Search Results (156)

Ecological security evaluation serves as the cornerstone for ecological management decision-making and spatial optimization. This study focuses on the Dongping Lake Basin. Based on the Pressure–State–Response (PSR) model framework, it integrates ecological risk, ecosystem health, and ecosystem service indicators. Utilizing methods including Local Indicators of Spatial Association (LISA), Transition Matrix, and GeoDetector, it analyzes the spatio-temporal evolution characteristics and driving mechanisms of watershed ecological security from 2000 to 2020. The findings reveal that the Watershed Ecological Security Index (WESI) exhibited a trend of “fluctuating upward followed by periodic decline”. In 2000, the status was “relatively unsafe”. It peaked in 2015 (index 0.332, moderately safe) and experienced a slight decline by 2020. Spatially, a significantly clustered pattern of “higher in the north and lower in the south, higher in the east and lower in the west” was observed. In 2020, “High-High” clusters of ecological security aligned closely with Shandong Province’s ecological conservation red line, concentrating in core protected areas such as the foothills of the Taihang Mountains and Dongping Lake Wetland. Level transitions were characterized by “predominant continuous improvement in low levels alongside localized reverse fluctuations in middle and high levels,” with the “relatively unsafe” and “moderately safe” levels experiencing the largest transfer areas. Geographical detector analysis indicates that the Human Interference Index (HI), Ecosystem Service Value (ESV), and Annual Afforestation Area (AAA) were key drivers of watershed ecological security change, influenced by dynamic interactive effects among multiple factors. This study advances watershed-scale ecological security assessment methodologies. The revealed spatio-temporal patterns and driving mechanisms provide valuable insights for protecting the ecological barrier in the lower Yellow River and informing ecological security strategies within the Dongping Lake Watershed. Full article

This study aimed at proposing a predictive model for real-time monitoring of epidemic dynamics at the municipal scale in Lombardy region, in northern Italy, leveraging Emergency Medical Services (EMS) dispatch data and Geographic Information Systems (GIS) methodologies. Unlike traditional epidemiological models that rely on official diagnoses and offer limited spatial granularity, our approach uses EMS call data (rapidly collected, geo-referenced, and unbiased by institutional delays) as an early proxy for outbreak detection. The model integrates spatial filtering and machine learning (random forest classifier) to categorize municipalities into five epidemic scenarios: from no diffusion to active spread with increasing trends. Developed in collaboration with the Lombardy EMS agency (AREU), the system is designed for operational applicability, emphasizing simplicity, speed, and interpretability. Despite the complexity of the phenomenon and the use of a five-class output, the model shows promising predictive capacity, particularly for identifying outbreak-free areas. Performance is affected by changing epidemic dynamics, such as those induced by widespread vaccination, yet remains informative for early warning. The framework supports health decision-makers with timely, localized insights, offering a scalable tool for epidemic preparedness and response. Full article

Identifying correspondences of road segments in different road networks, namely road-network matching, is an essential task for road network-centric data processing such as data integration of road networks and data quality assessment of crowd-sourced road networks. Traditional road-network matching usually relies on feature engineering and parameter selection of the geometry and topology of road networks for similarity measurement, resulting in poor performance when dealing with dense and irregular road network structures. Recent development of graph neural networks (GNNs) has demonstrated unsupervised modeling power on road network data, which learn the embedded vector representation of road networks through spatial feature induction and topology-based neighbor aggregation. However, weighting spatial information on the node feature alone fails to give full play to the expressive power of GNNs. To this end, this paper proposes a Spatial Pattern-aware Graph EMbedding learning method for road-network matching, named SP-GEM, which explores the idea of spatially-explicit modeling by identifying spatial patterns in neighbor aggregation. Firstly, a road graph is constructed from the road network data, and geometric, topological features are extracted as node features of the road graph. Then, four spatial patterns, including grid, high branching degree, irregular grid, and circuitous, are modelled in a sector-based road neighborhood for road embedding. Finally, the similarity of road embedding is used to find data correspondences between road networks. We conduct an algorithmic accuracy test to verify the effectiveness of SP-GEM on OSM and Tele Atlas data. The algorithmic accuracy experiments show that SP-GEM improves the matching accuracy and recall by at least 6.7% and 10.2% among the baselines, with high matching success rate (>70%), and improves the matching accuracy and recall by at least 17.7% and 17.0%, compared to the baseline GNNs, without spatially-explicit modeling. Further embedding analysis also verifies the effectiveness of the induction of spatial patterns. This study not only provides an effective and practical algorithm for road-network matching, but also serves as a test bed in exploring the role of spatially-explicit modeling in GNN-based road network modeling. The experimental performances of SP-GEM illuminate the path to develop GeoEmbedding services for geospatial applications. Full article

As ecologically sensitive interfaces shaped by the interplay of land and sea, coastal zones demand close attention. Uncovering the spatiotemporal evolution of ecosystem service value (ESV) and the intricate interrelations among ecosystem service (ES) functions is imperative for the informed governance of human–land interactions and for fostering sustainable regional development. This study analyzes the spatiotemporal evolution of ESV based on the modified equivalent factor table, combining the Geo-information Tupu, Markov transfer model, and standard deviation ellipse. Additionally, we introduce an ecosystem service tradeoff degree (ESTD) to assess the tradeoffs and synergies among various ESs, and we utilize GeoDetector to elucidate the driving forces behind the spatial disparities in ESV. Our findings reveal that (1) Although the land use composite index in the Liaoning coastal economic belt (LCEB) increased, the pace of land use transformation demonstrated a trend toward stabilization over the study duration. (2) Between 2000 to2020, ESV initially declined but subsequently experienced an upward rebound, resulting in a net gain of approximately 48 billion yuan. Spatial analysis indicated continuous enlargement of the standard deviation ellipse, with its centroid consistently located within Yingkou City and a gradual directional shift toward the southwest. (3) The dominant relationship among ESs showed synergy, with notable tradeoffs between hydrological regulation and other services. (4) Topography and climate factors were the primary drivers of spatial heterogeneity of ESV in the LCEB. The research provides spatial decision support for optimizing the ecological security pattern of the coastal zone. Full article

The occurrence of illegal waste activities is a worldwide problem, due to improper actions and inadequate services across many territories. Geographical Information Systems (GISs) software plays a crucial role in optimizing waste management and determining the shortest route paths for waste transportation. This work focuses on the development of a GIS-based workflow for the detection of Illegal Abandoned Waste Sites (IAWSs) and waste management planning. The integration of remote/ground sensing activities, geospatial data, and models within a GIS framework is a useful practice for conducting cost analysis and supporting the development of efficient waste management plans. Firstly, available satellite images are employed in a baseline assessment, combining ancillary and remote sensing data. As a result of satellite monitoring, a ground-piloted survey is carried out by checking the potential-IAWSs density map retrieved from the satellite pre-recognition phase. Hence, a total of 171 ground points are geo-localized and spatialized, according to qualitative on-site products and 2.5D volume analysis. Consequently, distances from illegal dumping sites to proper disposal plants are calculated, achieving the shortest route paths as geospatial information. From these data, a Functional Unit (FU) of 1 ton of mixed waste plus 381.6 kg of inert material is determined, a fundamental stage for comparing different cost analysis processes in similar contexts. By using a GIS-based workflow, a cost analysis assessment is provided, aiming to support principal activities such as waste transportation and disposal to the proper plant (e.g., landfill or incineration). In conclusion, spatial data analysis results are fundamental in managing illegal abandoned waste sites, helping to establish a cost analysis assessment. Full article

The development of restorative tourism in post-war countries is crucial to economic recovery, cultural preservation, and social stabilization. While various nations have adopted different reconstruction strategies following conflicts, Ukraine’s situation requires an innovative and large-scale approach due to the extensive damage inflicted on infrastructure, cultural heritage, and tourism assets. This study explores the role of virtual and augmented reality technologies in restoring tourism potential, particularly in preserving destroyed cultural heritage through digitalization. Virtual tourism is increasingly relevant to maintaining cultural identity, attracting investment, and fostering international engagement. This study examines the evolution of digital tourism solutions, consumer behaviour shifts towards online leisure, and the integration of geoinformation systems for post-crisis planning. The findings emphasize that Ukraine’s tourism sector must adapt to digital trends while developing physical infrastructure, ensuring a comprehensive, resilient, and future-oriented restoration strategy. This study provides recommendations for leveraging innovation in post-crisis tourism development. It explains how the change in the paradigm of consumption of recreation and leisure services in the modern world impels the restoration of the destroyed tourism infrastructure. Furthermore, it highlights the importance of strategic migration policies to rebuild the labour market, which is essential for sustainable recovery. Full article

The traditional construction industry has predominantly relied on labor-intensive methods, resulting in significantly lower efficiency and productivity compared to other industries. According to a report by the Korea Productivity Center (KPC), the productivity of the construction industry is approximately 24.5% lower than that of the manufacturing sector and 15.7% lower than that of the service sector, highlighting a significant productivity gap. To enhance efficiency and productivity in the construction sector, the South Korean government, led by the Ministry of Land, Infrastructure, and Transport, has announced a policy aimed at achieving 100% adoption of smart construction technologies by 2025. In this paper, we propose a methodology for standardizing the format of underground utilities plan drawings by incorporating 3D coordinates, shapes, and attribute information to facilitate the digital transformation of construction site data. Furthermore, we introduce a standardized approach for extracting data from these drawings and converting them into 3D spatial data in the GeoJSON (Geographic JavaScript Object Notation) format. The experimental results of the technology for processing structured drawings into 3D spatial data demonstrated that all data were successfully converted without any omissions. Full article

Community-based care services offered by senior centers are vital for supporting older adults’ independent living. The number of senior centers has escalated in China in recent years. Despite scholarly interest in the potential accessibility of senior centers, research on older adults’ realized access remains scarce. Using the geo-referenced check-in data of 2382 users of senior centers in Nanjing, China, this study aims to fill this gap by examining the disparities between older adults’ potential and realized access to senior centers and the influence of multilevel spatial and non-spatial factors. This study indicates that potential access is often significantly overestimated compared with the actual accessibility of senior centers, with older adults’ distances of realized access (mean = 1319 m) being considerably greater than potential access (mean = 325 m). Spatial and regression analyses confirm that older adults living in newly built, lower-priced houses in the inner city are more likely to travel longer distances to reach senior centers. Spatial proximity is less effective in predicting realized access for those living further from senior centers. Instead, the location and service quality of senior centers play a more prominent role. These findings enrich our understanding of older adults’ access to community-based care, informing planning and policy interventions for the development of age-friendly communities. Full article

As a vital part of the geo-environment and water cycle, ecosystem health and human development are dependent on water resources. Water supply and demand are influenced significantly by land use and cover change (LUCC) which shapes the surface ecosystems by altering their structure and function. Under future climate change scenarios, LUCC may greatly impact regional water balance, yet the impact is still not well understood. Therefore, examining the spatial relationship between LUCC and water yield services is crucial for optimizing land resources and informing sustainable development policies. In this study, we focused on the Hanjiang River Basin and used the patch-generating land use simulation (PLUS) model, coupled with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, to assess water yield services under three Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios. For the first time, we considered the impact of future changes in socio-economic and water use indicators on water demand using correction factors and ARIMA projections. The relationship between water supply and demand was explored using this approach, and LUCC’s effects on this balance are also discussed. Results indicate that: (1) The patterns of LUCC are similar for the three scenarios from 2030 to 2050, with varying levels of decrease for cropland and significant growth of built-up areas, with increases of 6.77% to 19.65% (SSP119), 7.66% to 22.65% (SSP245), and 15.88% to 46.69% (SSP585), respectively, in the three scenarios relative to 2020; (2) The future supply and demand trends for the three scenarios of produced water services are similar, and the overall supply and demand risks are all on a downward trend. Water demand continues to decline, and by 2050, the water demand of the 3 scenarios will decrease by $96.275\times {10}^8\mathrm{t}$, $81.210\times {10}^8\mathrm{t}$, and $84.13\times {10}^8\mathrm{t}$ relative to 2020, respectively; while supply decreases from 2030 to 2040 and rises from 2040 to 2050; (3) Both water supply and demand distributions exhibit spatial correlation, and the distribution of hotspots is similar. The water supply and demand are well-matched, with an overall supply-demand ratio greater than 1.5; (4) LUCC can either increase or decrease water yield. Built-up land provides more water supply compared to other land types, while forest land has the lowest average water supply. Limiting land use type conversions can enhance the water supply. Full article

Efforts to establish Pakistan’s National Spatial Data Infrastructure (NSDI) have been underway for the past 15 years, and therefore it is necessary to gauge the current progress to channelize efforts into areas that need improvement. This article assessed Pakistan’s NSDI implementation efforts through well-established approaches, including the SDI readiness model, organizational aspects, and state of play. The data were collected from Spatial Data Infrastructure (SDI) and Geographic Information System (GIS) experts. The findings underscored challenges related to human resources, SDI education/culture, long-term vision, lack of awareness of geoinformation (GI), sustainable funding, metadata availability, online geospatial services, and geospatial standards hindering NSDI development in Pakistan. However, certain factors exhibit favorable standings, such as the legal framework for NSDI establishment, web connectivity, geospatial software availability, the unavailability of core spatial datasets, and institutional leadership. Thus, to enhance the development of NSDI in Pakistan, recommendations include bolstering financial and human resources, improving online geospatial presence, and fostering a long-term vision for NSDI. Full article

Urban Vitality (UV) is a critical indicator for measuring sustainable urban development and quality. It reflects the dynamic interactions and supply–demand coordination within urban systems, especially concerning the human–land relationship. This study aims to quantify the UV of Luohe City, China, for the year 2023, analyze its spatial characteristics, and investigate the driving patterns of socio-ecological land functions on UV intensity and heterogeneity under different scenarios. Utilizing multi-source data, including human mobility data from Baidu Location-Based Services (LBSs), Landsat-9, MODIS, and diverse geo-information datasets, we conducted factor screening and comprehensive assessments. Firstly, Self-Organizing Maps (SOMs) were employed to identify typical activity patterns, and the Urban Vitality Index (UVI) was calculated based on Human Mobility Intensity (HMI) data. Subsequently, a framework for quantity–quality–structure assessments weighted and aggregated sub-indicators to evaluate the Land Social Function (LSF) and Land Ecological Function (LEF). Following the screening process, a Multi-scale Geographically Weighted Regression (MGWR) was applied to analyze the scale and driving relationships between UVI and the land assessment sub-indicators. The results were as follows: (1) The UV distribution in Luohe City was highly uneven, with high vitality areas concentrated within the built-up regions. (2) UV showed significant correlations with both LSF and LEF. The influence of LSF on UV was stronger than that of LEF, with the effectiveness of LEF relying on the well-established provisioning of LSF. (3) Artificial Surface Ratio (ASR) and Corrected Night Lights (LERNCI) were identified as key drivers of UV across multiple scenarios. Under the weekend scenario, the Green Space Ratio (GSR) and the Vegetation Quality (VQ) notably enhanced the attractiveness of human activities. (4) The impacts of drivers varied at the urban, township, and street scales. The analysis focuses on factors with significant bandwidth changes across multiple scenarios: VQ, Remote-Sensing-based Ecological Index (RSEI), GSR, ASR, and ALSI. This study underscores the importance of socio-ecological land functions in enhancing urban vitality, offering valuable insights and data support for urban planning. Full article

With the rapid development of geo-positioning technologies, location-based services have become increasingly widespread. In the field of location-based services, range queries on geographical data have emerged as an important research topic, attracting significant attention from academia and industry. In many applications, data owners choose to outsource their geographical data and range query tasks to cloud servers to alleviate the burden of local data storage and computation. However, this outsourcing presents many security challenges. These challenges include adversaries analyzing outsourced geographical data and query requests to obtain privacy information, untrusted cloud servers selectively querying a portion of the outsourced data to conserve computational resources, returning incorrect search results to data users, and even illegally modifying the outsourced geographical data, etc. To address these security concerns and provide reliable services to data owners and data users, this paper proposes an efficient and verifiable range query scheme (EVRQ) for encrypted geographical information in untrusted cloud environments. EVRQ is constructed based on a map region tree, 0–1 encoding, hash function, Bloom filter, and cryptographic multiset accumulator. Extensive experimental evaluations demonstrate the efficiency of EVRQ, and a comprehensive analysis confirms the security of EVRQ. Full article

Geographic information systems (GISs) based on WebGIS architectures have transformed geospatial data visualization and analysis, offering rapid access to critical information and enhancing decision making across sectors. This study conducted a bibliometric review of 358 publications using the Web of Science database. The analysis utilized tools, such as Bibliometrix (version R 4.3.0) and Biblioshiny (version 1.7.5), to study authors, journals, keywords, and collaborative networks in the field of information systems. This study identified two relevant clusters in the literature: (1) voluntary geographic information (VGI) and crowdsourcing, focusing on web integration for collaborative mapping through contributions from non-professionals and (2) GIS management for decision making, highlighting web-based architectures, open sources, and service-based approaches for storing, processing, monitoring, and sharing geo-referenced information. The journals, authors, and geographical distribution of the most important publications were identified. China, Italy, the United States, Germany, and India have excelled in the application of geospatial technologies in areas such as the environment, risk, sustainable development, and renewable energy. These results demonstrate the impact of web-based GISs on forest conservation, climate change, risk management, urban planning, education, public health, and disaster management. Future research should integrate AI, mobile applications, and geospatial data security in areas aligned with sustainable development goals (SDGs) and other global agendas. Full article

The National Oceanic and Atmospheric Administration’s (NOAA) Center for Satellite Applications and Research (STAR) facilitates and enables societal benefits from satellite oceanography, supporting operational and experimental satellite missions, developing new and improved ocean observing capabilities, engaging users by developing and distributing fit-for-purpose data, applications, tools, and services, and curating, translating, and integrating diverse data products into information that supports informed decision making. STAR research, development, and application efforts span from passive visible, infrared, and microwave observations to active altimetry, scatterometry, and synthetic aperture radar (SAR) observations. These efforts directly support NOAA’s operational geostationary (GEO) and low Earth orbit (LEO) missions with calibration/validation and retrieval algorithm development, implementation, maintenance, and anomaly resolution, as well as leverage the broader international constellation of environmental satellites for NOAA’s benefit. STAR’s satellite data products and services enable research, assessments, applications, and, ultimately, decision making for understanding, predicting, managing, and protecting ocean and coastal resources, as well as assessing impacts of change on the environment, ecosystems, and climate. STAR leads the NOAA Coral Reef Watch and CoastWatch/OceanWatch/PolarWatch Programs, helping people access and utilize global and regional satellite data for ocean, coastal, and ecosystem applications. Full article

The improvement of search engines for geospatial data on the World Wide Web has been a subject of research, particularly concerning the challenges in discovering and utilizing geospatial web services. Despite the establishment of standards by the Open Geospatial Consortium (OGC), the implementation of these services varies significantly among providers, leading to issues in dataset discoverability and usability. This paper presents a proof of concept for a search engine tailored to geospatial services in Switzerland. It addresses challenges such as scraping data from various OGC web service providers, enhancing metadata quality through Natural Language Processing, and optimizing search functionality and ranking methods. Semantic augmentation techniques are applied to enhance metadata completeness and quality, which are stored in a high-performance NoSQL database for efficient data retrieval. The results show improvements in dataset discoverability and search relevance, with NLP-extracted information contributing significantly to ranking accuracy. Overall, the GeoHarvester proof of concept demonstrates the feasibility of improving the discoverability and usability of geospatial web services through advanced search engine techniques. Full article