Although fire service accessibility has been extensively studied for urban public facilities such as hospitals and parks, it remains largely unexplored for high-risk industrial facilities such as fireworks manufacturers. In this study, fire risk and transportation network conditions are jointly considered, and a dynamic risk-weighted fire service accessibility evaluation framework is developed. An empirical study was conducted using 35 fire stations and 410 fireworks manufacturers in Liuyang City, China. Fire truck travel times were simulated via online map application programming interfaces (APIs) at 10–30 min intervals over a continuous three-day period, generating 193 evaluation scenarios and 158,260 data samples. The results indicate that the total average travel time of different types of fire trucks to demand points with varying risk levels was found to range from 570.33 to 1362.18 s, and risk-weighted fire service accessibility ranges from 16.84 to 57.26, reflecting a generally poor level of fire service accessibility within the current transport network. To resolve these constraints, it is recommended to optimize the spatial configuration of micro fire stations, promote the collaborative planning of shared enterprise-based fire units, and cross-integrate accessibility metrics into regional industrial land-use planning. These spatial measures are expected to significantly enhance the spatial resilience of transportation systems serving peripheral high-risk industrial clusters.
Planning emergency assembly areas, which are vital in the first hours after a disaster, is a critical decision problem, especially in metropolitan areas with high population density and urbanization pressure. This study comprehensively evaluates the capacity adequacy, population pressure, and spatial imbalances of existing assembly areas in the Eyüpsultan district of Istanbul. A two-stage hybrid model is proposed: in the first stage, assembly areas are clustered based on geographical proximity using the K-means algorithm; in the second stage, an improvement is implemented for clusters falling below the per capita area target (3 m2/person), based on the reallocation of parks from neighboring clusters with excess capacity through administrative assignment. As a result of trials with different cluster numbers, the five-cluster configuration was selected as the preferred solution when the Silhouette score (0.568), minimum per capita area (3.21 m2), and spatial continuity criteria were evaluated together. Thanks to the proposed hybrid model, the per capita space in all clusters has been kept above the 3 m2 threshold. These findings demonstrate that capacity gaps can be addressed solely through the rational optimization of service assignments, without increasing the urban open space stock. Consequently, the proposed approach offers a more equitable, accessible, capacity-balanced decision-support structure for disaster management for local governments.
Three-dimensional urban morphology has gained renewed analytical relevance as advances in remote sensing, photogrammetry, and geospatial data processing have made it possible to derive 3D data and high-resolution digital surface and elevation models. Block-scale morphological analysis remains predominantly two-dimensional, leaving volumetric scale, vertical heterogeneity, and spatial aperture undetectable, while existing 3D approaches treat building metrics as isolated variables or rely on costly semantic city models. This study proposes a parsimonious geometric vector framework in which each urban block is represented as a vector of three conceptually distinct dimensions: Mean Building Volume (MBV, volumetric scale), Height Variation Coefficient (HVC, vertical heterogeneity), and Mean Sky View Factor (SVF, spatial aperture). The framework was operationalized through reproducible GIS and Python 3.12 workflows using a large-scale topographic dataset and a LiDAR-derived Digital Surface Model, and it was tested through K-means clustering of 816 statistical blocks in the municipality of Amadora, Portugal. The results confirm the near-orthogonality of the three dimensions (correlations |r| < 0.25; Variance Inflation Factors ≤ 1.08) and identify five structurally distinct typologies ordered along a volumetric gradient (MBV), in which HVC is the principal differentiating dimension among Types 1–4, and SVF distinguishes Type 5; the partition remains stable under 500-iteration consensus clustering (Adjusted Rand Index = 0.9445). The contribution of this study lies in the transferability of the analytical pipeline rather than of the typology itself, which is specific to Amadora and would require independent derivation elsewhere. The framework provides urban planners with a parsimonious, geometrically transparent, and operationally feasible tool for classifying urban blocks in 3D, complementing 2D analytical approaches and establishing a morphological basis for future climate-responsive analyses.
GeoAI has maintained strong momentum since the conclusion of the 2024 Special Issue on “Advances in AI-Driven Geospatial Analytics and Data Generation” [...]
Coastal zones provide favorable geographical conditions for aquaculture. Accurate information on land and marine aquaculture (LMA) activities is essential for managing their spatial distribution and estimating blue-food production. However, complex coastal landscapes and the heterogeneity of aquaculture facilities make it difficult to extract LMA areas accurately and completely at large scales. To address this challenge, we developed an adaptive multi-level scale segmentation (AMLSS) method for the precise extraction of LMA areas and modified a radar dual-polarization water index to reduce interference from the seawater background. We then constructed a class-specific hierarchical decision-tree model using multi-source features to map LMA areas at the national scale. AMLSS increased extraction accuracy by 19.7% for pond aquaculture and 22.9% for marine aquaculture compared with single-scale segmentation and was particularly effective in northern China. In 2022, landward pond and marine cage/raft aquaculture within China’s coastal zone covered approximately 7237 km2 and 1152 km2, respectively; the resulting map achieved an overall accuracy of 90.8%. This study provides a methodological basis for the long-term monitoring and spatially explicit mapping of LMA areas.
The integration of geo-spatial databases, advanced modeling, and artificial intelligence provides novel opportunities to investigate regional differentiation with direct implications for urban science and regional development. To avoid homogeneous competition and support regional tourism planning, it is important to examine how tourists perceive different tourism elements across large regional spaces. However, existing studies still lack a systematic workflow for extracting destination-specific perceptual characteristics and linking them to specific tourism locations. This study develops a UGC-based framework to identify tourism elements and perceptual characteristics, construct POI-level perceptual weights, and map the spatial patterns of tourism perception across Northeast China using 196,028 Ctrip reviews from 1491 POIs. The framework integrates jieba segmentation with BERT ranking for keyword extraction, ChatGPT-assisted filtering and manual review for keyword standardization, K-means clustering for perceptual dimension identification, and GIS-based kernel density analysis for spatial visualization. The main findings are as follows. First, tourism perception in Northeast China presented a diversified composite structure. Second, different perceptual dimensions and representative keywords showed differentiated spatial distributions. Third, the spatial structure of tourism perception in Northeast China was summarized as a composite spatial pattern centered on the Five-Dimensional Composite Perception Core Belt, supported by the Natural-Landscape Perception Belt and the Water Recreation Perception Area, and supplemented by multi-level urban nodes and inter-node perceptual linkages. This study provides methodological support and planning implications for understanding regional tourism differentiation and guiding evidence-based destination planning. The construction of geo-spatial databases plays a fundamental role in linking tourists’ perceptions to specific tourism elements and locations, enabling fine-grained and spatially explicit analysis that is difficult to achieve using conventional survey-based data.
The rapid advancement of digital twin technologies has increased the importance of establishing interoperable geospatial information standards for national-scale Digital Twin implementation. The Republic of Korea has developed the National Digital Twin (NDT) standard framework based primarily on OGC CityGML 3.0. This study presents a case-study-based analysis of the development and application of the Korean NDT standards, focusing on how a geospatial standard framework can be structured and extended to support a national-scale Digital Twin. A comparative review of international initiatives indicates that existing approaches generally develop CityGML-based 3D city models as geospatial foundations and subsequently extend them toward Digital Twin applications, whereas the Korean NDT establishes a common reference framework and geospatial data models as a standardized foundation for national-scale Digital Twin implementation. The analysis examines the phased development process, the hierarchical relationship among the reference framework, core data model, and application data models, and the organization of multiple spatial domains, including Building, Transportation, Terrain, Indoor Space, and Underground. The applicability of the NDT standards was further examined through pilot dataset implementation and validation from schema, semantic, geometric, and topological perspectives. The analysis also identifies current limitations, including restricted data openness, limited software support for CityGML 3.0, and the need for further mechanisms to integrate dynamic information. Based on these findings, this study discusses future directions for NDT standardization, including model management, standardized data production and validation, dynamic information integration, and technical implementation guidance. The Korean experience provides a case for understanding both the potential and practical challenges of applying CityGML 3.0-based geospatial standards to national-scale Digital Twin initiatives.
Urban resilience (UR) is shaped by multiple interacting factors, yet existing studies have paid limited attention to the differentiated nonlinear and interactive mechanisms across different resilience dimensions. Based on panel data from 110 prefecture-level and above cities in the Yangtze River Economic Belt (YREB) from 2015 to 2024, this study adopts a four-dimensional framework covering social, economic, ecological, and infrastructure resilience. Spatial analysis and the XGBoost-SHAP model are combined to examine the spatiotemporal evolution, key drivers, nonlinear responses, turning point characteristics, and factor interactions of overall UR and its four dimensions. The results show that overall UR increased steadily, while the four dimensions followed distinct evolutionary trajectories: economic and infrastructure resilience improved most rapidly, ecological resilience increased steadily, and social resilience improved relatively slowly. The importance and effects of key drivers also varied across overall resilience and its four dimensions. Major factors, including technological investment, non-registered population, unemployment insurance coverage, land use, and natural population growth, exhibited distinct nonlinear responses and turning point characteristics, with their effects changing across different variable levels. Furthermore, significant interactions were identified among population and industrial structure, population and transportation, industry and technology, and industry and social security across different resilience dimensions. Overall, UR in the YREB is characterized by differentiated development, nonlinear responses, nonlinear transition patterns, and joint influences of multiple factors.
Over 200 million of the world’s 273 million out-of-school children live in Central and Southern Asia or Sub-Saharan Africa, where distance to school is a persistent barrier, and walking remains the dominant travel mode. Tanzania presents a critical case: over 300,000 children dropped out before completing primary school in 2023, and only 8.5% complete upper-secondary education. We estimated walking travel time to the nearest primary and secondary school using a 100-metre resolution cost-surface that incorporated land cover, topography, and road networks. Travel time surfaces were linked to Demographic and Health Survey (DHS) data and analysed using multilevel Poisson regression for primary school-age children (aged 7–13) and single-level models for secondary school-age adolescents (aged 14–19). Among primary school-age children (n = 9213), poor geographic access (walking time > 45 min) was associated with a 27.9% higher prevalence of non-participation after socioeconomic adjustment, consistent across wealth quintiles and maternal education groups. Among secondary school-age adolescents (n = 2585), poor access was not significant. Household wealth and maternal education showed strong protective relationships at both levels. Each additional 10 min of walking travel time was associated with a 2.9% higher prevalence of non-participation, equivalent to approximately 18% per additional hour. Geographic access is an independent barrier to primary school-age participation irrespective of socioeconomic position, as there was approximately a 2.9% higher prevalence of non-participation in school for every additional 10 min of travel time, supporting geographically targeted infrastructure investment at primary level and demand-side interventions at secondary level.
University geospatial hubs support research dissemination, teaching, public data access, and community engagement, but their value depends on whether resources can be found, accessed, interpreted, integrated, and reused. This study evaluates 747 publicly listed items in North Carolina Central University’s CoDE Open Data Hub using FAIR principles as an interpretive framework and the Federal Geographic Data Committee Content Standard for Digital Geospatial Metadata (FGDC CSDGM) as the domain-specific measurement basis. Nineteen criteria were scored on explicit criterion-specific 0–2 rules using live ArcGIS item properties, formal metadata XML, and available layer properties. Discoverability was evaluated for a proportionally stratified sample of 153 items through exact-title, keyword, location, item-type, category-browsing, and click-depth tests. The equal-criterion benchmark averaged 28.57 of 38 (75.19%); equal FAIR-dimension weighting produced a mean of 73.26%, and 95.85% of items retained the same descriptive performance band. Independent rescoring of 50 stratified items produced 76.74% exact criterion-level agreement, a linear weighted Cohen’s kappa of 0.587, and an absolute-agreement ICC of 0.715 for total scores. FAIR-aligned scores were highest for Findability (87.25%) and Accessibility (85.96%), followed by Reusability (67.26%) and Interoperability (52.59%). Exact-title and item-type searches retrieved all sampled items, while keyword search was successful for 88.24%. The results distinguish public availability from technical reuse readiness and identify spatial reference, attribute definitions, lineage, limitations, category structure, and navigation depth as priorities. The performance bands are study-specific descriptive summaries rather than FGDC compliance levels or FAIR certification. The study provides an evidence-preserving, type-aware method for applying FAIR principles alongside geospatial metadata standards in heterogeneous ArcGIS Hub catalogs.
Improving the precise perception of disaster risk and evaluating the effectiveness of warning systems are essential for natural-hazard prevention, mitigation, and spatial risk management. Using Sichuan Province, China, as a case study, this study treats government-issued disaster warning texts within 72 h before accident occurrence as formal risk signals and spatiotemporal information sources. Drawing on the protective action decision model and risk communication theory, we propose an integrated framework combining BERT-CRF-based information extraction, spatiotemporal analysis, and a case-crossover design to evaluate a multi-level warning system through the chain of risk identification, risk communication, protective action, and risk outcome. The results show a clear gradient across warning levels. High-level warnings are significantly associated with lower accident risk, whereas lower-level warnings can identify risky contexts but have limited intervention effects. These findings indicate that disaster warning systems have dual functions of risk identification and intervention, while medium-level warnings may reveal a weak link in the transition from risk perception to protective action. The study provides a geospatial analytical framework for linking official warning information with tourism safety outcomes and offers empirical support for optimizing warning systems and strengthening hazard risk governance in high-risk regions.
Recent learning-based damage assessment methods using remote sensing data have greatly contributed to the rapid identification and classification of damaged buildings from pre- and post-disaster imagery. However, most outputs are provided in the form of final damage labels or scores. To address this limitation, this study proposes an evidence-aware data model for explainable post-disaster building damage assessment. The proposed model is derived from a component-level roof-damage assessment workflow using optical imagery and post-disaster surface-height data, and is designed to describe the observational evidence, intermediate indices, and decision rules leading to a specific damage grade, as well as its interpretation as a post-disaster change event. Based on the proposed logical data model, a hybrid spatial–graph database was implemented to support both spatial retrieval and the semantic explanation of damage assessment results. The proposed model was evaluated through six competency questions derived from different damage assessment requirements. The results confirmed that the proposed model can support component-level retrieval of damage results, tracing of damage evidence and assessment rules, spatial visualization of damaged building components, and integrated spatial–semantic explanation for individual objects. The results provide a foundation for managing disaster damage assessment processes and results as explainable and reusable structured data.
Electric vehicles (EVs) are attracting choice and adoption as a travel mode in global transportation systems, driven by technological innovation, economic considerations, and advancing sustainable urban development. The planning, development, design, and location of EV charging stations are challenging tasks for underdeveloped countries such as Pakistan. To propose the site location for an EV charging station, this study comprises Multi-Criteria Decision Analysis and Geographic Information Systems. The study adopts a mixed-methods design, combining qualitative expert input with quantitative spatial analysis to investigate technical, social, and infrastructural criteria. The study considers indicators, including site suitability, population density, interaction between land use and transport, road network, transportation interactions, existing electricity grid infrastructure, and existing fueling and charging stations. This research presents pairwise expert comparisons, indicating that grid capacity (0.28) and demand (0.30) are important factors to consider. A composite suitability Index (CSI) through GIS-weighted overlay was used to classify the area into low, medium, and high suitability zones. According to the CSI, 26% of the area falls in highly suitable areas for EV infrastructure in Islamabad, and 41% falls in medium-to-high suitable areas. The remaining 33% area lies in low-suitability peripheral zones near the hilly region of Islamabad city, which is not considered suitable for EV infrastructure. A total of 67% of the city’s area is suitable for EV infrastructure.
Under increasing cropland constraints and pressure to secure oilseed supplies, using winter–fallow cropland for winter rapeseed production can improve annual cropland-use efficiency. Taking the Wanjiang Plain, China, as the study area, this study integrated 10 m winter–fallow cropland maps (2019–2024), 30 m winter rapeseed maps (2000–2022), annual Satellite Embedding features, cropland data, and administrative boundaries. Multi-year occurrence frequency and Getis–Ord Gi* statistics characterized temporal persistence and spatial clustering. A MaxEnt model calibrated with 394 occurrence records from long-term high-frequency rapeseed areas and 26 screened embedding dimensions delineated cropland with present-day land-surface characteristics similar to historically persistent rapeseed locations. This layer was progressively intersected with the historical winter–fallow union and stable winter–fallow cropland. Stable winter–fallow cropland covered approximately 4437 km2, whereas long-term high-frequency winter rapeseed covered only 568 km2, revealing a marked spatial mismatch. Under five-fold spatial cross-validation, the selected linear-feature model with a regularization multiplier of 4 achieved a mean test AUC of 0.904 ± 0.015 and a 10% training-omission rate of 0.108 ± 0.022. Using the model-specific threshold of 0.3096, the final estimates were 6017 km2 of potentially suitable cropland, 2943 km2 of general expansion potential, and 1137 km2 of candidate spatial priority areas for field verification. The last tier was concentrated mainly in Xuanzhou District, Lujiang County, urban Wuhu, Wuwei City, Nanling County, and He County. The framework provides a cautious spatial-screening basis for optimizing winter–fallow cropland use and guiding subsequent field and feasibility assessments.
In response to the escalating challenges of global climate change and extreme meteorological events, this study addresses the critical issue of low disaster resistance in socialist-built heritage (SBH, i.e., built cultural heritage constructed during China’s socialist construction stage from 1949 to 1978). Focusing on Henan Province, China, with meteorological observation datasets spanning 1990–2023 and 1362 SBH sites constructed from 1949 to 1978, we innovatively construct a four-dimensional risk assessment model integrating Meteorological–Environmental Hazard (H), Exposure (E), Vulnerability (V), and Ecological Resilience (R). By synthesizing Spearman correlation analysis, Variance Inflation Factor (VIF) diagnostics, and piecewise regression, this research elucidates the spatial differentiation and driving mechanisms of meteorology-induced compound disaster risks. Key findings identify Zhengzhou and Kaifeng as core high-risk zones and precisely quantify a critical hazard threshold of 0.4 with a 95% breakpoint confidence interval [0.372, 0.428], passing the Chow test p < 0.001 and validated via historical heritage damage records (R2 = 0.78). Variable-importance analysis proves that extreme precipitation (32.7%) and annual strong-wind days (28.1%) are the dominant meteorological–environmental drivers, alongside the significant buffering effect of ecological resilience. Ultimately, this study refines the disaster risk assessment framework for SBH, providing robust theoretical support and a decision-making basis for adaptive conservation strategies in similar regions. The average disaster risk of SBH in Henan is 0.421 ± 0.153; for each 0.1 growth of H-E-V coupling term above the threshold of 0.4, disaster risk rises by 37.2%.
The management and consultation of heterogeneous spectral datasets present significant challenges in multidisciplinary research, where records from different campaigns, instruments, and scientific domains must remain linked to consistent metadata, geographic provenance, and analytical tools within a unified framework. This paper presents the design, implementation, and operational use of the INGV Spectral Library, a web-based spatial data infrastructure integrating structured metadata management, georeferenced Web-GIS consultation, and browser-native spectral preprocessing within a single environment. The system relies on a three-tier architecture and organizes spectral records through a domain-aware metadata model associating each entry with geographic, thematic, and domain-specific descriptors. Consultation is supported through two complementary access modes: an interactive map enabling spatial exploration and direct map-to-record navigation, and a metadata-driven filtered table supporting progressive retrieval across domains including geology, mineralogy, environmental surveys, and cultural heritage. Integrated preprocessing tools—first derivative, continuum removal, and sensor spectral response function-based resampling—operate directly within the environment without export to external software. A controlled ingest and harmonization workflow ensures metadata consistency and spectral validity prior to record exposure. The paper discusses the platform’s geoinformation design principles, spatial consultation model, and current limitations, contributing a practical example of georeferenced spatial data management applied to multidisciplinary spectral archives.
Extracting dominant points from complex building outlines is an important task in cartography and building footprint simplification. This study proposes a semi-supervised, geometric-aware graph-Transformer framework for automatic dominant point selection from vector point sequences. The framework combines geometric rules, topological constraints, and data-driven learning to facilitate dominant point detection. A graph network is used to capture local neighborhood structures, while a Transformer models global morphology and long-range dependencies. To improve detection stability, Gaussian scale space is introduced to analyze contours across multiple smoothing scales, and multi-scale geometric features are extracted to distinguish structural changes from local noise. Experimental results demonstrate that the proposed method achieves the best overall performance in building footprint simplification. Indicates that the proposed method provides more accurate, robust, and geometrically stable dominant points for subsequent building reconstruction.
Ecotourism, as one of the most important forms of sustainable tourism, plays a significant role in the conservation of natural resources, the economic development of local communities, and the achievement of sustainable development goals. However, the sustainable development of this sector requires the accurate identification of suitable areas and the simultaneous assessment of the ecological capacities and limitations arising from anthropogenic activities, an issue that has received less attention at the national scale, particularly in countries with high environmental diversity such as Iran. Therefore, the present study was conducted with the aim of assessing the potential for ecotourism development in Iran based on Geographic Information System (GIS) and Spatial Multi-Criteria Decision Analysis (SMCDA). In line with the scope of the sub-factors evaluated, the assessed construct is referred to throughout this study as Environmental–Anthropogenic Ecotourism Suitability (EAES), reflecting an environmental-quality and anthropogenic-pressure perspective rather than a comprehensive assessment of ecotourism sustainability. The main innovation of this study lies in the simultaneous integration of natural and anthropogenic factors at the national scale and the sensitivity assessment of the results through the design of different development scenarios. In this study, a set of natural sub-factors including protected areas, vegetation cover, slope, precipitation, land use, and natural attraction density, as well as anthropogenic sub-factors including settlements, roads, accommodations, mines, industrial parks, dams, power transmission lines, dust, and the Global Human Modification (GHM) index were used. The weighting of the sub-factors was carried out using the Best–Worst Method (BWM), and spatial suitability maps were subsequently generated through the Weighted Linear Combination (WLC) method in the GIS environment. To evaluate uncertainty and examine the effect of the relative importance of sub-factors, three scenarios including natural factor dominance, anthropogenic factor dominance, and a balanced scenario were designed and analyzed. The results showed that protected areas (0.19), vegetation cover (0.17), and natural attraction density (0.16) were the most important natural sub-factors, while the GHM index (0.16), roads (0.15), and settlements (0.14) were the most important anthropogenic sub-factors affecting ecotourism development. The spatial pattern of the results indicated the concentration of areas with good potential in the Alborz and Zagros mountain ranges, the Hyrcanian forests, and parts of the protected areas of Iran. In the balanced scenario, approximately 24.8% of Iran’s area was classified within the suitable and highly suitable classes, while 46.3% was classified within the moderately suitable class. Furthermore, comparison of the scenarios showed that the use of one-dimensional approaches may lead to unrealistic estimates of ecotourism capacity. Overall, the results of this study indicate that the sustainable development of ecotourism in Iran requires the adoption of an integrated approach in which the conservation of natural assets and the management of anthropogenic interventions are simultaneously considered. The proposed framework can serve as a decision-support tool for spatial planning, investment prioritization, and sustainable ecotourism development policymaking in Iran and other similar regions.
Route-choice research has treated route diversity mainly as an individual behavioral property, but population-scale route signatures make it possible to examine diversity as an attribute of origin–destination (OD) flows. Using September 2025 mobile phone signaling data from the DaaS BI platform (Zhihuizuji, China Unicom), we analyze map-matched route signatures for 1.61 million stable commuters in Shenzhen, China. The primary outcome is the route diversity index (RDI), computed as the ratio of distinct route signatures to total route observations within an analytical unit. OD flows whose endpoints occupy the same geohash-7 cell are more diverse than flows whose endpoints occupy different cells (RDI = 0.83 vs. 0.49; Cohen’s d = 1.61), and route diversity follows a nonlinear distance profile, with the lowest values in the 5–10 km range. The full OD-level model achieves high in-sample fit, while a model without the endpoint-grid indicator has nearly the same fit. The near-equivalent fits are consistent with shared information among flow scale, distance, and OD structure rather than being unique contributions from the endpoint-grid indicator. Social gradients are present but smaller than the OD contrast: the same-cell vs. different-cell gap is 1.6 times the full age-gradient range and 5.4 times the ARPU range. The local road-node density measure adds little independent information after OD-level controls. The largest observed differences occur at the OD-pair level. All the results refer to DaaS-derived, map-matched route signatures, not to direct observations of every trip.
