Search Results (988)

The world evolves continuously across space and time. Massive volumes of data are generated through sensing, simulation, remote observation, and human activities, capturing dynamic processes in environmental, social, economic, and engineered systems. Critical insights are embedded within these large-scale spatiotemporal datasets. Spatiotemporal Data Science provides a conceptual and methodological framework for analyzing such data by integrating spatiotemporal thinking, computational infrastructure, artificial intelligence, and domain knowledge. The field advances methods for data acquisition, harmonization, modeling, visualization, and decision support, enabling applications in natural disaster response, public health, climate adaptation, infrastructure resilience, and geopolitical analysis. By leveraging emerging technologies—including generative Artificial Intelligence (AI), large-scale cloud platforms, Graphics Processing Unit (GPU) acceleration, and digital twin systems—Spatiotemporal Data Science enables scalable, interoperable, and solution-oriented research and innovation. It represents a critical frontier for scientific discovery, engineering advancement, technological innovation, education, and societal benefit. Spatiotemporal Data Science is a transdisciplinary field that studies and models dynamic phenomena across space and time by integrating spatial theory, temporal reasoning, artificial intelligence, and scalable computational infrastructure. It enables the development of adaptive, predictive, and increasingly autonomous systems for understanding and managing complex real-world processes. Full article

Dirofilaria repens is the leading cause of subcutaneous (dogs) and subcutaneous/ocular dirofilariosis (humans) in the Old World. Despite its rapid geographical spread, driven by climate change, the emergence of new invasive vectors (Aedes albopictus) and growing interest in its study due to the emergence of new cases in areas previously free of the parasite, amongst other factors, scientific research into this pathogen remains limited. This study provides the first longitudinal bibliometric analysis of global research on D. repens (1955–2025). Data from Web of Science and Scopus were processed using PRISMA and RAMIBS protocols, resulting in a normalized corpus of 624 documents analyzed via science mapping techniques. The field exhibits a sustained annual growth rate of 3.79%, transitioning into an exponential expansion phase in 2011. While Italy retains historical leadership, spatial analysis confirms a research displacement towards Central and Eastern Europe (Germany, Poland). Thematic evolution reveals a structural shift from isolated clinical case reports to a multidisciplinary ecosystem dominated by molecular epidemiology, vector competence, and surveillance. Dirofilaria repens has gone from being a minor and neglected issue to having a significant number of reports and studies subject to interest in addressing the disease that results from its infection in different hosts. However, the intellectual structure exposes an operational fragmentation between clinical medicine and medical entomology. Future research must overcome national silos and integrate reservoir management with vector control, transforming the current reactive approach into a predictive preventive system aligned with the One Health framework. Full article

Variety Dynamics (VD) is a new methodology to identify reasons for failures in spatial data infrastructure (SDI) governance and standardisation as well as potential opportunities for improvement. SDI governance and standardisation situations are often shaped by multiple feedback loops and do not conform to the assumptions needed for causal analysis. This combination is an intrinsic basis for faulty decision and policy making. Variety Dynamics presents geographic information science with a new ability to address the above issues and reveal otherwise hidden structural factors. It shows that most SDI initiatives for change are ineffective because they do not influence variety distributions. Standards are published, coordinating bodies established, and technical platforms deployed without significant changes in equitable outcomes. Variety Dynamics also reveals opportunities for successful SDI policy initiatives leveraging data sovereignty changes that force infrastructure migration and temporarily invert transaction cost structures. After data sovereignty is established, however, any SDI governance and standardisation problems will be likely locked in through path dependencies and accumulated switching costs. Full article

Urban mobility is a central challenge for sustainable and inclusive cities, as climate change, congestion, and spatial inequality increasingly reveal mobility patterns as expressions of deeper social and spatial structures. Inclusive urban mobility examines whether transport systems equitably support the everyday movements and accessibility needs of historically marginalized and underserved populations. The integration of artificial intelligence with geographic information science, combined with multimodal geospatial data fusion, provides powerful tools to diagnose and address these disparities by integrating heterogeneous data sources such as satellite imagery, GPS trajectories, transit records, volunteered geographic information, and social sensing data into scalable, high-resolution urban mobility analytics. This paper presents a systematic survey of recent GeoAI studies that fuse multiple geospatial data modalities for key urban mobility tasks, including accessibility mapping, demand forecasting, and origin–destination flow prediction, with particular emphasis on inclusive and equity-oriented applications. The review examines 18 multimodal GeoAI studies identified through a PRISMA-ScR screening process from 57 candidate publications between 2019 and 2025. The survey synthesizes methodological trends across data-, feature-, and decision-level fusion strategies, highlights the growing use of deep learning architectures, and examines emerging techniques such as knowledge graphs, federated learning, and explainable AI that support equity-relevant insights across diverse urban contexts. Building on this synthesis, the review identifies persistent gaps in population coverage, multimodal integration, equity optimization, explainability, validation, and governance, which currently constrain the inclusiveness and robustness of GeoAI applications in urban mobility research. To address these challenges, the paper proposes a structured research roadmap linking these gaps to concrete methodological and governance directions including equity-aware loss functions, adaptive multimodal fusion pipelines, participatory and human-in-the-loop workflows, and urban data trusts to better align multimodal GeoAI with the goals of inclusive, just, and sustainable urban mobility systems. Full article

Mobility of the migrant population plays a crucial role in shaping urban spaces, neighbourhood change and socio-economic development. While extensive research has been conducted on the spatio-temporal dynamics of migration in large metropolitan areas, there remains a notable lack of understanding of the impact of migration on medium-sized cities, on their internal spatial distribution and socio-spatial differentiation. This study aims to fill this gap by examining the urban settlement patterns of migrants in two medium-sized Danish cities: Aalborg and Odense. The research explores the intra-urban spatial distribution of various migrant groups, considering their origins and residential preferences. Additionally, it analyses the social and structural pull-factor proxies that influence these patterns, including urban housing market dynamics and access to amenities and services. Through an exploratory spatial analysis and data visualisation approach, this study reveals detailed insights into the determinants of migrant settlement. The findings indicate a significant intra-urban concentration of certain migrant groups, especially in the city centres, which often correspond to areas with a higher concentration of essential amenities. By focusing on mid-sized cities and adopting a case-based, comparative methodology through an extensive data visualisation approach, this research enhances urban science knowledge by illuminating underexplored urban contexts and providing a fresh view on the interplay between migration, urban development and spatial planning in medium-sized cities. Full article

Urban green space (UGS) is a critical component of sustainable cities and a modifiable determinant of mental health (MH). This review synthesizes 93 empirical studies and 929 bibliometric records to map theoretical advances, methodological evolution, and governance implications in the UGS–MH field. We integrate the following six validated pathways into a unified socio-ecological framework: attention restoration, stress recovery, behavioral activation, physiological regulation, social cohesion, and environmental buffering. Methodological trends indicate a shift from static greenness proxies to street-view and multimodal exposure measures, and from cross-sectional correlations to models that address spatial heterogeneity, causal identification, and AI-enabled prediction. Bibliometric mapping reveals increasing interdisciplinarity, geographic diversification, and growing attention to dynamic exposure science. Persistent challenges include spatial and temporal misalignment between exposure and outcome measures, reliance on single-modality indicators, limited causal inference, and constrained cross-cultural generalizability. Building on these findings, we propose a governance-oriented framework to support sustainable and healthy cities through equitable green access, behavior-informed planning, nature-based interventions, and data-driven decision support. Overall, this review strengthens the bridge from evidence to action at the interface of urban sustainability and population mental health. Full article

The integration of Geographic Information Systems (GISs) with hydrologic science has evolved over seven decades from manual catchment delineation and output visualization to AI-native spatial water intelligence, reshaping how the water cycle is observed, modeled, and managed. This review explores that evolution, from the progressively tightening coupling between GIS software and hydrologic models to an AI-assisted future in which the line between these two fields blurs and eventually dissolves completely. The evolution of GISs in hydrology is traced through four eras, stratified as: (1) the formalization of governing equations and digital terrain representations (1950–1985); (2) the initial GIS–model coupling era and the rise in watershed simulation (1985–2000); (3) open source and the start of the open data deluge (2000–2015); and (4) machine learning and cloud-native computing (2015–present). A four-level vision for the role of artificial intelligence in the next generation of spatial hydrology is then articulated, from AI-assisted GIS operation to spatially aware AI water intelligence that reasons directly over geospatial data without requiring a traditional GIS or simulation software as an intermediary. Broader limitations and challenges are also discussed. Full article

Climate change and rapid urbanization are intensifying flood risks in China, particularly in regions with complex terrain and dense populations. Traditional risk assessment methods often lack the flexibility to handle uncertainties in multi-dimensional risk systems. This study proposes a probabilistic flood risk assessment framework integrating Monte Carlo simulation with a composite indicator system from the perspective of disaster system theory. Taking Hunan Province as a case study, we constructed a hierarchical indicator system encompassing environmental susceptibility, hazard intensity, exposure vulnerability, and mitigation capacity. The analytic hierarchy process (AHP) and coefficient of variation (CV) methods were combined for indicator weighting, and Monte Carlo simulation was employed to quantify uncertainties and classify risk levels. Results reveal significant spatial heterogeneity in flood risk across the province, with high-risk areas concentrated in regions exhibiting intense rainfall, dense river networks, and insufficient mitigation infrastructure. The study provides a transferable, data-driven approach for spatially explicit flood risk zoning, offering evidence-based insights for land-use planning, resilient infrastructure development, and sustainable flood governance. This research contributes to the integration of probabilistic modeling into land system science, supporting disaster risk reduction and climate adaptation strategies aligned with SDG 11. This study also provides policy-relevant insights for regional flood governance by supporting risk-informed land-use planning, targeted infrastructure investment, and adaptive flood management strategies, thereby contributing to more resilient and sustainable land system development under increasing climate uncertainty. Full article

Zenith Tropospheric Delay (ZTD) and its associated atmospheric water vapor information constitute essential environmental variables for Earth observation (EO)-based atmospheric monitoring and environmental variable retrieval. High-quality ZTD products are therefore of great importance for the post-processing, refinement, and reconstruction of atmospheric environmental variables at regional scales. Among existing observation techniques, Global Navigation Satellite System (GNSS) measurements provide high-precision ZTD estimates and have become an important means for retrieving tropospheric delay and water vapor. However, the sparse and uneven spatial distribution of GNSS stations limits their direct applicability for continuous environmental monitoring. Reanalysis-based products, such as ERA5 provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), offer EO big data with excellent spatiotemporal continuity but suffer from pronounced systematic biases compared to precision GNSS retrievals, restricting their direct use in high-accuracy regional applications. To address these limitations, this study proposes a Residual Correction Kriging method for ZTD (RK ZTD) that integrates GNSS ZTD and ERA5 ZTD grids through a multi-source data fusion framework. High-precision GNSS ZTD is treated as reference data, and the differences between GNSS ZTD and ERA5 ZTD at modeling stations are defined as residuals to characterize the systematic bias in ERA5 ZTD grids. A Kriging interpolation algorithm is then employed to model the spatial distribution of these residuals and generate residual correction grids. By superimposing the interpolated residual grids onto the ERA5 ZTD grids, a refined and high-precision regional ZTD product is reconstructed. Experiments were conducted using observations collected in 2023 from 36 GNSS stations in the Netherlands, including 10 modeling stations and 26 independent validation stations, together with concurrent ERA5-derived ZTD grids. The results demonstrate that the proposed RK ZTD model provides spatially robust and high-precision ZTD products across the study region. The RK ZTD achieves a Root Mean Square Error (RMSE) of 5.70 mm, representing improvements of 58.4% and 35.4% compared with the original ERA5 ZTD (13.69 mm) and the GNSS-Kriging ZTD (8.82 mm), respectively. Moreover, the absolute bias is reduced to 0.41 mm, in contrast to 5.15 mm for the ERA5 ZTD, indicating that systematic biases are effectively mitigated. Spatial and seasonal analyses further confirm that the proposed method maintains stable performance across all seasons and significantly alleviates interpolation inaccuracies caused by sparse GNSS stations, even under extreme weather conditions such as Storm Ciarán, proving its value for advanced Earth environmental science applications. Full article

Background/Objectives: Cardiac magnetic resonance (CMR) imaging at 7 Tesla provides a substantially higher intrinsic signal-to-noise ratio compared with conventional 1.5 T and 3 T systems, potentially enabling higher spatial resolution, improved tissue contrast, and advanced metabolic imaging. However, clinical translation remains limited by technical challenges associated with ultra-high-field operation. This systematic review aimed to synthesize current human in vivo evidence on the feasibility, applications, and methodological limitations of 7-T cardiovascular MRI. Methods: A PRISMA-guided systematic search of PubMed, Cochrane Library, Web of Science, and Scopus was conducted from database inception through January 2025. Studies reporting human in vivo cardiovascular MRI at 7 Tesla were included. Data regarding study design, sample characteristics, imaging applications, feasibility, quantitative findings, and reported limitations were extracted and qualitatively synthesized. Results: Sixty-five studies met inclusion criteria, predominantly small prospective cohorts (mean sample size = 13), largely involving healthy volunteers. Across diverse applications—including coronary MR angiography, cine imaging, valvular assessment, vascular imaging, flow quantification, myocardial tissue characterization, and multinuclear (³¹P, ²³Na, ³⁹K) imaging—7-T CMR was consistently feasible and capable of producing high-quality images. Quantitative ventricular and vascular measurements were generally concordant with lower field strengths. Incremental benefits were most apparent in high-resolution structural imaging and metabolic applications, whereas routine functional and flow assessments showed limited additional advantages. No serious adverse events were reported. Conclusions: Human cardiovascular MRI at 7 Tesla represents a technically feasible research and early translational platform with selective advantages over established field strengths. Further advances in radiofrequency technology, protocol harmonization, and larger disease-focused studies are required to clarify its potential clinical role. Full article

In response to land-use and land-cover (LULC) changes in South Africa, which have varied effects on biodiversity, several studies have characterized LULC changes using remote sensing data due to its cost-effectiveness, repetitiveness, spatial coverage and flexibility. However, the geotemporal and methodological characteristics of these studies remain relatively unknown. In this regard, we review remote sensing-based studies conducted in South Africa using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). From the 343 articles retrieved from Web of Science, Google Scholar, and Scopus databases, 103 studies were eligible for analysis. The analysis showed that (a) various remote sensing datasets were increasingly and effectively used to characterize LULC in South Africa over the period 2001–2024, primarily Landsat data with integration of various advanced classification algorithms; (b) most studies were conducted in the eastern seaboard, particularly in the Maputaland–Pondoland–Albany hotspot and highveld to the north, and (c) much research dealt with issues pertaining to “pristine class” conversion to urban area and other human-induced activities, mainly in biodiversity-rich landscapes. Overall, LULC studies achieved consistently reliable accuracies, largely using publicly available geospatial datasets, thereby creating an accessible foundation for all researchers. LULC research is expected to increase as conservation efforts strengthen amid ongoing developments in South Africa. Full article

Hyperspectral remote sensing (HRS) is gaining widespread adoption within the geoscience and Earth observation communities. It fosters diverse applications, including precision agriculture, soil science, mineral exploration, and carbon detection, to name a few. Recent technological advancements facilitated a growing number of satellite missions as well as an increase in the availability of commercial sensors and platforms, such as drones. A significant challenge in deploying the varied platforms and sensors is the design and optimization of the hyperspectral surveys. Forward modelling simulators are valuable for optimizing mission parameters and estimating imaging performance. Limited accessibility of open-source simulators presents an obstacle for users who seek to benefit from such tools. To bridge this gap, HySIMU (Hyperspectral SIMUlator) was developed and described herein. It is an open-source, forward modelling toolkit that combines and integrates a primary processing pipeline with various open-source packages into a transparent and modular workflow. It offers a cost-effective approach to evaluating the performance of hyperspectral surveys. HySIMU is designed to simulate hyperspectral imagery based on user-defined targets, platforms, and sensor parameters. Features include (i) a ground truth data cube builder for customizable input parameters, (ii) a terrain-based solar and view geometry calculator for illumination modelling, (iii) integrated open-source radiative transfer models for incorporating atmospheric effects, and (iv) spatial resampling filters. In this manuscript, the initial framework for HySIMU is presented with some example applications, including two validation studies with real hyperspectral images. As remote sensing technologies advance, forward modelling toolkits such as HySIMU play a crucial role in refining mission designs and assessing survey feasibility. The scalability for arbitrary hyperspectral sensors, platforms, and spectral libraries ensures broad applicability. Of particular importance is support for parameter optimization for both scientific and commercial HRS campaigns. Full article

Understanding how climate and land use interact to shape freshwater quality remains challenging across heterogeneous river basins. This study analysed monthly citizen-science measurements of nitrate (NO₃), phosphate (PO₄), and turbidity, collected between 2016 and 2024, across seven Italian river basins representing contrasting climatic and land-use contexts. A non-parametric analytical framework combining Kruskal–Wallis tests, aligned rank transform analyses, principal component analysis (PCA), and basin-specific Somers’ D statistics was applied to ordinal concentration data. Significant differences among basins revealed persistent spatial structuring of water-quality regimes. PCA identified two largely independent gradients: a dominant nutrient axis defined by NO₃ and PO₄, and a secondary turbidity axis. Urban and industrial land use aligned with higher nutrient categories, while vegetated landscapes were associated with lower concentrations. Climatic effects were basin specific. Precipitation showed opposing relationships with NO₃, suggesting both mobilisation and dilution processes, whereas temperature was positively associated with PO₄ in several basins and negatively related to NO₃. Turbidity displayed variable links with precipitation and temperature, reflecting hydrological and seasonal controls. Overall, results indicate that land use represents the primary structural driver of nutrient variability, while climatic factors modulate basin-specific responses. The integration of citizen science observations with robust non-parametric approaches provides a scalable framework for detecting environmental drivers and supporting the targeted management of Mediterranean river systems. Full article

As a typical example of a high-density city, Macau’s medical resource allocation system, a key component of the city’s complex socio-technical system, suffers from significant spatial imbalances, which restricts the overall effectiveness of the medical service system. Based on the perspective of systems science theory, regards the allocation of medical resources as a dynamic system with multiple coupled factors. It comprehensively utilizes systems research methods such as POI data mining and space syntax analysis and employs techniques such as kernel density analysis and spatial structure coupling models to systematically evaluate the spatial structure, resource accessibility, and service balance of Macau’s medical service system. It found that (1) the Macau Peninsula has concentrated core medical resources, such as the Conde de São Januário Hospital (CHCSJ) and Kiang Wu Hospital, which form a core subsystem with high service saturation. Excessive concentration of resources has led to high concentration of a certain type of facility. (2) Taipa Island and the Cotai Reclamation Area have created an extended subsystem of medical resources along with urban development. However, the northern area does not have enough facilities, and its internal structure is not balanced. (3) Coloane Island has only basic health stations remaining, forming a marginal subsystem with scarce medical resources, which has a significant hierarchical gap with the core and extended subsystems. This spatial pattern of “saturated Macau peninsula, expanded Taipa Island, and sparse Coloane Island” is essentially a concrete manifestation of the imbalance between the medical resource allocation system and the urban spatial development system. Therefore, based on system optimization theory, it proposes constructing a multi-level, networked spatial system for medical facilities to promote the coordinated operation of various regional medical subsystems and achieve overall functional optimization and a balanced layout for Macau’s medical service system. This research analyzes the imbalance mechanism of high-density urban public service systems using systems science methods, providing not only a scientific basis for the precise optimization of Macau’s medical resource allocation system but also a practical reference for the planning and governance of similar high-density urban public service systems under a systems thinking framework. Full article

Video-based photogrammetric reconstruction is increasingly used when high-resolution still images are unavailable. However, limited spatial resolution, compression artifacts, and motion blur often reduce geometric accuracy. Recent advances in learning-based image super-resolution (SR) offer a promising preprocessing method, but their geometric reliability within photogrammetric workflows remains not well understood. This study provides a controlled quantitative evaluation of learning-based super-resolution for video-based 3D reconstruction. Low-resolution video frames are enhanced using two representative methods: an open-source real-world SR model (Real-ESRGAN ×4) and a commercial solution (Topaz Video AI ×4). All datasets are processed with the same Structure-from-Motion and Multi-View Stereo pipelines and tested against terrestrial laser scanning (TLS) reference data. Results show that super-resolution significantly increases reconstruction density and improves the recovery of fine-scale surface details, while also leading to greater local surface variability compared with reconstructions from the original video; photogrammetric stability remains consistent despite these changes. The findings highlight a fundamental trade-off between reconstruction completeness and local geometric accuracy and clarify when enhanced video imagery via super-resolution can be a reliable source for 3D reconstruction. These results are especially important for spatial data science workflows and AI-powered 3D modeling and digital twin applications. Full article