Geospatial Data Research Articles

Proximity to high streets, social isolation and social support in British adolescents: A longitudinal analysis of sociospatial influences on social connectedness using geospatial data

High streets have been shown to be central to socio-economic activity, given their diverse residential, leisure, and commercial activities. This study explores the link between adolescent social isolation and proximity to, and land use mix in, high streets. Hypothesising that greater distance from high streets might increase social isolation, measured via social activities, friend contact frequency, and social support, we used multilevel modelling with data from the Millennium Cohort Study. We did not observe a relationship between proximity to high streets and these social isolation indicators, suggesting that high streets may either not significantly influence adolescent social engagement or that young people are willing to travel greater distances.

A Smart Application Frame of Remote Sensing in Non-grain Production Data Governance

Abstract. This study addresses the intricate challenges encountered in the data governance process of Non-grain Production (NGP) on Arable land. This involves managing data from diverse sources, with varying accuracies and formats, and utilizing multiple specialized software tools. An object-oriented approach is adopted to encapsulate experiential knowledge related to the data and associated processing methods, thus creating an Application Knowledge Body Model (AKBM). This model acts as a conduit between users and computational resources, encompassing various types of data and their corresponding processing and analysis methods. Moreover, by employing model inference techniques to devise methods for transitioning from raw data models to target models, a foundation is laid for the accumulation, sharing, and intelligent application of expertise on data, methods, models, and knowledge.The application examples demonstrate that users can directly construct new solutions containing relevant data and associated processing methods, rather than grappling with a multitude of data files and complex specialized software when encountering novel challenges. This promotes collaborative development in data governance on geospatial big data platforms, significantly enhancing governance efficiency, improving the quality of information support in NGP cultivation management, advancing current technological capabilities, and fostering the progression of related technologies.

Map retrieval intention recognition based on relevance feedback and geographic semantic guidance: For better understanding user retrieval demands

Effective retrieval is essential for finding resources in demand handily amidst extensive data records in data warehouse. Mainstream map retrieval methods suffer from intention gap problem and are incapable to describe sophisticated user demands precisely due to the limits of low- and middle-level text or visual feature matching, resulting in unsatisfactory retrieval results. Such limitations are more marked when map retrieval demands were characterized with joint constraints of geographic concepts. To address this issue, we propose a map retrieval intention recognition method to perceive user demands with relevance feedback samples and geographic semantics guidance. Specifically, we construct a hierarchical intention expression model to describe retrieval goals and their multi-dimensional attribute constrains; incorporate geographic ontologies to provide semantic guidance and facilitate recognition; utilize the frequent itemset mining (FIM) algorithm Apriori to generate intention candidates from relevance feedback samples, and search for the optimal intention set by adopting the minimum description length (MDL) principle. The experiments verify the effectiveness of Apriori algorithm and MDL principle on intention recognition. The proposed method outperforms the FIM algorithm Gene Ontology (RuleGO) and the Decision Tree algorithm with Hierarchical Features (DTHF) with higher recognition accuracy and noise tolerance. Furthermore, through our sample augmentation strategy, the method yields promising recognition accuracy even when the feedback sample size is as low as ten, substantially reducing the feedback burden in human-computer interactions. We envision that the application of our method in spatial data infrastructures (SDIs), such as geoportals and catalogue services, could enhance the quality of service and user experience in geospatial data discovery.

Examining nonlinear effects of socioecological drivers on urban solar energy development in China using machine learning and high-dimensional data

In the context of carbon neutrality target, renewable energy sources have been transforming from “supplementary energy” to “main energy”, which have promoted the green and low-carbon transition of global energy supply system. In-depth analyzing the spatial patterns and driving mechanisms of renewable energy expansion are of significance for optimizing the spatial layout of clean power, and avoiding the phenomenon of wind and solar power curtailment. In this paper, we proposed an ensemble learning model to examine the nonlinear effects of physical geography, resource endowment, and socio-economic factors on solar photovoltaic (PV) capacity at the prefecture-level city scale in China. Using the city-level multi-sources geospatial big data, we extensively collected a total of 175 related explanatory variables and cumulative installed capacity of solar PV power for 295 prefecture-level cities of China. The recursive feature elimination algorithm (SVM-REF) is firstly used to extract the optimal feature subset of urban PV capacity from multi-dimensional features variables. Furthermore, three advanced machine learning models (random forest, decision tree, extreme gradient boosting) are developed to identify the key influencing factors and nonlinear driving effect of urban solar PV power expansion in China. The results show that China's PV installation capacity is highly concentrated in Northern and Northwest parts of China, with the occupancy over 70% in 2019. Moreover, the XGBoost model has the best prediction accuracy (R2 = 0.97) among three methods. We also found that total amount of urban water resources, average solar radiation, and population density are the most important controlling factors for urban solar PV capacity expansion in China, with contribution of 35.6%, 17.7%, and 13.3%, respectively. We suggested that urban solar PV layout mode in China is recommended to gradually shift from resource orientation to the “resource-environment-demand” comprehensive orientation. The paper provides a replicable, scalable machine learning models for simulating solar PV power capacity at the prefecture-level city scale, and serves as a motivation for decision-making reference of the macro siting optimization and sustainable development of China's green power industry.

Prediction model for future OHCAs based on geospatial and demographic data: An observational study.

This study used demographic data in a novel prediction model to identify areas with high risk of out-of-hospital cardiac arrest (OHCA) in order to target prehospital preparedness. We combined data from the nationwide Danish Cardiac Arrest Registry with geographical- and demographic data on a hectare level. Hectares were classified in a hierarchy according to characteristics and pooled to square kilometers (km2). Historical OHCA incidence of each hectare group was supplemented with a predicted annual risk of at least 1 OHCA to ensure future applicability. We recorded 19,090 valid OHCAs during 2016 to 2019. The mean annual OHCA rate was highest in residential areas with no point of public interest and 100 to 1000 residents per hectare (9.7/year/km2) followed by pedestrian streets with multiple shops (5.8/year/km2), areas with no point of public interest and 50 to 100 residents (5.5/year/km2), and malls with a mean annual incidence per km2 of 4.6. Other high incidence areas were public transport stations, schools and areas without a point of public interest and 10 to 50 residents. These areas combined constitute 1496 km2 annually corresponding to 3.4% of the total area of Denmark and account for 65% of the OHCA incidence. Our prediction model confirms these areas to be of high risk and outperforms simple previous incidence in identifying future risk-sites. Two thirds of out-of-hospital cardiac arrests were identified in only 3.4% of the area of Denmark. This area was easily identified as having multiple residents or having airports, malls, pedestrian shopping streets or schools. This result has important implications for targeted intervention such as automatic defibrillators available to the public. Further, demographic information should be considered when implementing such interventions.

Strategic Planning of Rural Telecommunication Infrastructure: A Multi-Source Data Fusion and Optimization Model

Abstract. The United Nations has underscored the critical role of digital connectivity by integrating it into several sustainable development goals, with the ambition for nations worldwide to achieve comprehensive access by 2030. Over thousand million people, primarily in rural areas, are disconnected from the digital world, highlighting the urgent need for viable and sustainable telecommunications solutions. These areas are characterized by sparse populations and lower economic levels, presenting great challenges for connectivity. This work introduces a strategy for enhancing rural telecommunication planning using geospatial and remote sensing data, deep learning-based clustering techniques, network graphs, and terrain analysis. The objective is to develop an optimal network topology and identify prime locations for telecommunications infrastructure, such as towers or relay stations. The methodology begins with the application of an adapted Deep Embedded Clustering (DEC) technique to identify community boundaries accurately. Then, it combines geospatial data (such as roads, terrain slope, flatness, etc.) and remote sensing data (vegetation, waterways, etc.) through an optimization algorithm. This process aims to determine the most suitable sites for infrastructure placement and the best network topology for connecting these areas. The study focuses on the region of Congo, offering a detailed case study on the application of this approach. Experimental results are presented to demonstrate the effectiveness of the proposed telecommunications expansion strategy.

Tracing the Untraceables: A Joint Outbreak Investigation With Law Enforcement Using a Geographic Information System.

The application of geospatial data often allows the tracing of people who are involved in activities of an illegal nature. In June 2021, we estimated the true magnitude of the spread of COVID-19 within the networks of escort-karaoke bars in Seoul, Republic of Korea, using geographic information system (GIS)-based contact tracing that was applied to our epidemiological investigation. Our joint rapid response team, composed of epidemic investigation officers and police personnel, identified 19 paper-traced cases and 158 GIS-traced cases from 5,692 confirmed cases in Seoul during the study period (June to July 2021). Our findings suggest that collaboration with law enforcement agencies and the use of overlaid satellite imagery in outbreak investigations enhances high vigilance and reduces the risk of potential breaches of human rights in the process.

Performance of health care service area definitions for capturing variation in inpatient care and social determinants of health.

To quantify the degree to which health care service area (HCSA) definitions captured hospitalizations and heterogeneity in social determinants of health (SDOH). Geospatial data from the Centers for Medicare and Medicaid Services, the Census Bureau, and the Dartmouth Institute. Drive-time isochrones from MapBox. Area Deprivation Index (ADI) data. 2017 inpatient discharge data from Arizona, Florida, Iowa, Maryland, Nebraska, New Jersey, New York, and Wisconsin, State Emergency Department Databases and State Inpatient Databases, Healthcare Cost and Utilization Project, Agency for Healthcare Research and Quality; and Fee-For-Service Medicare data in 48 states. Cross-sectional, descriptive analysis. The capture rate was the percentage of inpatient discharges occurring in the same HCSA as the hospital. We compared capture rates for each HCSA definition for different populations and by hospital type. We measured SDOH heterogeneity using the coefficient of variation of the ADI among ZIP codes within each HCSA. HCSA definitions captured a wide range of inpatient discharges, ranging from 20% to 50% for Public Use Microdata Areas (PUMAs) to 93%-97% for Metropolitan Statistical Areas (MSAs). Three-quarters of inpatient discharges were from facilities within the same county as the patient's residential ZIP code, while nearly two-thirds were within the same Hospital Service Area. From the hospital perspective, 74.7% of inpatient discharges originated from within a 30-min drive and 90.1% within a 60-min drive. Capture rates were the lowest for teaching hospitals. PUMAs and drive-time-based HCSAs encompassed more homogenous populations while MSAs, Commuting Zones, and Hospital Referral Regions captured the most variation. The proportion of hospital discharges captured by each HCSA varied, with MSAs capturing the highest proportion of discharges and PUMAs capturing the lowest. Additionally, researchers face a trade-off between capture rate and population homogeneity when deciding which HCSA to use.

Does Aid Improve Local Wealth? Micro‐Level Evidence from Africa

AbstractThis paper adopts a micro‐level analysis to assess the effect of aid from China and the World Bank on the wealth of locals residing near aid project sites. We match geospatial data on aid projects financed by China and the World Bank in 35 African countries between 2008 and 2014 to respondents from rounds 5 and 6 of the Afrobarometer survey. The results indicate that aid from these two donors increases the wealth of the local population. Our transmission mechanism test suggests that aid impacts wealth by promoting employment opportunities.

The service model of a heterogeneous distributed database for software system scaling

The article explores the topicality of horizontal scaling for software systems that re-quire the use of database management systems (DBMS) to process large volumes of structured, JSON, and geospatial data, while maintaining requirements for ACID proper-ties and data integrity. An analysis of existing products is conducted, outlining their main advantages, disadvantages and identifying issues such as single points of failure, techni-cal limitations, and lack of support for necessary functionality. To solve these issues, a service model of a heterogeneous distributed database is developed, providing a descrip-tion of its characteristics and architecture. Using Kubernetes technology, performance metrics of the database cluster based on the developed model are investigated, demon-strating superior performance compared to the products discussed.

A web GIS based integrated water resources assessment tool for Javeh Reservoir

The creation of tools and services for tracking, estimating, and predicting water resource indices that might provide authorities access to information in close to real-time is essentially a must. The considerable development of Web-GIS DSS (Web-Geographic Information System Decision Support System) allows for the analysis of geospatial data and scenarios via the internet without a traditional desktop GIS. The web GIS technology is coupled to techniques which determine water resources Carlson, and OECD (Organization for Economic Corporation and Development) Trophic Status Indices (TSIs), the water resource vulnerability index (WRVI), and social water stress index (SWSI). The water quality indices were determined by helping machine learning (ML) techniques, Decision Tree, Random Forest, and XGBoost. The performances of aforementioned MLs were compared, and the best efficient one, XGBoost, was included in the Web-GIS DSS tool. The developed Web-GIS DSS was examined in Javeh Reservoir, Kurdestan, IranThe developed Web-GIS DSS allows the users to define the relevant features of the Javeh Reservoir system in pre-defined time horizons such as meteorological, hydrological, inflow water quality, reservoir operation strategies, downstream water demands, geographical external water transfers, etc. The results indicate that the Javeh Reservoir experiences eutrophication status according to the Carlson and OECD indices over time horizons. The water quantity assessment reveals the Javeh Reservoir's susceptibility based on WRVI, although no stress is indicated by SWSI. This susceptibility may be attributed to limited precipitation and water inflow during the simulation period, suggesting a need for comprehensive water resources management.The conclusion drawn from this research underscores the essential role of the developed Web-GIS DSS in decision-making processes for water and environment management, substantiating its practicality with accurate and user-friendly water and environmental data analysis. The tool exemplifies the potential of integrating ML and Web-GIS technologies to promote sustainable environmental practices, thereby establishing a model for future research and policy-making initiatives.

From hearing to seeing: Linking auditory and visual place perceptions with soundscape-to-image generative artificial intelligence

People experience the world through multiple senses simultaneously, contributing to our sense of place. Prior quantitative geography studies have mostly emphasized human visual perceptions, neglecting human auditory perceptions at place due to the challenges in characterizing the acoustic environment vividly. Also, few studies have synthesized the two-dimensional (auditory and visual) perceptions in understanding human sense of place. To bridge these gaps, we propose a Soundscape-to-Image Diffusion model, a generative Artificial Intelligence (AI) model supported by Large Language Models (LLMs), aiming to visualize soundscapes through the generation of street view images. By creating audio-image pairs, acoustic environments are first represented as high-dimensional semantic audio vectors. Our proposed Soundscape-to-Image Diffusion model, which contains a Low-Resolution Diffusion Model and a Super-Resolution Diffusion Model, can then translate those semantic audio vectors into visual representations of place effectively. We evaluated our proposed model by using both machine-based and human-centered approaches. We proved that the generated street view images align with our common perceptions, and accurately create several key street elements of the original soundscapes. It also demonstrates that soundscapes provide sufficient visual information places. This study stands at the forefront of the intersection between generative AI and human geography, demonstrating how human multi-sensory experiences can be linked. We aim to enrich geospatial data science and AI studies with human experiences. It has the potential to inform multiple domains such as human geography, environmental psychology, and urban design and planning, as well as advancing our knowledge of human-environment relationships.

Analysis of landslide susceptibility and potential impacts on infrastructures and settlement areas (a case from the southeastern region of Türkiye)

Landslides have harmful effects not only on buildings but also on infrastructure and the natural environment. While they are typically triggered by natural events, such as heavy rainfalls and earthquakes, landslides can also be induced by anthropogenic activities, such as excavation and blasting. In certain regions, gigantic paleo-landslides exist, but triggering them is extremely difficult. However, triggering secondary landslides in gigantic paleo-landslides is relatively easy compared to the main corpus. The main purpose of this study was to produce a susceptibility map in a region in southeastern Türkiye and to discuss the impact of petroleum seismological investigation concerning the trigger of the landslides. For this purpose, a landslide inventory was compiled using geospatial data sets and field observations and used for landslide susceptibility mapping with the Random Forest algorithm. The accelerations sourced from blasting were also measured and the run-out distances were determined. A run-out distance map was produced using inverse distance weights. The study presents comprehensive insights by integrating a landslide susceptibility map and run-out distance map. It evaluates the impact of blasting on landslides through in-situ measurements and slope stability analyses. Findings indicate that no triggering effect on landslides was observed if the dynamite quantity remains below 4 kg and the blasting distance exceeds 10 m from the landslide.

Assessing Multi-source Random Forest Classification and Robustness of Predictor Variables in Flooded Areas Mapping

Flood extent delineation techniques have benefited from the increasing availability of remote sensing imagery, classification techniques and the introduction of geomorphic descriptors derived from Digital Elevation Models (DEM). On the other hand, high-performing Machine Learning (ML) methods have allowed for the development of accurate flood maps by integrating several predictor variables into supervised or unsupervised algorithms. Among others, Random Forest (RF) is a powerful and widely applied ML classifier, providing accurate predictions also with complex datasets and for varying parameters set. In the present study, the effectiveness of this algorithm for mapping flooded areas was evaluated. Various geospatial data sources were integrated, including morphological indicators, such as the Geomorphic Flood Index (GFI), Sentinel-2 bands, multispectral indices, and Sentinel-1 polarizations. The reliability of the predictor variables under different training sample sizes was evaluated and the accuracy of the RF classifier was assessed. Moreover, by exploring the algorithm ability to identify the most important variables, the predictors contributing the most to the classification were identified and their stability for varying training parameters was investigated. To gauge the adaptability and consistency of these features, we applied our analyses to different study areas around the World. The results indicate that certain predictors displayed remarkable stability across different sample sizes and remained robust under various training parameters. However, some variability in the algorithm structure and the features related to the specific complexities of each considered study case was also observed.

An integrated geospatial data model for active travel infrastructure

Active travel has received increased investment and interest in many countries both due to COVID-19 and to policies which promote a shift in mobility behaviours to support a wide range of public and individual goods. However, while there has often been substantial investment in the physical infrastructure that can help facilitate active travel, there has not so far always been commensurate investment in the data infrastructure which can help enable people to shift trips to active modes. Current fragmented data and data models and a lack of data standards pose a barrier to the development of the applications which are needed to support planners, users and journey planning. There is therefore a need for a more integrated, better-connected and more richly attributed active travel geospatial network model. This paper describes the development of such a data model, and its initial application to a case study of Great Britain. It demonstrates how the development, population and maintenance of such a data model could facilitate a range of novel applications, such as the personalisation of active travel journey planning to address different user needs and capabilities. If the potential societal benefits from investment in physical active travel infrastructure are to be fully realised, this needs to be supported by the availability of a robust spatial data infrastructure capable of providing the information required by walkers, wheeled users and cyclists to make effective use of the active travel network.

Construction of ecological network in Qujing city based on MSPA and MCR models.

With the rapid advancement of urbanization and industrialization, ecological patches within cities and towns are fragmented and ecological corridors are cut off, regional ecological security is threatened and sustainable development is hindered. Building an ecological network that conforms to regional realities can connect fragmented patches, protect biodiversity and regional characteristics, and provide scientific reference for regional ecological protection and ecological network planning. By taking Qilin District, the main urban area of Qujing City as an example, and using geospatial data as the main data source, based on morphological spatial pattern analysis (MSPA) and minimum cumulative resistance (MCR), this study identified ecological source areas, extracted ecological corridors, and build & optimize ecological networks. (1) All landscape types are identified based on MSPA, the proportion of core area was the highest among all landscape types, which was 80.69%, combined with the connectivity evaluation, 14 important ecological source areas were selected. (2) 91 potential ecological corridors were extracted through MCR and gravity models, there were 16 important ones. (3) The network connectivity analysis method is used to calculate the α, β, and γ indexes of the ecological network before optimization, which were 2.36, 6.5, and 2.53, while after optimization, α, β and γ indices were 3.8, 9.5 and 3.5, respectively. The combined application of MSPA-MCR model and ecological network connectivity analysis evaluation is conducive to improving the structure and functionality of ecological network.

An effective spatial join method for blockchain-based geospatial data using hierarchical quadrant spatial LSM+ tree

An effective spatial join method for blockchain-based geospatial data using hierarchical quadrant spatial LSM+ tree

Discovering Links between Geospatial Data Sources in the Web of Data: The Open Geospatial Engine Approach

The Web of Data has been fueled significantly by geospatial data over the last few years. In the current link discovery frameworks, there is still a lack of robust support for finding geospatial-aware links between geospatial data sources in the Web of Data. They are also limited in efficient association capabilities for large-scale datasets. This paper extends the data integration capability based on the spatial metrics in the open geospatial engine OGE. These metrics include topological relationships and spatial matching between geospatial entities within multiple geospatial data sources. Thus, the tool can be employed by data publishers to set geospatial-aware links to facilitate geospatial data and knowledge discovery in the Web of Data. Several geospatial data sources are used to demonstrate the usability and effectiveness of the approach and tool implementation.

A Comprehensive Resource Assessment for Wind Power Generation on the Rural Island of Sibuyan, Philippines

Amid rising energy demands in rural areas, thorough resource assessments for initiatives such as wind power are crucial. This study involves a land resource assessment for wind power generation on the rustic Sibuyan Island in the Philippines, which is currently experiencing an electricity shortage. A comprehensive overview of the island’s suitability for wind energy projects is performed via evaluation and analysis using geospatial data and multi-criteria decision making (MCDM). The research results indicate that 50.44% (220.68 km2) of the island’s land area is categorized as ‘poorly suitable’ since it considers protected areas where developments are not allowed. Only 0.08% (0.35 km2) of the island can be classified as ‘marginally suitable’, while 9.15% (40.73 km2), 36.64% (176.39 km2), and 0.69% (3.05 km2) are labeled as ‘moderately suitable’, ‘suitable’, and ‘highly suitable’, respectively. This confirms the potential for wind energy exploration on the island. Delineating the suitability levels provides a foundational framework for stakeholders that enables them to identify optimal sites for wind power, sustain the island’s resources, and contribute to the renewable energy landscape of this rural location. Overall, this study, underpinned by data analysis, offers invaluable insights for decision making in wind power development, with the presented framework adaptable to other areas of interest.

There is no place to go in “America's Finest City”: Basic sanitation deprivation is punishment in San Diego, California, USA

Safe and consistent access to water, sanitation, and hygiene (WaSH) resources is an internationally recognized human right. Yet in the U.S., access to public toilets, handwashing facilities, potable water, and menstrual supplies is grimly limited, especially for unhoused people. This article develops the concept of “sanitation justice” and argues for its urgent consideration alongside calls for racial, gender, social, and environmental justice. We advance this argument via a case study analyzing three novel data sources on WaSH access in San Diego, California's second-largest city, with a focus on public restrooms: geospatial data on facility locations, infrastructure assessments, and interviews with unhoused people on their restroom needs and challenges. We present a conceptual model, informed by the prior literature and our data, for visualizing how deliberate sanitation deprivation in the U.S. urban environment functions as a punishment regime inflicted on city residents, unhoused residents especially, to the detriment of public health and safety. As our data show, WaSH access is the most basic way governments can support not only public health but also social and economic mobility. We conclude with a vision of sanitation justice that centers the voices of those most affected by sanitation deprivation.