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Linear heritage corridors are increasingly exposed to spatially heterogeneous pressures from climate change and human activities, yet integrated geospatial frameworks for corridor-scale risk identification remain limited. Taking the Beijing–Hangzhou Grand Canal as a representative linear World Heritage corridor, this study developed a GIS-based spatiotemporal assessment framework to quantify natural risk, anthropogenic pressure, and their coupled patterns during 1995–2024. Approximately 350 canal segments were constructed as comparable assessment units and linked with 49 heritage sites and 18 World Heritage canal sections through a multi-scale spatial framework integrating canal sections, buffer zones, and heritage sites. Natural risk was characterized using extreme temperature, precipitation, and drought indices, while anthropogenic pressure was represented by nighttime lights, population density, impervious surface, and road density. The results reveal a clear north–south gradient in integrated natural risk, with higher values concentrated in the southern canal sections. Among the three natural-risk modules, temperature, precipitation, and drought contributed weights of 0.594, 0.242, and 0.164, respectively, indicating the dominant role of heat-related processes. The first two principal components of anthropogenic pressure explained 80.8% of the total variance. Four dominant coupling types were identified, among which the dual high-pressure type was concentrated mainly in the southern canal and marked the most critical areas of compound risk. This study provides a geospatial approach for hotspot detection and spatial decision support for the conservation of large linear heritage systems. Full article

The 6 February 2023 Kahramanmaraş earthquakes highlighted the importance of emergency assembly areas for disaster response, evacuation safety, and urban resilience in earthquake-prone cities. Although GIS-based multi-criteria decision-making approaches are widely used to assess spatial suitability, relatively few studies integrate suitability, capacity adequacy, and accessibility within a single framework, particularly in cities directly affected by the 2023 earthquakes. This study evaluates emergency assembly areas in Malatya, Türkiye, using an integrated GIS–Best–Worst Method (BWM) framework. Nine criteria—geology, population density, building density, elevation, slope, distance to roads, distance to rivers, distance to fault lines, and distance to buildings—were weighted based on the judgements of 15 experts involved in Provincial Disaster Risk Reduction Plan (İRAP) processes. The BWM results show that geology and distance to fault lines received the highest weights, whereas distance to roads had the lowest weight. The spatial analysis indicates that highly suitable areas are concentrated mainly in the city centre, while several peripheral neighbourhoods are constrained by geological, topographical, and accessibility-related factors. Existing official emergency assembly areas cover only 27.9% of the population and are located in 13 of 88 neighbourhoods. Estimated access times range from 0 to 5 min in central areas to 10–15 min, or beyond effective service coverage, in peripheral neighbourhoods. Although integrating parks and green spaces substantially increases potential capacity, it does not fully eliminate neighbourhood-level inequalities. The findings provide a spatial decision-support framework for emergency planning in earthquake-prone cities. Full article

Improving the comprehensiveness of old community renewal is a key approach to enhancing residents’ quality of life and the community environment. Currently, research on improving comprehensiveness from both supply and demand perspectives remains limited. This study constructs an evaluation system comprising 27 indicators that cover three dimensions: physical infrastructure, community services, and community governance. Adopting the approach of “single indicator, two-way assessment, and comprehensive evaluation,” this study conducts evaluations from both supply and demand perspectives. On the supply side, facility coverage is calculated through field surveys, POI data, and ArcGIS spatial analysis; on the demand side, resident satisfaction is measured via questionnaires, and an evaluation framework for supply–demand matching is constructed using the IPA model. An empirical analysis using Community X in Beijing as a case study reveals that the completeness of community renewal exhibits significant hierarchical differentiation: supply–demand matching and conditions are favorable for basic services, elderly care and services for special groups, and cultural services; supply and demand for buildings, infrastructure, and public safety are balanced and moderately complete; environmental facilities exhibit oversupply and excessive completeness; and long-term management and resident participation suffer from insufficient supply and lack of completeness, emerging as core constraints. Based on these findings, targeted optimization strategies are proposed, which can provide scientific guidance for the development of comprehensive communities and the renewal of existing urban stock. Full article

The rapid expansion of solar photovoltaics is intensifying competition for land and highlighting the need for scalable energy solutions that can be integrated into existing power systems without displacing agricultural activity. Once the technical and agronomic viability of agrivoltaic configurations has been demonstrated at field scale, a critical next step toward their market consolidation is the assessment of their deployment potential at regional scales from an energy systems and grid integration perspective. This study presents a GIS-based framework to evaluate the large-scale implementation of vertically integrated agrivoltaic systems, using vineyard landscapes in the Region of Murcia (southeastern Spain) as a representative case study. The analysis combines high-resolution land-use data, crop distribution, regulatory constraints on grid connection distances, and existing electrical infrastructure to quantify installable capacity, energy production, self-consumption potential, and grid accessibility. Results indicate that vertically mounted bifacial PV systems could reach up to 7.06 GWp, generating approximately 11.84 TWh/year, while revealing a pronounced spatial mismatch between optimal agrivoltaic production sites and current grid connection points. This distance-dependent distribution highlights the need for differentiated deployment strategies, balancing local self-consumption, grid reinforcement, and centralized injection. Beyond the specific case examined, the proposed approach provides a transferable framework for energy system planning, supporting grid-aware agrivoltaic deployment in diverse regions and regulatory contexts. Full article

Evapotranspiration (ET) is a fundamental process within the water cycle and the agricultural water balance, optimizing resource allocation, maintaining soil health, and enhancing ecosystem resilience to climate change. Because ET represents a primary consumptive use of irrigation on agricultural lands, enhancing water-use efficiency and sustainable water management requires accurate estimation of evapotranspiration to support long-term sustainability and productivity. This study offers an effective means to visualize spatial and temporal patterns of reference evapotranspiration (ETo) across various vegetation management practices. This study examined the impacts of agroforestry buffers (ABs), grass buffers (GBs), biofuel crops in an agroforestry watershed (BCa), and biofuel crops in a grass buffer watershed (BCg) on ETo, compared to a corn (Zea mays L.)–soybean (Glycine max L.) rotation (RC) for claypan soil in Northern Missouri, USA. The experimental watersheds were located at the Greenley Memorial Research Center, Missouri, USA. Campbell Scientific sensors and Photosynthetically Active Radiation (PAR) smart sensors were installed to measure net radiation, anemometers, humidity, and air temperature. All instruments were mounted on masts at a height of 2 m above ground level in crop, tree, grass, and biofuel areas. Measured meteorological data were recorded hourly from April to October during 2017 and 2018. Daily ETo predictions were calculated using the Penman–Monteith model. These ETo predictions were displayed across the landscape using Python-based GIS for selected dates (each Saturday) for the watersheds. The methodology was implemented using the software programs of Python 2.7.10 and ArcGIS 10.3.1. The results indicated that ETo increased by 11%, 17%, 18%, and 25% in 2017, and by 7%, 9%, 14%, and 20% in 2018 for AB, BCa, BCg, and GB, respectively, compared to RC management. This process may improve soil water recharge in perennial management systems. Accurate estimation of ET in agricultural regions is critical for understanding water balance, hydrological and ecosystem processes, and climate variability. Given that agriculture constitutes the majority of global water consumption, precise ET estimation is particularly significant for sustainable water management, especially in regions experiencing water scarcity. These outcomes may support effective planning and management of agricultural water resources by enabling optimized irrigation and agricultural production. Full article

The rapid growth of electric vehicle (EV) adoption requires the scalable and cost-effective deployment of publicly accessible charging infrastructure, where cost-effectiveness is understood in terms of infrastructure reuse rather than explicit economic optimisation. Integrating slow AC charging units into existing public lighting networks represents a promising infrastructure reuse strategy, though spatial feasibility, electrical constraints, and regulatory requirements must be addressed. This study proposes an integrated GIS–MCDA–MILP framework for the optimal allocation of EV charging stations within public lighting systems. GIS-based spatial analysis identifies feasible poles based on parking accessibility and demand indicators, while MCDA ranks candidate locations and a MILP model determines optimal deployment under capacity constraints and phased rollout scenarios. The framework also incorporates AFIR-based policy benchmarking to assess compliance under current and future EV adoption levels. A real-world case study identifies 1223 feasible poles with a structural hosting capacity of 368 chargers. The results demonstrate that such integration is viable at the spatial and cabinet-capacity planning level but structurally limited, with a critical fleet growth multiplier of approximately 3.4 identified as the threshold beyond which lighting-integrated deployment alone becomes insufficient for AFIR compliance. The proposed framework advances the state of practice by coupling spatial, electrical, and regulatory analysis within a single reproducible methodology, offering a transferable decision-support tool for sustainable urban EV charging planning. Full article

Climate change poses an increasing threat to the functionality of forest transportation infrastructure, particularly in northern regions where seasonal access and ground conditions are critical for wood mobilization. The objective of this study was to assess how projected changes in temperature and precipitation may compromise accessibility to forest resources. In addition, it aimed to develop targeted adaptation recommendations to support resilient transportation systems. These actions are essential to ensure the continuity of wood supply under future climatic conditions. Climate projections were extracted from the climatedata.ca platform based on the CMIP6 (CanDCS-M6) model under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Using a GIS-based workflow, projected temperature and precipitation data were spatially matched to the selected Forest Management Units (FMUs) in Quebec, Canada, and the study area was divided into three latitudinal subregions to capture spatial temperature variation. Classified road network maps were then overlaid with projected climate data for 2020, 2040, 2060, and 2080 to evaluate winter road usability, precipitation-related exposure of road classes, and changes in effective winter road density. Results showed a consistent shortening of the winter road operational period under all scenarios, with the most severe reductions under SSP5-8.5. In highly affected areas, the winter road usability window may decrease from 90 days in 2020 to only 21 days by 2080. Increased precipitation is also expected to affect numerous road segments, raising risks of erosion, sedimentation, and loss of accessibility. A reduction of approximately 7% in effective winter road density is projected across the study area under the high-emission scenario (SSP5-8.5), reflecting the most severe impact of future temperature increases. Based on these findings, targeted road upgrades, climate-informed infrastructure design, and alternative access planning are proposed to help sustain wood flow and support year-round forest operations under future climatic conditions. Full article

Rapid urbanization highlights the increasing importance of urban green infrastructure in shaping urban spatial organization, quality of life, and environmental sustainability. This study examines spatial inequalities in the provision of urban parks in Sofia (Bulgaria) and Istanbul (Republic of Türkiye) from a comparative urban geography perspective. The two cities are selected as contrasting urban contexts in Southeastern Europe, characterized by different patterns of urban development, population density, and spatial structures. A GIS-based analytical framework is applied at the district administrative level, integrating indicators such as the share of urban parks, park area per capita, Local Moran’s I, and the Gini coefficient. The results reveal distinct spatial patterns: Sofia demonstrates relatively higher levels of park provision but pronounced inequalities, characterized by the concentration of large park areas in a limited number of central districts. In contrast, Istanbul exhibits a more even spatial distribution but significantly lower levels of park area per capita, indicating an overall shortage of urban park space. The findings demonstrate that Sofia and Istanbul experience different forms of spatial disparities in park provision due to distinct trajectories of urban development. Full article

The steno-endemic Verbascum rupicola faces a precarious future due to its extreme habitat specialization on tectonically active hydrothermal quartz veins. This study presents a long-term assessment based on periodic population censuses spanning 18 years (2007, 2016, and 2025) to assess the demographic and spatial trends of its global population in the Tahtalı Dam basin, Türkiye. Field surveys, GIS-based habitat mapping, and controlled pollination experiments were integrated with seed germination kinetics and ex situ cultivation trials. Results reveal a precipitous 69.12% global population decline, primarily driven by a 33.41% habitat loss from agricultural expansion in 2011 and the total extirpation of three sub-populations by a major wildfire in 2017. Furthermore, a “reproductive squeeze” was identified, where climate-induced reductions in flower production (18.87%) are compounded by intensifying floral predation by Pieris rapae. Reproductive analysis revealed random monomorphic enantiostyly—reported for the first time in the genus—which functions as a flexible mating system prioritizing outcrossing while providing reproductive assurance. Despite high intrinsic seed viability (69.12%), ex situ cultivation largely failed (3.5% survival; 1 out of 28 transplanted individuals), underscoring the species’ obligate chasmophytic nature. Consequently, V. rupicola meets the criteria for Critically Endangered (CR) status, necessitating urgent “micro-reserve” protection of its remaining habitat and in situ restoration efforts. Full article

Wastewater containing high pollutant loads is discharged into the municipal sewerage system by industrial facilities operating within the industrial zones of Konya, Türkiye. Although regulations mandate that wastewater be treated to comply with specified discharge standards, some facilities lack pretreatment systems due to high capital and operational costs, while existing systems experience operational deficiencies. As a consequence, operational disruptions and increased environmental risks occur within the municipal sewerage system. Periodic sampling and inspection activities conducted by municipal authorities are becoming increasingly challenging for effective monitoring and evaluation as the number of facilities increases. In this study, a Geographic Information System (GIS)-based approach was developed to enhance monitoring effectiveness, and industrial wastewater quality data were analyzed using ArcGIS Pro 2.9 software (Esri, Redlands, CA, USA) to generate spatial pollution distribution maps. Samples were collected from five industrial facilities and four sewer junction points located in the Hacıyusufmescit, Emirgazi, and Fetih neighborhoods, where odor problems are frequently reported, during the 2022–2023 period. It was determined that COD (24,960 mg/L), BOD (2970 mg/L), and oil and grease (254 mg/L) concentrations significantly exceeded the regulatory discharge limits, particularly during the summer season. The results demonstrate that GIS-based monitoring systems constitute an effective tool for the early detection of pollution and odor-related problems at the urban scale, for the systematic management of control processes, and for the facilitation of evidence-based decision-making. Full article

Geospatial Artificial Intelligence (GeoAI) enables the automated generation of built environment map features, such as building outlines/footprints, on a global scale. However, the integration of these AI-generated datasets into Volunteered Geographic Information (VGI) platforms like OpenStreetMap (OSM) risks incorporating ‘AI slop’, consisting of geometrically inconsistent/unreliable data, into the online map. While the OSM “Code of Conduct for Automated Edits” provides a policy framework for data ingestion, it lacks a machine-enforceable mechanism for real-time quality gating. This paper proposes a GeoAI-Gatekeeper to perform this task—an automated process that applies empirical Acceptable Quality Thresholds (AQT) to address the GeoAI data governance problem. Because the Gatekeeper utilizes an intrinsic, no-reference evaluation of geometric fidelity, it can assess incoming AI-generated data streams in real-time without requiring ground-truth benchmarks. Importantly, it focuses exclusively on the geometric validation of building footprints, acknowledging for now that semantic enrichment, such as tagging, remains a human-centric task. The presented GeoAI-Gatekeeper is a working prototype developed for a specific urban area, systematically triaging incoming AI-generated data into three tiers; Auto-Accept, Manual Review, and Reject. It provides a Web-GIS interface for Human-in-the-Loop (HITL) functionality to ensure the OSM community remains the final arbiter of acceptable data quality. Testing the Gatekeeper in Dublin (Ireland) demonstrates that our solution can auto-ingest 93.6% of features with a 14x reduction in human review effort while still adhering to OSM’s cartographic integrity standards. By implementing qualitative community guidelines into machine-enforceable thresholds, our approach introduces a viable methodology for next-generation hybrid VGI systems. Importantly, it ensures that the transition towards automated data ingestion reinforces, rather than undermines, the reliability of global crowd-source mapping datasets. Full article

The availability of tsunami hazard and exposure information is crucial to support effective emergency response in coastal areas. This study presents an automated framework that integrates tsunami numerical simulation, geospatial exposure analysis, and WebGIS-based visualization to generate standardized hazard and exposure reports for decision support. Using a parallel implementation of the TUNAMI-N2 model, a 6-h tsunami simulation for the Peruvian coast can be completed in ~45 min, with hazard and exposure reports automatically published on a WebGIS platform within ~3 min. Application to the historical 1746 Lima tsunami demonstrates the system’s capability to quantify hazard and exposure for operational decision-making. Full article

The Copenhagen 10-step method is a set of policies that originated in the 1950s to reduce vehicle traffic in Copenhagen, which was heavily impacted by traffic. These policies are incorporated into a different dynamic on a global scale every day and are adopted while maintaining relevance. These policies, advocated in the context of climate change and carbon emission targets, as well as livability and health-focused urbanization, justice, and accessibility in transportation, are criticized for potentially negatively affecting low-income groups and commercializing urban transformation. Furthermore, they require adaptation because their applicability is seen as limited in terms of localization. In this context, the adaptability of the method to different social and spatial contexts has become a critical research topic, particularly in local studies, where application is more important and the order of implementation becomes of great importance. Within the scope of this study, a Copenhagen 10-step prioritization study was conducted specifically for the Küçükçekmece Kanarya Neighborhood, where low-to-middle socioeconomic groups live, and which has been declared a risky area in terms of building stock. Accordingly, a two-phase study was conducted. In the first phase, transportation and planning experts were asked to prioritize the 10 steps, and the timing of each implementation was determined based on the resulting ranking. In the second phase, accessibility analyses for the region were conducted using GIS (Geographical Information Systems)-based spatial data, such as accessibility, slope, and the distribution of urban facilities. Subsequently, these two phases were combined to create a simple prioritization framework for the areas of greatest concern in Kanarya, as well as for urban renewal, transportation, and government investment plans. According to the SWARA results, increasing bicycle use (C10) was the most important criterion at 17.2%, followed by making the bicycle the primary mode of transportation (C9) at 13.8% and adapting the city to seasonal changes (C8) at 11.5%. This study, which is significant for its focus on a specific region at the local implementation level, presents a straightforward model—based on concrete findings—for prioritizing sustainable transportation and urbanization policies in socioeconomically vulnerable areas. In doing so, it contributes to aligning theoretical approaches with practical field applications. Full article

Interior Alaska’s rapidly thawing permafrost poses risks to environmental systems and infrastructure, challenging municipal planning. As part of a larger project examining frozen commons, McGrath, Alaska, officials and tribal council members requested a permafrost map. This paper presents ground thermal monitoring (October 2023 to March 2025) and imagery-derived land cover and permafrost/seasonal freezing maps developed after testing machine learning and contextual feature methods. Over the two years of observation, ground temperature warmed 0.26 °C year⁻¹ at 1.5 m depth. A high-accuracy land cover classification was generated to project ground thermal conditions across the community. Several supervised machine learning algorithms were compared with and without contextual features on a Satellite Pour l’Observation de la Terre (SPOT) scene in ArcGIS Pro. Per-pixel classification performed better given the contiguous spectral features, and contextual features did not improve overall accuracy. Instead, a random forest classifier that yielded the highest overall accuracy was used to generate a 1.5 m resolution permafrost/seasonal freezing map. Maps and thermal data can inform community frozen commons decision-making, and methods can be repeated to monitor regional change. Discussion of results highlights potential permafrost mapping applications, particularly of Gabor and mean contextual features with object segmentation. Full article

Background: Family caregivers are essential partners in the outpatient care of older adults with cancer, yet the knowledge, skills, and confidence, collectively, of caregiver activation are not well characterized in Asian settings. Understanding activation can inform tailored support to sustain effective caregiving. Accordingly, this study assessed the overall activation levels among cancer caregivers in the three most common cancer diagnoses in Singapore by using the Caregiver-Patient Activation Measure (CG-PAM). Methods: A total of 103 informal caregivers of patients ≥60 years (lung, GI, or myeloma) at Singapore’s largest public cancer hospitals completed the 13-item CG-PAM, scored 0–100 and classified into four activation levels. Descriptive statistics summarized characteristics and CG-PAM responses, and logistic regression analyses assessed the degree of activation for individual demographic and other characteristics (p < 0.05). Results: Caregivers showed moderate–high activation (mean 65.9 ± 16.1) and the following levels: L1, 4.9%; L2, 23.3%; L3, 38.8%; L4, 31.1%. They strongly endorsed personal responsibility and active engagement, reporting confidence in key self-management tasks, but struggled to sustain behaviors under stress. Activation was not significantly associated with demographic factors or any other measured characteristics. Conclusions: Caregivers of older adults with cancer in Singapore included in this study showed moderate–high activation and strong self-management confidence but struggled to sustain behaviors under stress. Routine activation assessment and tailored support (education, coaching) could strengthen outpatient care. Longitudinal and interventional research, alongside follow-up interviews, is needed to identify unmet needs, existing support systems, and inform scalable, sustainable models. Full article