure [...] Read more.

Urban heat exposure, which intensifies with climate change, poses serious threats to public health in rapidly growing cities. Traditional assessments rely on static land surface temperature, often overlooking the role of human mobility in exposure frequency. This study introduces a travel-related heat exposure index (THEI) that combines ride-hailing trajectories and remote sensing data to capture dynamic human–environment thermal interactions. Using Chengdu, China, as a case study, the THEI is combined with local indicators of spatial association to outline high-exposure risk zones (HERZ). XGBoost with SHAP and partial dependence plot (PDP) methods is also applied to identify the nonlinear effects of built environment factors. Results showed the following: (1) distinct spatial clustering of high travel-related heat exposure in central urban districts and transit hubs; (2) city-wide exposure is primarily driven by transportation accessibility and urban form, such as intersection density and floor area ratio; (3) in contrast, HERZ are more strongly associated with demographic and socioeconomic factors, including population density, housing price and road density; and (4) vegetation, measured by the normalized difference vegetation index, demonstrates a consistent negative effect across scales, highlighting its critical role in mitigating thermal risks. These findings emphasize the necessity of incorporating mobility-based exposure metrics and spatial heterogeneity into climate-resilient urban planning, with differentiated strategies tailored for city-wide versus high-risk zones. Full article

(This article belongs to the Topic Spatial Decision Support Systems for Urban Sustainability)

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35 pages, 7791 KB

Open AccessArticle

Data-Driven Spatial Optimization of Elderly Care Facilities: A Study on Nonlinear Threshold Effects Based on XGBoost and SHAP—A Case Study of Xi’an, China

by Linggui Liu, Han Lyu, Jinghua Dai, Yuheng Tu and Taotao Gao

ISPRS Int. J. Geo-Inf. 2025, 14(10), 371; https://doi.org/10.3390/ijgi14100371 - 24 Sep 2025

Abstract

Under the accelerating demographic aging trend, the rational allocation of elderly care facilities has emerged as a critical challenge. Although existing studies have investigated elderly care facilities planning using conventional methods, they frequently overlook the nonlinear interactions between built environment factors and heterogeneous [...] Read more.

Under the accelerating demographic aging trend, the rational allocation of elderly care facilities has emerged as a critical challenge. Although existing studies have investigated elderly care facilities planning using conventional methods, they frequently overlook the nonlinear interactions between built environment factors and heterogeneous demands across different elderly care facility types. This study addresses these gaps by proposing a data-driven framework that integrates machine learning with spatial analysis to optimize elderly care facility distribution in Xi’an City central area, Shaanxi Province, China. Leveraging multi-source datasets encompassing points of interest (POIs), road networks, and demographic statistics, we classify facilities into three categories (service-oriented, activity-oriented, and care-oriented) and employ an XGBoost model with SHAP interpretability to evaluate spatial distributions and influencing factors. The results demonstrate that the XGBoost model outperforms comparative algorithms (Random Forest, CatBoost, LightGBM) with superior performance metrics (accuracy rate of 97%, precision of 95%, and F1-score of 90%), effectively capturing nonlinear thresholds effects. Key findings reveal the following: (1) Accessibility and road density exert threshold effects on care-oriented facilities, with facility attractiveness saturating when these values exceed 6; (2) Land use intensity and medical resources positively correlate with activity-oriented facilities, while excessive retail density inhibits their distribution; (3) Service-oriented facilities thrive in areas with balanced accessibility and moderate commercial diversity. Spatial analysis identifies clustered distribution patterns in urban core areas contrasted with peripheral deficiencies, indicating need for targeted interventions. This research contributes a scalable methodology for equitable facility planning, emphasizing the integration of dynamic built environment variations with model interpretability. The framework provides significant implications for formulating age-friendly urban policies applicable to global cities undergoing rapid urbanization and population aging. Full article

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27 pages, 22665 KB

Open AccessArticle

Assessing Spatial Accessibility Uncertainty with Dempster–Shafer Theory: A Comparison of Potential and Revealed Accessibility

by Roya Esmaeili Tajabadi, Parham Pahlavani, Amin Hosseinpoor Milaghardan and Christophe Claramunt

ISPRS Int. J. Geo-Inf. 2025, 14(10), 370; https://doi.org/10.3390/ijgi14100370 - 23 Sep 2025

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Abstract

This study introduces a framework for comparing and integrating revealed and potential accessibility maps, using the Dempster–Shafer theory to identify regions with varying spatial accessibility while accounting for uncertainty. It presents a method for determining revealed accessibility from individuals’ trajectory data, weighting accessibility [...] Read more.

This study introduces a framework for comparing and integrating revealed and potential accessibility maps, using the Dempster–Shafer theory to identify regions with varying spatial accessibility while accounting for uncertainty. It presents a method for determining revealed accessibility from individuals’ trajectory data, weighting accessibility inversely to the square of uncertainty. This dual approach aids urban planners in making more reliable decisions. The methodology is applied to supply centers, including shops, restaurants, and sports centers, using data from the Mobile Data Challenge (MDC) in Vaud, Switzerland. The results show good access to shops in the northwestern and southeastern regions and good access to restaurants in the eastern regions. The final maps indicate that areas with low access to sports centers form the highest proportion (62.7%) of regions with low access, while those with low access to shopping centers form the lowest (9.3%). The findings suggest the need for more sports centers in Nyon and Jura-Nord Vaudois and more accessible restaurants in Nyon and southern Aigle. Additionally, the analysis reveals that lower station densities correlate with smaller discrepancies between real and expected accessibilities, while higher population densities are linked to lower uncertainty, underscoring the importance of considering density in spatial accessibility assessments. Full article

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28 pages, 6780 KB

Open AccessArticle

Development of an Ontology-Based Framework to Enhance Geospatial Data Discovery and Selection in Geoportals for Natural-Hazard Early Warning Systems

by Amirhossein Vahdat, Thierry Badard and Jacynthe Pouliot

ISPRS Int. J. Geo-Inf. 2025, 14(10), 369; https://doi.org/10.3390/ijgi14100369 - 23 Sep 2025

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Abstract

Discovering and selecting relevant geospatial datasets from heterogeneous sources remains difficult in conventional geoportals, where keyword-based search often fails to capture thematic relationships or user intent. This article presents an ontology-based framework that augments geoportals with semantic-aware discovery and selection. The contributions are [...] Read more.

Discovering and selecting relevant geospatial datasets from heterogeneous sources remains difficult in conventional geoportals, where keyword-based search often fails to capture thematic relationships or user intent. This article presents an ontology-based framework that augments geoportals with semantic-aware discovery and selection. The contributions are as follows: (1) the geospatial metadata ontology (GMO), which reuses W3C and OGC ontologies and aligns with ISO 19115 to provide a uniform metadata representation enriched with thematic hierarchies and relations; and (2) GeoFit, a discovery framework that integrates GMO into geoportal workflows. The framework extends conventional functionality by enabling semantic query expansion, faceted exploration of thematic hierarchies, and ranking of datasets according to conceptual proximity and fitness-for-use criteria. These capabilities demonstrate how ontology integration operationalizes domain knowledge in the discovery process and makes dataset selection more interpretable and targeted. Validation demonstrated feasibility in the context of natural hazard Early Warning Systems (EWSs), where the prototype surfaced datasets relevant to different components, organized them into ranked and navigable results, and illustrated portability of the method to applied settings. The study confirms that embedding an ontology layer into geoportals provides semantic capabilities absent from keyword-only interfaces and establishes a foundation for extending discovery functions in heterogeneous geospatial infrastructures. 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, 36459 KB

Open AccessArticle

Third Spaces to Represent Urban Greenery: A Study of Informal Green Spaces in a High-Density City Using Deep Learning and Geo-Weighted Analysis

by Xiaoya Hou, Yu Tian and Mingze Chen

ISPRS Int. J. Geo-Inf. 2025, 14(10), 368; https://doi.org/10.3390/ijgi14100368 - 23 Sep 2025

Viewed by 174

Abstract

In high-density cities like Hong Kong, green spaces are often characterized by fragmentation and uneven spatial distribution, which negatively impacts their accessibility and equity. To address this issue, studies have proposed the use of informal green spaces (IGSs) as a supplementary component to [...] Read more.

In high-density cities like Hong Kong, green spaces are often characterized by fragmentation and uneven spatial distribution, which negatively impacts their accessibility and equity. To address this issue, studies have proposed the use of informal green spaces (IGSs) as a supplementary component to formal urban green spaces (UGSs). However, the spatial delineation and quantitative analysis of IGSs remain challenging due to the lack of standardized identification and evaluation methods. Building upon the work of urban theorists Henry Lefebvre and Edward Soja, this study explores informal green spaces as third spaces. This study employed remote sensing and GIS technologies to systematically assess the spatial distribution and benefits of IGSs, categorizing them into four types: Urban Interstitial IGSs, Transitional IGSs, Fringe IGSs, and Riparian IGSs. Subsequently, an evaluation framework was constructed across ecological, social, and economic dimensions to quantify the overall value of IGSs. The results reveal that IGS significantly contributes to ecological regulation, social interaction, and economic potential, particularly in urban areas with limited green resources. This demonstrates that IGSs can serve as a vital complement to formal urban green spaces, playing a key role in alleviating green space inequity, enhancing urban livability, and promoting sustainability. Furthermore, this study provides a scientific foundation for precise identification, benefit assessment, and optimized management of IGSs, supporting effective integration and rational utilization in future urban planning. Full article

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

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25 pages, 11023 KB

Open AccessArticle

Spatio-Temporal Mapping of Violence Against Women: An Urban Geographic Analysis Based on 911 Emergency Reports in Monterrey

by Onel Pérez-Fernández, Octavio Quintero Ávila, Carolina Barros and Gregorio Rosario Michel

ISPRS Int. J. Geo-Inf. 2025, 14(10), 367; https://doi.org/10.3390/ijgi14100367 - 23 Sep 2025

Viewed by 196

Abstract

In Latin American cities, violence against women (VAW) remains critical for public health, well-being, and safety. This phenomenon is influenced by social, political, and environmental drivers. VAW is not randomly distributed; built environments—geography, ambient population, and street networks—influence criminal through spatial dependence across [...] Read more.

In Latin American cities, violence against women (VAW) remains critical for public health, well-being, and safety. This phenomenon is influenced by social, political, and environmental drivers. VAW is not randomly distributed; built environments—geography, ambient population, and street networks—influence criminal through spatial dependence across multiple scales. Despite growing interest in the spatial distribution of crime, few studies have explicitly explored the spatiotemporal dimensions of VAW in Monterrey. This study explores spatio-temporal patterns of VAW in Monterrey, Mexico, based on the analysis of 27,036 georeferenced and verified emergency reports from the 911 system (2019–2022). The study applies kernel density estimation (KDE), the Getis–Ord G_i* statistics, the Local Moran I index, and space–time cube analysis to identify spatial and temporal clusters of VAW. The results show concentrations of incidents during nighttime and weekends, particularly in northern and eastern sectors in Monterrey. The analysis reveals clusters in areas of high socioeconomic vulnerability. VAW in Monterrey follows predictable and cyclical patterns. These insights contribute to the design of tailored public policies and actions to improve women’s health, well-being, and safety in critical zones and timeframes. The findings also enhance international understanding of gender-based spatial violence patterns in the rapidly urbanizing contexts of the Global South. Full article

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

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ISPRS Int. J. Geo-Inf., Volume 14, Issue 10 (October 2025) – 12 articles

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