Search Results (125)

Land resources constitute the fundamental basis for human survival and a core element of social development. The quantity, quality, and ecological condition of land resources are crucial for human well-being and sustainable development, and they make significant contributions to achieving the United Nations Sustainable Development Goals (SDGs). However, the influence of land ecological quality on the implementation of the SDGs has not yet been fully clarified. This study utilizes 1 km spatial resolution geospatial data and statistical data to construct a land ecological environment quality evaluation index system based on the Pressure–State–Response (PSR) model, analyzing the spatiotemporal dynamics of land ecological environment quality in China from 2010 to 2020 (with five-year intervals). In addition, the Spearman correlation coefficient was employed to examine the relationships between the land ecological environment quality index (LEEQI), pressure index (PI), state index (SI), response index (RI), and the implementation of SDGs 6, 11, 12, and 15, and to further explore how geographical economic zones influence the effects of these indices on the achievement of the SDGs. The results indicate that land ecological quality in China shows a strong north–south gradient, while the east–west differentiation is relatively weak, and the overall trend is increasing. The LEEQI values ranged from 0.16 to 0.48; the PI values ranged from 0.00 to 0.24; the SI values ranged from 0.03 to 0.29; and the RI values ranged from 0.01 to 0.26. The LEEQI gap between the western and northeastern regions narrowed significantly, from 0.10 to 0.07. LEEQI and RI promote the achievement of all four SDGs, whereas PI and SI mainly promote the realization of SDGs 6, 11, and 12. The synergistic effects of the four indices on the SDGs are observed in the central, eastern, and western regions, with the most significant effects occurring in western China. Specifically, LEEQI shows the strongest correlation with SDG 6; both PI and SI exhibit synergistic effects with SDGs 12 and 15; and RI demonstrates synergistic effects with all four SDGs. These findings suggest that improving land ecological quality is crucial for advancing the achievement of the SDGs. Furthermore, given that land ecological environment quality and its dimensions exert different influences on the implementation of the SDGs across geographical economic regions, it is necessary to develop tailored and region-specific strategies, particularly in western China, where maximizing improvements in land ecological quality is crucial for promoting sustainable development. Full article

This study presents the first integrated quantification of mud diapirism susceptibility in the Membrillal sector of Cartagena de Indias, Colombia, through a multidisciplinary approach combining geospatial, geotechnical, hydrogeochemical, and socio-structural analyses. Using GIS-based multicriteria modeling, household surveys (n = 240), and temporal satellite imagery from 2013 to 2024, the research identifies spatial and temporal dynamics of active mud volcano reactivation. Field sampling of vent waters and gases followed ISO/IEC 17025 and APHA–AWWA–WEF standards, revealing high-salinity fluids (TDS = 13,220 mg/L; EC = 20.4 mS/cm; pH = 8.0) with elevated chloride (6996 mg/L) and low sulfate (1.67 mg/L) under reducing conditions, though a significant charge-balance discrepancy (Na⁺ = 8 mg/L) indicates either sample dilution during the collection or presence of unmeasured cationic species, and low free-gas flux constrained by high-density brine sealing. Principal component analysis of 240 georeferenced dwelling surveys yielded dimension-specific reliability (α = 0.68–0.76) and strong spatial correlation (Spearman ρ = 0.61–0.87) between vent proximity and structural damage—46.9% of dwellings exhibited visible cracking, with 27.2% severe (width > 1.5 mm). Satellite differencing documented 233% increase in active vents (3→10) and 35% vegetation reduction correlated with informal settlement expansion into moderate-to-high susceptibility zones. Weighted overlay GIS modeling (validated Kappa = 0.82) classified four hazard classes; high-susceptibility zones (18% of the study area) encompassed all ten active vents. Findings underscore anthropogenic pressurization drivers—primarily surface loading from settlement densification—and the need for continuous InSAR deformation monitoring, piezometric observation, complete hydrogeochemical characterization (including alkalinity and unmeasured cations), and establishing early-warning thresholds for community risk mitigation. Full article

Positive Energy Districts (PEDs) have been proposed as a holistic approach to urban decarbonization. PEDs are defined as delimited areas that produce, annually, more energy than they consume. The methodology proposed for PED assessment integrates multicriteria decision-making geospatial analysis and weighted overlay techniques to assess PED suitability across different dimensions. Data harmonization is included as part of the modeling process, ensuring methodological consistency across diverse contexts. The approach employs a layer overlay and aggregation through a weighting average process, calibrated through stakeholder input, to reflect local priorities and urban-specific conditions in order to identify the potential areas for PED implementation. Geospatial datasets provided as inputs are processed to produce maps that reflect the PED suitability index for the city districts according to the selected dimensions. As a result, the open-source developed MCDA algorithm provides maps that facilitate the identification of relevant zones for PED feasibility. The algorithm was applied in Bratislava city, understanding its identification potential, adaptability and scalability to other cities. The obtained results highlight the most interesting districts in which to build a PED in Bratislava, promoting the algorithm as a replicable decision-making tool for advancing PED identification and deployment. Full article

Ensuring equitable access to healthcare services safeguards individual wellbeing and enhances society’s overall happiness. This study investigates the complex relationships between spatial hospital accessibility, spatial inequality, and urban wellbeing, focusing on the physical dimension of access measured by travel time. Using geospatial and economic data from 13,776 hospitals, this study reveals that inequality in hospital accessibility, as measured by the Gini coefficient, significantly and negatively impacts urban happiness. Additionally, the results reveal a nonlinear, inverted U-shaped relationship between hospital accessibility and city-level happiness, indicating an optimal threshold beyond which marginal benefits decline. Additionally, the results indicate a key mediating mechanism: unequal access drives population out-migration and reduces the permanent resident population. This outcome, in turn, partially transmits adverse effects to city-level wellbeing. These findings demonstrate substantial spatial and contextual heterogeneity, underscoring the need for policymakers to tailor urban health policies that prioritize enhancing accessibility and ensure equitable distribution to foster sustainable demographic stability and overall urban wellbeing. Full article

China’s urban development is shifting from extensive expansion to stock-oriented renewal, making the precise identification of inefficient land stock essential for sustainable spatial governance. To overcome the limitations of single-source data and coarse delineation, we propose a multi-source geospatial framework integrating land-use surveys, socioeconomic statistics, spatiotemporal mobility trajectories, and ecological indicators. Using Shenzhen as a case study, we construct an eight-indicator system across social, economic, and ecological dimensions and apply entropy-based objective weighting to support GIS-based weighted overlay evaluation. The results identify 65.37 km² of inefficient land in 2019, accounting for approximately 7% of Shenzhen’s construction land, with a distinctive “edge aggregation and corridor extension” pattern concentrated along urban–rural fringes and administrative boundaries. Inefficient land is highly uneven across districts, with Longgang (21.11 km²) and Bao’an (12.57 km²) contributing 51.5% of the total and exhibiting statistically spatial clustering (p < 0.01). The observed configuration reflects path-dependent historical development and policy–ecology constraints, including the interaction between ecological control boundaries and peripheral expansion. Overall, by integrating multi-source spatiotemporal big data within a multi-dimensional evaluation framework, the framework offers an objective and transferable approach for diagnosing inefficient land stock and informing targeted urban renewal strategies in high-density cities worldwide. Full article

Grasslands represent an essential resource for rural economies and for the provision of ecosystem services, yet they are increasingly affected by anthropogenic pressures, functional land-use changes, and institutional constraints. This study develops a geospatial decision-support framework for assessing grassland suitability in Hunedoara County, Romania, by integrating the Analytic Hierarchy Process (AHP) and Weighted Overlay Analysis (WOA) within a GIS environment. The assessment is based on nine criteria thematically grouped into three dimensions: (A) physical-geographical, including topographic suitability, climatic pressure, and hydrological risk exposure; (B) ecological and conservation-related, reflected by ecological conservation value, ecological carrying capacity, and the anthropic pressure index; and (C) socio-economic and functional, represented by spatial accessibility, recreational value, and policy support mechanisms. Suitability is defined as the integrated capacity of grasslands to sustain productive and multifunctional uses compatible with ecological conservation and the existing policy framework. Results indicate that 0.43% of the grassland area exhibits very high suitability (Class 1), 44.51% high suitability (Class 2), and 54.75% moderate suitability (Class 3), while unfavorable areas account for only 0.31% of the total (Class 4). The proposed methodology is reproducible and transferable, providing support for prioritizing management interventions, agri-environmental payments, and rural planning in mountainous and hilly regions. Full article

This study presents a comprehensive geospatial framework for assessing coastal vulnerability and ecosystem service distribution along the Greek coastline, one of the longest and most diverse in Europe. The framework integrates two complementary components: a Coastal Erosion Vulnerability Index applied to all identified beach units, and Coastal Flood Risk Indexes focused on low-lying and urbanized coastal segments. Both indices draw on harmonized, open-access European datasets to represent environmental, geomorphological, and socio-economic dimensions of risk. The Coastal Erosion Vulnerability Index is developed through a multi-criteria approach that combines indicators of physical erodibility, such as historical shoreline retreat, projected erosion under climate change, offshore wave power, and the cover of seagrass meadows, with socio-economic exposure metrics, including land use composition, population density, and beach-based recreational values. Inclusive accessibility for wheelchair users is also integrated to highlight equity-relevant aspects of coastal services. The Coastal Flood Risk Indexes identify flood-prone areas by simulating inundation through a novel point-based, computationally efficient geospatial method, which propagates water inland from coastal entry points using Extreme Sea Level (ESL) projections for future scenarios, overcoming the limitations of static ‘bathtub’ approaches. Together, the indices offer a spatially explicit, scalable framework to inform coastal zone management, climate adaptation planning, and the prioritization of nature-based solutions. By integrating vulnerability mapping with ecosystem service valuation, the framework supports evidence-based decision-making while aligning with key European policy goals for resilience and sustainable coastal development. Full article

This study investigates flooding and erosion impacts and human responses in Aoraki Mount Cook and Westland Tai Poutini national parks in Aotearoa New Zealand. These fast-eroding landscapes provide important test cases and insights for considering the public access dimensions of climate change. Our objectives were to explore and characterise the often-overlooked role of public access as a ubiquitous concern for protected areas and other area-based conservation approaches that facilitate connections between people and nature alongside their protective functions. We employed a mixed-methods approach including volunteered geographic information (VGI) from a park user survey (n = 273) and detailed case studies of change on two iconic mountaineering routes based on geospatial analyses of digital elevation models spanning 1986–2022. VGI data identified 36 adversely affected locations while 21% of respondents also identified beneficial aspects of recent landscape changes. Geophysical changes could be perceived differently by different stakeholders, illustrating the potential for competing demands on management responses. Impacts of rainfall-triggered erosion events were explored in case studies of damaged access infrastructure (e.g., roads, tracks, bridges). Adaptive responses resulted from formal or informal (park user-led) actions including re-routing, rebuilding, or abandonment of pre-existing infrastructure. Three widely transferable dimensions of public access management are identified: providing access that supports the core functions of protected areas; evaluating the impacts of both physical changes and human responses to them; and managing tensions between stakeholder preferences. Improved attention to the role of access is essential for effective climate change adaptation in parks and reserves. Full article

In recent years, the rapid construction of government-subsidized rental housing (GRH) has partially alleviated housing pressures caused by the growing number of migrant workers and persistently high rental costs in Shanghai. However, its overriding emphasis on construction and allocation efficiency neglects the realization of spatial justice, particularly in underdeveloped urban areas. This study adopts a mixed-methods approach to examine all 25 GRHs completed and operational in Shanghai’s Five New Towns, employing morphological characteristics and inherent differences to analyze their impacts on spatial justice. First, this study integrates urban functions and spatial justice elements to establish a systematic classification framework and an evaluative system for GRH, and then assesses the achievement of spatial justice across existing projects. Subsequently, morphological analysis is employed to examine how GRHs shape the socio-spatial context of new towns, thereby assessing their role in reinforcing or undermining spatial justice. Finally, this study establishes data logic between typological factors and morphological characteristics and analyzes the inherent differences among various types of GRH by using Fisher’s exact test. The results reveal that although the existing GRHs are situated in different urban geospatial contexts, they exhibit a severe homogenization phenomenon in terms of construction modality, planning layout, and community boundary, with only the residential scale showing inherent differences. The research findings highlight a systematic neglect of spatial justice in the current GRH development paradigm and reveal the underlying causes. This study contributes to the discourse on spatial justice in GRH development by broadening its dimensions, and it provides valuable insights for promoting the realization of spatial justice through multi-tiered policy framework, place-making design strategy, and a joint operation model. Full article

This study investigates the spatial patterns and driving mechanisms of China’s industrial heritage using nationwide provincial-level geospatial data. It combines multiple spatial analysis techniques to identify distribution characteristics and applies a multi-model framework integrating Multi-Scale Geographically Weighted Regression and machine learning to assess the impacts of demographic, economic, climatic, and topographic factors. Results reveal a pronounced clustered pattern and marked spatial differentiation, with core concentrations in the southeastern coastal and central regions. Industrial layouts across historical periods show a shift from coastal to inland areas, reflecting security-oriented spatial strategies. Economic development has a significant positive influence, whereas temperature and the number of industrial enterprises exert negative effects. Natural environmental conditions—such as slope, vegetation coverage, and water systems—serve as both spatial supports and constraints. At the macro level, the spatial configuration of industrial heritage emerges from the structured interplay of historical path dependence, national strategic regulation, and geographic environmental constraints, rather than short-term interactions among isolated variables. The study elucidates the evolutionary logic of industrial civilization and highlights the synergistic mechanisms linking economic, social, and environmental dimensions. It concludes by advocating a hierarchical and multi-factor balanced framework for spatial governance. Full article

Significant changes in the global climate are a focus of widespread concern, with profound implications for economies, daily life, and sustainable development. Analyzing and forecasting these trends relies heavily on meteorological data, which typically possess high-dimensional spatiotemporal attributes. Effectively extracting underlying patterns and meaningful information from such complex data is crucial for informed decision-making. This study addresses the challenge of visually representing temporal sequences within geospatial contexts, a process often hindered by the separate visualization of spatial and temporal dimensions. We propose a method that embeds a geographic map within a parallel coordinate plot: time is represented on the parallel axes, and high-dimensional attributes are encoded using color channels. This integrated view, combined with a suite of interactive techniques, enables detailed, multi-perspective, and holistic visual exploration and enhances the understanding of high-dimensional spatiotemporal meteorological data. Full article

Open data reuse has become a strategic driver of the digital transformation of agricultural, livestock, and environmental systems. In this industry yet significant disparities persist in regions with limited technological and institutional capacity. This global scoping reviews systematically maps providing the scientific evidence on open data reuse and examines its thematic, geographic, and socioeconomic dimensions in relation to sustainability, food security, and biodiversity conservation. The search, conducted in Scopus for peer-reviewed articles from 1993 to 2025, identified 2863 records, of which 1261 met the eligibility criteria. Evidence charting combined Bibliometric mapping, Multiple Correspondence Analysis, Principal Component Analysis, and a modified Delphi method to characterize thematic domains and research alignment. Results reveal three dominant global clusters: Intelligent Digital Agriculture & Sustainability, Geospatial Monitoring & Land Management, and Biodiversity & Livestock Dynamics alongside persistent geographic inequalities that favor high-income regions. A case analysis of Ecuador illustrates how open data reuse is emerging in a peripheral context shaped by structural constraints. Overall, findings show that open data reuse reduces informational asymmetries, enables cross scale environmental and production monitoring, and supports data driven innovation for climate resilience. The proposed BiblioConsensus Framework offers a transferable basis for policy design, capacity building, and international collaboration aimed at strengthening inclusive global open data ecosystems. Full article

As digital transformation intensifies, the governance of spatial data infrastructures is becoming increasingly dependent on the capacity to generate and sustain trust—technological, institutional and civic. This challenge is particularly acute in the Mediterranean region, where disparities in how geospatial data are produced, accessed, and validated are created by uneven digital development and fragmented governance structures. In response to this, this paper introduces an integrated framework combining geospatial artificial intelligence (GeoAI) and blockchain technologies to support transparent, verifiable and spatially explicit models of digital trust. Based on case studies from the Horizon 2020 TRUST project, the framework defines trust through territorial indicators across three dimensions: digital infrastructure, institutional transparency, and civic engagement. The system uses interpretable AI models, such as Random Forests, K-means clustering and convolutional neural networks, to classify regions into trust typologies based on multi-source geospatial data. These outputs are then transformed into semantically structured spatial products and anchored to the Ethereum blockchain via smart contracts and decentralized storage (IPFS), thereby ensuring data integrity, auditability and version control. Experimental results from pilot regions in Italy, Greece, Spain and Israel demonstrate the effectiveness of the framework in detecting spatial patterns of trust and producing interoperable, reusable datasets. The findings highlight significant spatial asymmetries in digital trust across the Mediterranean region, suggesting that trust is a measurable territorial condition, not merely a normative ideal. By combining GeoAI with decentralized verification mechanisms, the proposed approach helps to develop accountable, explainable and inclusive spatial data infrastructures, which are essential for democratic digital governance in complex regional environments. Full article

Urban green spaces are a crucial element of sustainable urban development, but their uneven distribution exacerbates socio-spatial inequalities within cities. This study analyses spatial inequalities in the distribution and accessibility of urban green spaces in 26 European capitals, introducing the composite Urban Green Equity Index. The index incorporates three fundamental dimensions, availability, accessibility and need, aiming to provide a comprehensive picture of environmental inequalities in the urban fabric. The index is composed of seven variables including the percentage of green space, green space per capita, average access time, access time range, population density and the percentages of children and elderly people. Free geospatial and demographic data were used to calculate the variables. The variables were grouped into the corresponding thematic dimensions and then Principal Component Analysis was applied to compose the individual axes, based on which the composite index was calculated. The results were mapped and revealed strong inter-regional and intra-city differences, with Northern European cities showing higher equality compared to those in the South. The study highlights the importance of integrating socio-demographic indicators into urban planning and concludes that the assessment of urban green space requires a holistic, socially sensitive and methodologically sound approach for a just transition to sustainable cities. Full article

In response to the coexistence of multi-objective conflicts and environmental complexity in the renewal of contemporary urban industrial heritage, this study develops a simulation and decision-making methodology for architectural and environmental renewal based on a digital twin framework. Using the Xi’an Old Steel Plant Industrial Heritage Park as a case study, a community-scale digital twin model integrating multiple dimensions—architecture, environment, population, and energy systems—was constructed to enable dynamic integration of multi-source data and cross-scale response analysis. The proposed methodology comprises four core components: (1) integration of multi-source baseline datasets—including typical meteorological year data, industry standards, and open geospatial information—through BIM, GIS, and parametric modeling, to establish a unified data environment for methodological validation; (2) development of a high-performance dynamic simulation system integrating ENVI-met for microclimate and thermal comfort modeling, EnergyPlus for building energy and carbon emission assessment, and AnyLogic for multi-agent spatial behavior simulation; (3) establishment of a comprehensive performance evaluation model based on Multi-Criteria Decision Analysis (MCDA) and the Analytic Hierarchy Process (AHP); (4) implementation of a visual interactive platform for design feedback and scheme optimization. The results demonstrate that under parameter-calibrated simulation conditions, the digital twin system accurately reflects environmental variations and crowd behavioral dynamics within the industrial heritage site. Under the optimized renewal scheme, the annual carbon emissions of the park decrease relative to the baseline scenario, while the Universal Thermal Climate Index (UTCI) and spatial vitality index both show significant improvement. The findings confirm that digital twin-driven design interventions can substantially enhance environmental performance, energy efficiency, and social vitality in industrial heritage renewal. This approach marks a shift from experience-driven to evidence-based design, providing a replicable technological pathway and decision-support framework for the intelligent, adaptive, and sustainable renewal of post-industrial urban spaces. The digital twin framework proposed in this study establishes a validated paradigm for model coupling and decision-making processes, laying a methodological foundation for future integration of comprehensive real-world data and dynamic precision mapping. Full article