Spatially varying coefficient (SVC) models have emerged as an important class of methods in spatial analysis because they enable the explicit modelling of spatial heterogeneity in the relationships between a response variable and a set of predictors. A wide range of SVC modelling frameworks has been developed, each with distinct assumptions, strengths, and limitations. However, a comprehensive synthesis that systematically reviews and contrasts these model families remains limited. In this study, we review major categories of SVC models, frequentist local regression approaches, Bayesian SVC frameworks, eigenvector spatial filtering (ESF), and the integration of deep learning techniques in frequentist frameworks. Additionally, the study discusses key challenges in spatial analysis, presents a comparative synthesis of SVC models, and provides practical guidance for their adoption. Based on a structured literature search using predefined keywords across three major online databases, a total of 98 articles were ultimately identified as suitable for inclusion in the review. Synthesis of these studies indicates that no single SVC model consistently outperforms others across all empirical contexts, and that model choice should be guided by the research question, data characteristics, and analytical objectives rather than by the pursuit of a universally optimal approach. Furthermore, we identify five important avenues for future research: improving the computational scalability and memory efficiency of these models; advancing methodological development in Bayesian and ESF frameworks relative to frequentist approaches; rethinking the conceptualization of proximity and the optimization of spatial scale; addressing the sensitivity of frequentist local models to sample size by establishing clearer guidance on adequate sample sizes for stable and reliable inference; and providing clearer recommendations on when each category of SVC model is most appropriate for application. Addressing these challenges would significantly advance the theoretical foundations, methodological rigour, and practical accessibility of SVC models in spatial analysis.
Coastal deltaic regions of Tamil Nadu are increasingly affected by salinization, intensive irrigation, and land-use transformation, yet existing studies often examine water quality or biodiversity independently, limiting their applicability for integrated resource management. Addressing this gap, the present study provides a spatially integrated assessment linking surface water irrigation suitability, hydrochemical controls, and broader land-use and ecological contexts across the coastal districts of Thiruvarur, Thanjavur, and Nagapattinam, Tamil Nadu. The study evaluates surface water quality for irrigation, identifies dominant hydrochemical processes and seasonal variability, and examines their implications for environmental sustainability. Field-based surface water sampling was combined with remote sensing, GIS techniques, and multivariate statistical analyses. Irrigation suitability was assessed using the Irrigation Water Quality Index (IWQI) along with salinity, sodicity, and magnesium hazard indices, while Pearson correlation and factor analysis were employed to identify the key drivers of water quality degradation. The results indicate that 48% of samples fall under moderate to severe irrigation restrictions, primarily due to elevated magnesium and salinity hazards, whereas sodicity remains largely within acceptable limits. GIS-based spatial analysis identified highly vulnerable coastal tracts, delineated as 'Red zones', while multivariate results highlight agricultural intensification, salinization, and excessive groundwater extraction as major anthropogenic controls. Overall, the findings provide actionable insights for zone-specific irrigation management, groundwater regulation, and sustainable coastal resource planning, with indirect relevance to biodiversity conservation, contributing to the achievement of SDGs 6, 13, and 15.
Point of Interest (POI) data is a critical source in urban geography for uncovering city structure and reflecting patterns of urban activity. Assessing and enhancing the effectiveness of POI data is fundamental. However, most current methods for evaluating POI validity rely on static reference data, and overlook the dynamic change of POIs, especially replacement by similar categories, a type of change that can be referred to as homogeneous substitution. This omission reduces both the accuracy and credibility of evaluation results. To identify the extent to which homogeneous substitution affects the evaluation of POI validity, this study designs a comparative experiment using POI data from Changzhou, China. Validity was evaluated under two conditions, with and without considering homogeneous substitution, and the results show that incorporating homogeneous substitution into the evaluation framework increased the validity estimate by approximately 13.8%, and the standard error was about 0.4% lower than the method excluding homogeneous substitution. These findings reveal the issue of underestimating of POI validity in traditional static verification and demonstrate the essential role of dynamic change in POI validity evaluation, offering an insightful direction for improving existing evaluation systems. Meanwhile, it also contributes to improving the realism of POI functional representation and enhances our understanding of urban functional structures and their processes of renewal and continuity.
Groundwater plays a critical buffering role against climate variability in semi-arid regions, yet its resilience to increasing drought stress remains poorly quantified in many trans-boundary African basins. The UZRB (Upper Zambezi River Basin), spanning Angola, Zambia, Namibia, and Botswana, is increasingly exposed to climate-change-induced drought, compounded by hydrogeological constraints and growing anthropogenic pressures. This study develops a spatially explicit GVDM (Groundwater Vulnerability to Drought Map) for the UZRB using an integrated fuzzy logic-based GIS (Geographic Information System) framework that explicitly accounts for exposure, sensitivity, and adaptive capacity dimensions of groundwater vulnerability. Twelve physically and socio-environmentally relevant indicators were derived primarily from remotely sensed and global datasets to address the scarcity of in-situ observations. Exposure was characterized using long-term precipitation (CHIRPS), evapotranspiration (WaPOR), and population density (WorldPop). Sensitivity was assessed through depth to groundwater, groundwater recharge and storage anomalies (GLDAS/GRACE), aquifer productivity, soil type (FAO), and topographic/aspect attributes (Copernicus DEM). Adaptive capacity was evaluated using groundwater storage anomalies, drainage density and lineament density. All indicators were normalized, fuzzified using expert-informed membership functions, and integrated using a fuzzy GAMMA overlay operator. Vulnerability classes were delineated using percentile-based defuzzification thresholds. Results reveal pronounced spatial heterogeneity in groundwater drought vulnerability across the basin. High vulnerability is concentrated in the southern and southeastern UZRB, where low precipitation, persistently high evapotranspiration, shallow groundwater tables, limited recharge potential, and increasing population pressure converge. In contrast, the northern and northwestern regions exhibit lower vulnerability due to comparatively higher rainfall, deeper aquifers, and more favourable hydrogeological conditions. Model validation using the SCWBI (Standardized Climatic Water Balance Index) for 2009-2023 demonstrates strong spatial agreement between high groundwater vulnerability zones and areas experiencing moderate to severe climatic water deficits, confirming the robustness of the proposed framework. This study represents the first basin-wide, fuzzy logic-based groundwater drought vulnerability assessment for the UZRB that integrates physical, climatic, and socio-economic drivers within a unified framework. The resulting GVDMs provide a practical decision-support tool for drought risk reduction, adaptive groundwater governance, and sustainable water-resources planning in data-scarce, climate-vulnerable trans-boundary basins across Southern Africa and beyond.
In recent years, the illogical flow of cropland resources in China has increased the mismatch between cropland resources and natural geographic conditions (NGCs) posing a threat to national food security. This paper investigates the spatial distribution pattern of NGCs, by utilizing land use data, it comprehensively studies the variation of cropland resources from 1980 to 2020, and meticulously analyzes the matching situation and change trends of cropland resources and NGCs. The results show that: (1) There are remarkable disparities in NGCs among different natural areas. The natural conditions in the South and East China Regions are far superior to those in the North and West regions. (2) Cropland has gradually flowed from the wet-hot southern regions to the warm-dry northern regions, and the quality of cropland decreased. From 1980 to 2020, the cropland of the Northeast Region and the Northwest Region has increased by 87,183 km2, while the total cropland area in the South China Region, the Middle and lower reaches of the Yangtze River Region, and the Huang-Huai-Hai Region decreased by 57,362 km2. The gravity centre of cropland overall shifted 82.2 km northwestward. (3) The distribution of cropland does not align with that of NGCs, and the mismatch is further intensifying with cropland changes. The study results suggest that the strict cropland acquisition and compensation policy norms should consider the characteristics of regional NGCs. Moreover, cropland resources should be sustained balance within natural areas or the 10 large regions, which have great significance for protecting the quantity and quality of cropland resources.
Rapid urbanization, coupled with frequent land-use shifts, has emerged as a primary driver of ecological degradation in the Anthropocene. This study dissects the intricate interplay between land-use and land-cover change (LULC) and ecological environment quality within Hubei Province, a region undergoing rapid transformation, employing a multi-faceted approach that combines remote sensing with advanced statistical modelling. Our analysis reveals that: (1) From 1995 to 2025, Hubei’s land-use trajectory was marked by a dramatic surge in construction land, characterized by an early phase of rapid change transitioning to relative stabilization, while the comprehensive index of land-use intensity paradoxically decreased across prefecture-level cities amidst rapid urbanization; (2) despite a seemingly positive trend of overall ecological environment improvement across the province over three decades, a significant divergence exists between areas of ecological recovery and degradation, highlighting spatial heterogeneity in these responses; (3) Anthropogenic activities are unequivocally identified as the primary drivers of ecological change, exhibiting distinct spatial variability. This trend is projected to result in urban expansion, a marginal decrease in farmland, strengthened protection of forests and wetlands, and an increase in transportation infrastructure across Hubei by 2035. These findings suggest that judicious adjustments to the land-use mosaic offer a pathway to maintaining, or even enhancing, regional ecological integrity, underscoring the urgency for integrated planning and management strategies that balance rapid development with the long-term well-being of the socio-ecological system.
Tourism plays a vital role in economic development, but fluctuations in demand, particularly during crises such as the COVID-19 pandemic, highlight the limitations of traditional data sources for timely and detailed monitoring. This study investigates the potential of social media data (Twitter now X) as a complementary data source for estimating domestic and international tourist arrivals. A three-step pipeline was developed to identify tourists from other non-tourist social media users: (1) users were classified using a Large Language Model (LLM) to exclude non-individual accounts; (2) home location was inferred through geocoding of self-reported textual information; and (3) tourists were classified based on the United Nations World Tourism Organization (UNWTO) criteria of spatial origin, trip duration, and travel purpose. Social media derived estimates of tourist arrivals in Bali between 2019 and 2021 were compared with official statistics and showed strong correlations, with coefficients of 0.999 for international tourist arrivals and 0.896 for domestic arrivals. As well as monthly counts, the approach provided fine-grained insights into tourist behaviour, including inferred home origins, destination hotspots, and daily and hourly arrival patterns - dimensions that are typically absent from conventional datasets. While the consistency of social media data declined during the COVID-19 pandemic, the findings demonstrate that social media can be used to generate timely, flexible, and spatially detailed observations that complement official statistics. A number of discussion points and areas of further work are identified to refine classification methods, to enhance scalability and the applicability of the approach in diverse contexts.
Landslide susceptibility analysis is a common approach for assessing the risk of slope failures in areas affected by rainfall. During the rainy season, the city of Salvador, Brazil, often experiences slope failures and landslides. This research applied the infinite slope model using GIS as a screening method to assess landslide susceptibility in Salvador. The GIS-based model calculated the slope stability factor of safety by integrating spatial data, including soil parameters, slope angles, and depths of water penetration, to identify areas prone to landslide during rainfall events. Soil parameters were obtained from laboratory analyses of 392 samples collected from various locations in Salvador, which is considered an administrative subdivision organized by districts. Four water penetration depths (h = 1, 2, 3, and 4 metres) in the soil were simulated, and their influence on the slope factor of safety was analyzed. Susceptible areas for landslide were obtained by calculating the thickness of the unstable material or the weight of the surface layer as a function of water penetration depth. For h = 1 m, the total landslide-susceptible area was 1.24 km2, while for h = 4 m, the area increased to 42.75 km2. The 4-Liberdade/S & atilde;o Caetano district showed the largest area susceptible to landslides. The results from the GIS model were consistent with the historical landslide data and landslide susceptibility maps from Servi & ccedil;o Geol & oacute;gico do Brasil (CPRM - Brazil Geological Service) official dataset. The integration of the infinite slope model into GIS provided a valuable tool for improving landslide hazard management by screening landslide-susceptible areas at both regional and local scales.
In recent years, in-person shoppers have increasingly demanded systems that minimize both travel costs and product prices. This research aims to design and implement an open-source, web-based location-based service (LBS) system to assist customers in locating stores that offer desired products at the lowest overall cost. The study focuses on Tehran, Iran, as a case study area. Unlike existing LBS and vehicle routing studies that focus on delivery or single-destination navigation, this work introduces the first open-source Web-GIS framework specifically designed for multi-stop, price-sensitive in-person shopping trips, a significantly underexplored consumer segment in dense urban environments of developing countries. The system utilizes open-source technologies to reduce development and operational costs. To optimize the shopping route, a heuristic store allocation method is proposed to reduce computational complexity from factorial O(n!) to polynomial O(n * m), where m is the average number of stores per product. Subsequently, the Travelling Salesman Problem (TSP) is solved using a Genetic Algorithm, leading to a significant reduction in total travel distance. In one test case with six product locations, the optimized route reduced the distance from 195 km (random order) to 146 km. Finally, for turn-by-turn navigation, the A* algorithm is employed, showing a 70% average reduction in response time compared to Dijkstra’s algorithm, with only minimal accuracy differences. The results indicate that the proposed framework effectively reduces shopping costs and improves efficiency for urban users. Moreover, the use of open-source Web-GIS technologies ensures scalability, maintainability, and low-cost implementation for future developments.
The inference of causation is essential for interpretable scientific research and evidence-based policymaking. Existing causal methods - including those based on intervention, forecasting, and graph models - have achieved considerable success in non-spatial domains such as medicine, economics, and sociology. However, significant challenges arise when these methods are applied to geographic phenomena, which constitute complex spatiotemporal processes integrating geo-events and geo-environments. Current causal methods, which predominantly infer relationships from temporal information, often fail to fully utilize spatial information, such as the locations and distributions of geo-events. Furthermore, they typically overlook spatial lags between events and how these lags vary across different geo-environments. These limitations result in potentially incorrect causal conclusions. To address these issues, we propose a novel causal framework for geographic phenomena. This framework develops an Adaptive Gaussian Field (AGF) model to capture spatial lags. It first extracts spatial association patterns from geo-event locations and distributions, then infers causal relationships from these associations. The integration of spatial lags into both association mining and causal inference is implemented through two novel methods: a spatial-lagged co-distribution mining method and a spatial-lagged Peter-Clark (spatial-PC) method. This proposed framework offers a robust approach for uncovering the underlying mechanisms of geohazards in Yunnan, China.
The synthesis of high-performance computing (HPC) and machine learning has been critical for addressing complex geospatial problems and enabling geospatial knowledge discovery. This paper conducts a systematic review of 289 selected literature indexed in the Web of Science Core Collection from 1996 to 2024 that integrates HPC and machine learning (ML) for geospatial discovery and innovation. Starting in 2015, there has been a significant increase in studies combining HPC including supercomputing, parallel computing, cloud computing and fog computing with machine learning models for geospatial research across domains. This paper categorizes prior work based on the purposes of leveraging machine learning and HPC for geospatial knowledge discovery including speedup, accuracy improvement, spatial and temporal resolution improvement, scaling up, real-time analysis and novel model development. In addition, we propose a future research agenda including five key research questions focusing on scaling geospatial foundation models on HPC systems, integrating ML with physics-based models while preserving fidelity, quantifying uncertainty and ethical risks in ML predictions, balancing computational intensity with energy efficiency and ensuring HPC-ML pipelines are FAIR and cross-sector usable as well as four interconnected research thrusts: scalable geospatial data fabrics, geospatial foundation models, domain knowledge and ML integration, and responsible and transparent geospatial AI, along with their future implementation strategies and anticipated impacts.
With the acceleration of global climate change, the evolution of geographical environments has become increasingly complex. This study explores the mechanisms through which topography-climate coupling influences extreme precipitation events in China. Using integrated geographic datasets on precipitation, temperature, and land use/cover, combined with GIS technology and a random forest regression (RFR) model, the spatiotemporal evolution from 1990 to 2020 is analysed. Unlike previous studies that only focused on a single factor or pairwise correlations, this study innovatively quantifies the nonlinear coupling effects of terrain features, climate variables, and land use/cover change (LUCC) on extreme precipitation’s spatial distribution and intensity. The results show that topographic factors, including elevation, slope, and terrain variation, significantly impact the spatial distribution and intensity of rainfall. Extreme precipitation events are spatially clustered, mainly in the southern Qinghai-Tibet Plateau, the southeastern coastal region, and the hilly areas of the middle and lower Yangtze River. These high-risk areas are characterized by complex terrain, steep slopes, and high temperatures. Moreover, the occurrence of extreme rainfall is found to be driven by multi-factor interactions rather than by a single factor. The prediction model demonstrates high accuracy (R2 = 0.85, MSE = 0.00023), providing valuable insights for disaster prevention and geographical environmental research.
Geographic Information Systems (GIS) offer powerful analytical capabilities for digital humanities research, yet technical barriers and reproducibility challenges limit their adoption by humanities scholars. This paper presents a workflow-based approach that democratizes reproducible spatial analysis by transforming complex GIS operations into accessible, executable workflows using the open-source KNIME platform. Our methodological framework addresses three critical challenges: technical complexity through intuitive workflow design, reproducibility through complete process documentation, and accessibility through browser-based deployment. The approach integrates specialized components for historical data processing - including temporal uncertainty handling and spatial disambiguation - within standardized, shareable workflows that preserve analytical transparency while requiring no GIS expertise. We demonstrate the framework's effectiveness through a comprehensive case study analysing spatial mobility patterns of pre-modern Chinese literati, where researchers successfully performed complex network analysis, trajectory visualization, and spatiotemporal pattern detection using our workflow-based tools. Results confirm that the approach enables exact analytical replication while significantly lowering technical barriers for humanities researchers. The framework's modular design supports adaptation to diverse historical spatial research contexts, establishing a replicable methodology for democratizing GIS in digital humanities. This workflow-based approach contributes to more inclusive and reproducible spatial humanities scholarship by making advanced geospatial analysis accessible to researchers regardless of technical background.
This paper presents a two-stage modelling framework for land suitability evaluation that integrates geographic information system (GIS)-enabled multi-criteria decision analysis (MCDA) with economic simulation under uncertainty. The first stage applies a hybrid MCDA method combining entropy weighting and the analytical hierarchy process to generate spatially explicit suitability maps incorporating biophysical, social, and sustainability criteria. In the second stage, Monte Carlo simulation is used to evaluate the economic performance of alternative land use scenarios, addressing variability in key input parameters such as yield, cost, and price. Applied in a tropical case study context, the framework enables probabilistic assessment of land allocation strategies and supports more robust decision-making in estate crop planning. By decoupling suitability modelling from deterministic economic assumptions, this approach enhances the transparency, flexibility, and realism of land use evaluation. The integration of spatial MCDA and stochastic simulation demonstrates a transferable method for supporting land use decisions in data-limited but uncertainty-prone environments.
This study introduces a novel methodology for the automatic generation of LOD2 3D building models for the historical city of Olomouc (Czechia) using Esri CityEngine. The methodology was demonstrated through three sample areas within Olomouc, addressing key challenges in urban modelling such as roof type classification, building height estimation, and the procedural generation of detailed roof structures. By integrating LiDAR data, orthoimagery, and building footprint datasets, the approach produces accurate spatial representations adaptable to diverse urban settings and represents a new approach tailored to historical Central European cities. The approach explicitly accounts for irregular parcels, heterogeneous roof geometries, and complex reconstruction patterns typical of such historical urban environments. A reusable Computer-Generated Architecture (CGA) script was developed to streamline the modelling process. The study includes an accuracy assessment to validate the reliability of the methodology. Accuracy assessment demonstrated the high reliability of the approach, with median-based ridge and eave height estimation achieving RMSE values of below 0.5 m in residential areas and approximately 1 m in rural areas. Roof type classification achieved accuracies of up to 0.89 for flat and 0.87 for gable roofs, although the accuracy for hip roofs remained lower. The outcome of the proposed approach is a processing pipeline created using Esri Tasks, a workflow automation tool in ArcGIS Pro that guides users through predefined steps, enabling them to apply the methodology to other cities and contribute to novel, scalable solutions, advanced GIS workflows, and enhanced urban planning.
In April 2024, the United Arab Emirates (UAE) experienced its heaviest rainfall in 75 years, which triggered widespread urban flooding and prolonged disruptions. This study assesses the spatial impact and post-rainfall recovery of the event using the U-Net deep learning architecture on daily PlanetScope satellite imagery captured on a pre-rainfall date and on a series of post-rainfall dates between 14 and 27 April 2024. Multi-temporal land use and land cover (LULC) classifications were generated using a U-Net model trained via transfer learning, and the LULC categories were water, vegetation, built area, and bare ground. The multi-temporal LULC classifications were further applied for change detection analyses to map flooded areas and track temporal recovery across LULC categories. The U-Net model for LULC classification achieved over 95% overall accuracy and a Kappa statistic of 0.927. The change detection and inundation recovery showed that approximately 23.8 km² —nearly 10 times the area of Downtown Dubai—was flooded. The flood affected the built area and bare ground most, while vegetation exhibited higher flood resilience. Although rainfall ended by April 17, 95% of flooded areas remained submerged three days later, and 37% were still underwater by day ten. These findings reveal the limitations of urban drainage systems in Dubai and the value of high-temporal remote sensing and deep learning for flood monitoring. This study offers a practical and replicable framework to support urban flood risk assessment, resilience planning, and climate adaptation in the Persian Gulf region, characterized by rapid urbanization and fragile arid environments similar to Dubai.
The relationship between democracy and economic growth has been widely debated since the 1980s, yet economists have not reached a consensus on its impact. This study examines this relationship using a spatial econometric approach across 100 countries from 1990 to 2022, incorporating both economic factors and spatial dynamics, such as geographical proximity and democratic context. A key innovation is the concept of institutional proximity, which posits that countries with similar institutional frameworks experience similar economic growth outcomes. The study provides strong evidence of positive growth spillovers from democracy, where democratic governance in one country enhances economic performance in other democratic nations, highlighting the interconnectedness of political and economic systems. Additionally, it uncovers spillover effects in which democratic countries transfer technology to less democratic regions, thus fostering global economic advancement. Notably, these effects extend beyond geographical proximity, with countries such as Australia, New Zealand, and Japan benefiting from democracy’s influence despite being distant from the epicentres of democracy. This challenges the notion that democracy’s impact is geographically confined, demonstrating its potential to drive global growth across national borders.
The problem of data-driven location prediction of individual users is based on an effective mining of travel behaviours and motion patterns. In this sense, a central aspect refers to the use of a relevant amount of historical mobility data, required for successfully training machine learning models, especially when involving artificial neural networks. However, such data are sensitive in nature, therefore not easily available and always subjected to privacy-related restrictions on their public share. With the purpose of merging information from different providers without directly sharing geo-private data, we hereby assess the feasibility of decentralized training over multiple data sources, leveraging different unmergeable trajectory datasets stored in separate servers. In particular, we integrate a long short-term memory (LSTM) recurrent neural network framework for location prediction into a federated learning environment, whereby local workers compute the network operations on their data share, and the learning results are progressively synchronized with a parameter server. Variants of federated algorithms are evaluated and compared to separate independent training processes (lower benchmark) and an ideal, but in fact not allowed, centralized training (upper benchmark). By leveraging real-world datasets of sparse non-repetitive mobility traces, our experiments aim to disclose insights on federated learning strategies for advanced trajectory analytic tasks, paving the way to decentralized applications involving multiple geo-private data sources.
To address the challenges of accurately extracting target features from complex scenes in UAV remote sensing imagery and the susceptibility of small objects to being obscured by noise, this paper proposes a lightweight detection algorithm, RE-YOLO, based on YOLOv8n. First, a multi-scale convolutional module named RFCSConv, which integrates channel and spatial attention mechanisms based on Receptive Field Attention Convolution (RFAConv), replaces the original convolution layers. This enhances feature selection and fusion at multiple scales. Second, the Efficient Squeeze-and-Excitation Module (ESEModule) is introduced into the backbone to strengthen feature representation while reducing computational overhead. Lastly, a composite loss function called Win-IoU, combining Wise-IoU (WIoU) and Inner-IoU, is proposed to dynamically adjust gradient contributions based on anchor quality. Experimental results on the VisDrone2019 dataset demonstrate that RE-YOLO achieves 29.7% mAP@0.5 with only 3.2MB of parameters and a real-time speed of 150 FPS. The algorithm also generalizes well across the HRSID and CARPK datasets, achieving 91.8% and 94.3% mAP@0.5 respectively.
