Geospatial Method Research Articles

Dual Clustering-Based Method for Geospatial Knowledge Graph Partitioning

Geospatial knowledge graphs provide critical technology for integrating geographic information and semantic knowledge, which are very useful for geographic data analysis. As the scale of geospatial knowledge graphs continues to grow, the distributed management of geospatial knowledge graphs is becoming an inevitable requirement. Geospatial knowledge graph partitioning is the core technology for the distributed management of geospatial knowledge graphs. To support geographic data analysis, spatial relationships between entities should be considered in the application of geospatial knowledge graphs. However, existing knowledge graph partitioning methods overlook the spatial relationships between entities, resulting in the low efficiency of spatial queries. To address this issue, this study proposes a geospatial knowledge graph partitioning method based on dual clustering which performs two different clustering methods step by step. First, the density peak clustering method (DPC) is used to cluster geographic nodes. The nodes within each cluster are merged into a super-node. Then, we use an efficient graph clustering method (i.e., Leiden) to identify the community structure of the graph. Nodes belonging to the same community are further merged to reduce the size of the graph. Finally, partitioning operations are performed on the compressed graph based on the idea of the Linear-Weighted Deterministic Greedy Policy (LDG). We construct a geospatial knowledge graph based on YAGO3 to evaluate the performance of the proposed graph partitioning method. The experimental results show that the proposed method outperforms ten comparison methods in terms of graph partitioning quality and spatial query efficiency.

SPATIAL ANALYSIS OF PHYSICAL ACCESSIBILITY TO PUBLIC PRIMARY HEALTHCARE CENTERS IN KADUNA STATE, NIGERIA

The rising call for spatial justice in the distribution and access to public facilities especially health necessitated this study. The study sets out to assess the spatial patterns of physical accessibility to PPHCs in Kaduna State. Four physical accessibility parameters were used in this study: travel time, travel distance to the nearest Public Primary Healthcare Centers (PPHCs), mean distances between and within PPHCs, and effective geographic catchment area coverage of PPHCs. Both travel time and distance to the nearest PPHC were evaluated using a questionnaire. The mean distance between and within PPHCs for LGAs was estimated from the Nearest Neighbor Analysis. The effective geographic catchment area of a PPHC was determined using the geospatial Thiessen polygon method. Findings showed that 42% of the respondents, live close (< 2 km) to a PPHC facility. The proximity of the respondents from Igabi (52%), Zaria (49%), and Zangon-Kataf (51%) is closer than the other LGAs. Thiessen catchment area showed a wide range of variations with some PPHCs with very large areas (426 -615 km2) mostly found in the central senatorial zone, especially in Birnin Gwari and Chikun LGAs. The most widespread are PPHCs with relatively moderate catchment sizes of (187–339 km2) found across parts of northern, central, and southern senatorial and LGAs. The study concludes that areal and topographic factors as revealed by the polygon-derived catchment areas should also guide the decision for the siting of PPHCs in the state. Hence, additional PPHCs are recommended especially LGAs with extensive land masses.

Mapping municipal land policies: applications of flexible zoning for densification

Municipalities develop individual land policies and can choose not to apply available planning instruments or to apply them differently than intended. While this strategic action can endanger the effectiveness of public policies, it is rarely considered in large-scale analyses. A new geospatial method is presented that captures such strategic action. Empirical evidence from the Canton of Bern, Switzerland, reveals municipalities’ application of planned unit development (PUD), an instrument potentially allowing for flexible zoning negotiations to promote densification. Detailed zoning and building data from the period 2002–23 are used to examine the variation in PUD application by municipalities and whether they apply it to enable densification. Distinguishing by municipality type (i.e. urban core, small town, suburban, rural or touristic) reveals that PUD application is connected to projects with higher building density, more complex parcel changes and, for urban and suburban municipalities, higher density gains in low-density neighbourhoods. However, rural municipalities seldom apply PUD. New indicators of planning complexity provide an insight into whether municipalities apply instruments to enable densification under difficult circumstances. Practice relevance Many policy instruments are provided for in the law, but it is unclear whether and how they are used in practice by municipalities. Following urban densification objectives, municipalities often struggle to implement planning or housing policy instruments, which target private landowners and developers. Therefore, evaluating how different municipalities apply policy instruments and how this application contributes to reaching stated policy goals is crucial. Therefore, this research explores new ways to capture the effect of policy instruments with spatial analysis. Furthermore, by examining the real-world implementation of PUDs, this research sheds light on possible barriers to PUD application for densification, thereby helping policymakers and urban planners improve the effectiveness of public policies.

Towards a more realistic estimation of urban land take by combining cadastral parcels and building footprints

Despite its importance as a research issue, a widely accepted methodology of estimating land take resulting from urbanization has not yet been reached. Accurate geospatial datasets are currently available at a European and global level; however, different methods of quantifying urban land take could lead to diverse outputs, potentially resulting in underestimation. This can be alarming as encroaching urban sprawl is emerging as a major environmental challenge by destroying natural habitats, consuming productive agricultural land, and contributing to climate change by increasing energy demands. To address this knowledge gap, we propose an estimation of urban land take that combines cadastral parcels and building footprints. Land parcels can be considered as a suitable minimum mapping unit as they are directly related to the spatial level where economic decisions on land use conversion are made. The proposed geospatial method is compared to methods that depend on datasets of High Resolution Layer Imperviousness, Global Building Footprints (alone), Corine Land Cover, Urban Atlas, and Global Human Settlement Layer. The method is exemplified in case studies in Greece, specifically: (a) two islands of the South Aegean Region (Mykonos and Thera), that are heavily impacted by tourism development and sprawl and (b) a peri-urban zone (Thermaikos-Michaniona) within the metropolitan area of Thessaloniki, impacted by intense suburbanization. Results show that urban land consumes important shares of available land since the mid-20th century, this fact highlighting the dynamic encroachment of urban development. Calculation shows that other methods could underestimate urbanized areas by up to 80%–90%. In the discussion section, the advantages of shifting the focus from the pixel to the parcel are further justified, while explicit links to spatial planning policies for sprawl containment are drawn. Such policies could be informed by more realistic estimations of urban land take, in order to proceed with strategic and regulatory measures to support the ‘no net land take’ policy of Europe.

EXPLORING THE POTENTIAL OF GEOSPATIAL VIRTUAL REALITY IN FORENSIC CSI: AN OVERVIEW

This paper presents a discussion on the applicability of geospatial data sources such as laser scanners and photogrammetry integrated with VR so that crime scene investigation (CSI) can be improved in data management, time consumption, and user experience. CSI is a part of forensic science where physical measurement and evidence consist of objects connecting to the case are recorded in the crime scene. Traditionally, the method of acquiring information on the crime scene is using hand sketching and 2D photography. Even 3D models are used, but the display on the screen does not support full information of the data, making the presentation of the data visually limited. Geospatial method has the advantage of 3D information that is able to digitally measure dimensions and volumetric properties of physical objects. The geospatial surveying such as laser scanning, and photogrammetry can be used to preserve the evidence by recording the physical crime scene in the digital world. The scanning world known as point clouds are essentially used to calculate the angle, distance and dimensions, area, volume, and speed of objects as well. The point clouds can be optimised into virtual reality (VR) with realistic textured details making the user experience more intuitive.

GPS and GIS Based Soil Mapping of Rajkot City

Abstract Traditional (Classical) soil mapping is inefficient in illustrating continuous changes in soil types or forms. Before designing and constructing any construction, geotechnical engineers should be aware of changes in the index qualities of the soil. Sand, Gravel, Silt and Clay, Plastic Limit, and other soil index values may vary from location to location. This work aims to develop a system for mapping important soil index features using GPS and GIS in conjunction with previously published soil exploration data. This database will allow geotechnical engineers to run more searches on various combinations of soil properties. During this study, data from 112 boreholes in Rajkot was compiled into soil investigation reports by various geotechnical specialists. All 112 boreholes are included in a soil database. GPS has been used to figure out each location’s longitude, latitude & altitude for georeferencing. The ordinary kriging geospatial interpolation method was used for thematic map preparation. In this 10% of boreholes, data has been used for validation. Map accuracy is measured using the Mean Average Error (MAE) and the Root Mean Squared Error (RMSE). Where the difference in values were not more than 10% in all 12 cases. The value is only crossed in the plastic index case, where it is more than 10% in the 1-2-meter depth case.

GIS and Geospatial Approaches for Political Science Studies: Urban Development Analysis

A geographic information system (GIS) is a tool that collects, organizes, interprets, and maps different kinds of data. The geospatial method consists of the gathering, displaying, and manipulating of imagery, GPS, satellite photos, and historical data that are used with geographic models and either explicitly specified in terms of geographic coordinates. Political science studies regional, state, and national governments, political actors, political discourse in the media, and the connections between political figures and groups. In this study, Geographic Information System and Geospatial approaches for Political Science studies are used to develop Urban Development Analysis. Political scientists have been incorporating GIS technology into their statistical analyses in recent years. This study's understanding of GIS as a mix of technology, methodology, and situated social practices is the foundation of the GIS. With the development of a permanent and easily editable database in a particular area, where various types of data may be kept, merged, and graphically displayed, GIS technologies make it all this simpler. GIS allow researchers to deal with complex, multivariate, and multidimensional historical processes more successfully as well as uncover historical, political, or social issues that may not always be readily apparent when using more conventional research and analytical techniques.

Landscape sightline analysis on height control of land around architectural heritage sites: Nanshan Temple and Nanping Academy in Zhangzhou, China

Geospatial technology has been extensively researched and applied in the urban spatial planning field. However, mountainous urban planning needs to consider not only the complex terrain but also the accessibility of the landscape to landmark buildings. Therefore, geospatial techniques and landscape sight analysis play important roles in formulating effective urban building height control measures. This article uses the Nanshan Temple and Nanping Academy of Classical Learning and its surrounding architectural planning as the research objects, constructs a digital elevation model through geospatial technology combined with Grasshopper parameterization, and calculates the visible area when viewed from different positions. To maintain the accessibility of the landscape to the landmark buildings, the layout, building height and location of the planning area are determined. The research shows that (1) through geospatial technology, landscape sightline analysis, linear projection and collision detection methods can be used to accurately calculate landscape sightline interruption points and occlusion areas around historical and cultural buildings. We concluded that the optimal viewshed protection range is a radius of 2000 m. (2) Compared with a design based on a surrounding height of 60 m, the method in this study increased the number of visible viewpoints by approximately 123.84%, while the number of invisible viewpoints decreased by approximately 21.03%. (3) Based on the sightline protection scope, the heights of the blocks in the protection zone are further calculated. Within a radius of 1000 m, a 52.78 m height limit of planned plots is recommended. Within a radius of 1500 m, a 40.87 m height limit of the planned plot is recommended. Within a radius of 2000 m, the height of the planned plot should be controlled at 24.79 m. Based on this, the specific building space arrangement can be reasonably formulated. (4) After optimizing the existing visual corridors in the reserve, the area of the viewsheds expanded from 13.0% to 19.1%. (5) Finally, this study also considered plant factors. The recommended height for plant landscapes is as follows. Woodland on hillsides should be reduced from the original 10–12 to 4.74–8.51 m. Garden land in flat areas should be reduced from the original 9 to 6.38–3.36 m. This study uses the geospatial technology landscape sight analysis method to reveal the impact of landscape sight on the spatial distribution of mountainous cities with cultural landmarks and, at the same time, provides a new method and strategy for the spatial planning of mountainous cities.

Coastal vulnerability differentiated according to geospatial quartiles method: Rasulpur to Subarnarekha estuary, east coast of India

Coastal vulnerability differentiated according to geospatial quartiles method: Rasulpur to Subarnarekha estuary, east coast of India

Determination of Sustainable Groundwater Conservation Zones Based on Groundwater Recharge Areas: Case Study: Denpasar City

Groundwater is a valuable resource for meeting the daily needs of humans, which continues to increase along with population growth. Therefore, it is essential to maintain groundwater properly. Groundwater management can be carried out comprehensively in groundwater basins, including determining groundwater recharge areas. This study aims to determine the location of groundwater recharge to conserve groundwater in Denpasar City to create groundwater sustainability. The geospatial analysis uses a weighting and scoring approach (Overlay Weighted Sum) to determine the groundwater recharge area. Retrieval data in the field by observing geological conditions and measuring the depth of the phreatic surface in Denpasar City. The determination of recharge and discharge areas was analyzed using the geospatial method using the ArcGIS 10.8 application using Interpolation Inverse Distance Weighted (IDW). The five parameters used in this scoring assessment are lithology, rainfall, soil cover type, slope, and phreatic surface depth. The highest score obtained from the weighting of the scoring is 46, while the lowest score is 36. The recharge area is found in the weighted value of 41-46, which is in the northern region of Denpasar City. The discharge area is located at a weight value of 36-41 in the southern area of Denpasar City.

Groundwater potential mapping in Trans Yamuna Region, Prayagraj, using combination of geospatial technologies and AHP method.

In this study, the combination of Remote Sensing and Geographic Information System (GIS) was utilized to identify the Groundwater Potential Zones (GPZs) of the Trans-Yamuna region. The Groundwater Potential Zones (GPZ) were mapped by integrating drainage density, slope, geology, geomorphology, NDVI, lineament density, rainfall, soil types, land use & land cover, and topographic wetness index maps. For the prediction output to have a non-trivial degree of accuracy, multicollinearity tests were run before integrating the layers. Using the Analytical Hierarchy Process (AHP), groundwater recharge-affecting parameters and classes of each parameter were scored. All thematic layers were integrated using weighted linear combination on a GIS platform to create a groundwater potential zone map. The outcomes of the model indicate that the research region exhibits three distinct groundwater potential zones, namely low (11.928%; 354.884 km2), moderate (76.44%; 2274.4 km2), and high (11.267%; 345.943 km2), in sequential sequence. These categories define the model's output in descending order of how closely it matches the actual conditions. After that, a map removal sensitivity analysis was also executed and found that geology, geomorphology, lineament density and drainage density strongly influence the prediction model for groundwater potential zone identification. The reliability of the results is established by employing a Receiver Operating Characteristic (ROC) curve for evaluation, which demonstrates a prediction accuracy of 81.3%. Authorities responsible for groundwater resource management can use this study's findings to better inform future regulatory initiatives.

Validation of a geospatial aggregation method for congressional districts and other US administrative geographies

Stakeholders need data on health and drivers of health parsed to the boundaries of essential policy-relevant geographies. US Congressional Districts are an example of a policy-relevant geography which generally lack health data. One strategy to generate Congressional District heath data metric estimates is to aggregate estimates from other geographies, for example, from counties or census tracts to Congressional Districts. Doing so requires several methodological decisions. We refine a method to aggregate health metric estimates from one geography to another, using a population weighted approach. The method's accuracy is evaluated by comparing three aggregated metric estimates to metric estimates from the US Census American Community Survey for the same years: Broadband Access, High School Completion, and Unemployment. We then conducted four sensitivity analyses testing: the effect of aggregating counts vs. percentages; impacts of component geography size and data missingness; and extent of population overlap between component and target geographies. Aggregated estimates were very similar to estimates for identical metrics drawn directly from the data source. Sensitivity analyses suggest the following best practices for Congressional district-based metrics: utilizing smaller, more plentiful geographies like census tracts as opposed to larger, less plentiful geographies like counties, despite potential for less stable estimates in smaller geographies; favoring geographies with higher percentage population overlap.

A Geospatial Analysis-Based Method for Railway Route Selection in Marine Glaciers: A Case Study of the Sichuan-Tibet Railway Network

Marine glaciers play a significant role in shaping landforms due to their erosive nature coupled with their surrounding environment. During this process, they pose a natural hazard threat to man-made infrastructure. The dynamic nature of these glaciers poses a particular threat, especially to railway infrastructure constructed in remote areas with glacial activity. Substantial research has been undertaken on the role of threats posed by marine glaciers to railway infrastructure. However, a detailed study of favorable glacier landforms prior to railway construction has yet to be explored. In this study, we propose a geospatial analysis-based method to determine the favorable most landforms shaped by marine glaciers for railway network route selection. This study provides a novel approach by first analyzing the availability of four major favorable landforms shaped by marine glaciers (glacier canyons, valley shoulders, moraine terraces, and ancient dammed lake basins), then proposes a railway route selection method for marine glacier distribution areas involving three steps. First, it is necessary to understand the basic situation of regional glaciers; then, to determine a feasible location for the railway based on judgment of the direct and indirect action areas of glaciers; and finally, through a thematic study of glacial geomorphology, to devise corresponding strategies for using glacial landforms to optimize the railway route. In order to verify the feasibility of the proposed method, it was implemented in the Palong Zangbo watershed of the Sichuan–Tibet railway network. Utilizing the power function method, the glacier basin areas of 22 glacier canyons along the Sichuan–Tibet railway line were identified and the maximum annual average velocity of 75 glaciers over the past ten years was calculated by offset tracking technology. The results indicate that the proposed optimization strategies utilizing glacier canyons for a short and straight route scheme and leveraging moraine terraces for a high-line scheme can provide comprehensive guidance for railway route selection in marine glacial areas.

Assessment of crack severity of asphalt pavements using deep learning algorithms and geospatial system

Applications of Artificial Intelligence (AI), Deep Learning (DL), and Machine Learning (ML) are becoming common in the field of civil and infrastructure engineering. This paper proposes an innovative asphalt pavement health monitoring framework based on state-of-the-art deep learning algorithms, a convenient data collection strategy, a novel crack width calculation algorithm, and a novel geolocation-based crack visualization method. Furthermore, this paper introduces a 2000-image (1440 × 1440) dataset for crack classification, an 8000 image dataset (360 × 360) dataset and another dataset with manually segmented images. Transfer learning based DL model achieved a validation accuracy of 99%. A novel crack width calculation algorithm is proposed based on the skeletonization and gradient calculation on binary images to quantify the severity of the crack. Locations of the cracked surface positions were identified using geotagged images, and a geospatial mapping method was proposed to visualize the crack locations for health monitoring and maintenance purposes.

Hotspots of change in use of public transport to work: A geospatial mixed method study

IntroductionSeveral studies have supported the role of public transport in encouraging active transport through commuting. Investigating actual increases in public transport use within focussed local areas can help unravel what causes such increases. MethodsIn this study, we investigated factors related to the increase in public transport use in focussed local areas (hotspots) through a geospatial mixed-method approach using data from South Western Sydney, Australia, spatial cluster detection, and local stakeholder interviews. We also examined areas with low levels of public transport use. ResultsWe found that while distance to train station is a significant predictor of usage, other important factors include the professional and socioeconomic profile of the neighbourhood around the train station, the train line's deemed attractiveness and parking availability. ConclusionsThus, researchers and planners must consider a range of built environment factors when planning for changes that encourage public transport use. In addition, focusing on small local areas utilising geospatial mixed methods can provide important insights into the local drivers of public transport use.

Mapping South Florida Daily Fire Risk for Decision Support Using Fuel Type, Water Levels, and Burn History

Mapping fire risk in South Florida depends on spatially varying water levels, fuel characteristics, and topography. When surface water levels recede below the lowest topographic features (cypress strands, marshes, etc.), the ecosystem loses its natural, wetted fire breaks, and landscape-level fire risk increases. We developed a geospatial method to generate daily, categorical fire risk maps; the maps visualize low-to-high risk areas using a newly developed 100 m DEM, modeled water levels, fuel types, and fire management units. We assigned fire risk by creating a water level distribution for each unique combination of fuel type and fire management unit; fire risk was then assigned for each pixel based on risk percentiles commonly used by fire management agencies. Assigning risk based on unique fuel types and management units helped avoid over- or under-assigning fire risk that may occur when applying landscape-level “average” risk relationships. Daily maps also incorporated (1) energy release component data to better estimate fuel moisture and (2) historical burn footprints to reduce risk in recently burned areas. Our data-driven approach generated at management-relevant spatial scales may enable more informed prescribed burn planning and may increase the efficiency of staff and resource allocation across the landscape on high-wildfire-risk days.

Mapping soft densification: a geospatial approach for identifying residential infill potentials

Infill development policies have been widely adopted as strategies to reduce urban sprawl and to promote sustainable urban transformation. However, little empirical data are available to analyse infill processes and to facilitate building activity on infill potentials. This is especially true for small-scale residential infill, which often takes place on vacant or underused lots as soft densification. To address this issue, a geospatial method is presented that enables automatic detection of vacant lots for large areas. Cadastral data are used to analyse spatio-temporal development for the period 2011–21 in a German study area, containing large cites as well as rural municipalities. The results show that every fourth vacant lot was mobilised since 2011. However, additional vacant lots emerged in rural areas as new residential development areas are not fully built-up, resulting in a net increase of vacant lots. Although the quantity of vacant lot areas in 2021 suggests a high potential for residential infill, the main development on these infill sites is expected to promote additional single-family housing rather than more dense structures. Practice relevance Automatic identification and monitoring of infill potentials and development are important both for policymaking and for local planning practitioners. For small municipalities with little financial capacities, the approach can provide an overview of their vacant lots and can serve as a basis for strategic planning decisions. For the regional or national level, a yearly monitoring schedule can be established at little cost. Although the approach proves to be robust regarding its precision and is promising for a nationwide application, the data availability for the whole of Germany is awaited and the method needs to be optimised to implement the workflow in practice.

Green hydrogen production and use in low- and middle-income countries: A least-cost geospatial modelling approach applied to Kenya

With the rising threat of climate change, green hydrogen is increasingly seen as the high-capacity energy storage and transport medium of the future. This creates an opportunity for low- and middle-income countries to leverage their high renewable energy potential to produce, use, and export low-cost green hydrogen, creating environmental and economic development benefits. While identifying ideal locations for green hydrogen production is critical for countries when defining their green hydrogen strategies, there has been a paucity of adequate geospatial planning approaches suitable to low- and middle-income countries. It is essential for these countries to identify green hydrogen production sites which match demand to expected use cases such that their strategies are economically sustainable. This paper therefore develops a novel geospatial cost modelling method to optimize the location of green hydrogen production across different use cases, with a focus on suitability to low- and middle-income countries. This method is applied in Kenya to investigate the potential hydrogen supply chain for three use cases: ammonia-based fertilizer, freight transport, and export. We find hydrogen production costs of €3.7–9.9/kgH2 are currently achievable across Kenya, depending on the production location chosen. The cheapest production locations are identified to the south and south-east of Lake Turkana. We show that ammonia produced in Kenya can be cost-competitive given the current energy crisis and that Kenya could export hydrogen to Rotterdam with costs of €7/kgH2, undercutting current market prices regardless of the carrier medium. With expected techno-economic improvements, hydrogen production costs across Kenya could drop to €1.8–3.0/kgH2 by 2030.

Impact of limestone caves and seawater intrusion on coastal aquifer of middle Andaman

Seawater intrusion has become a common problem in coastal and island aquifers with the rise in climate change that greatly affects the majority of developing countries. The island hydrology is very complex and associated with a unique set of environmental characteristics with the dynamic interaction of groundwater, surface water, and seawater. Further, Sea level rise, erratic rainfall, and over-extraction of groundwater triggered salt-water intrusion. A study on seawater intrusion and the effect of limestone caves on groundwater was carried out in middle Andaman using a combination of ionic ratios of major ions. A total of 24 samples and a reference sample from the sea were collected and analysed using ICP, spectrophotometer, and flame photometer. A combination of 10 ionic ratios Cl/HCO3, Ca/(HCO3 + SO4), (Ca + Mg)/Cl, Ca/Mg, Ca/Na, Cl/(SO4 + HCO3), Ca/SO4, K/Cl, Mg/Cl, and SO4/Cl was used to assess the dissolution of limestone minerals and the level of saltwater intrusion into groundwater. The geospatial method was used to extract and combine all the hydrogeochemical parameters and ionic ratios in the GIS platform. Durov plot was used for the interpretation of groundwater chemistry and the identification of natural processes controlling the hydrogeochemistry of the area. The dominance of Ca-HCO3 and Na-HCO3 was confirmed in 48% and 24% of the sample respectively. The equiline graph of chloride with other major ions showed the enrichment of alkali and alkaline earth metal salt in groundwater. Schoeller's diagram depicted the dominance of Cl, Ca, and the sum of CO3 and HCO3 in seawater near Mayabunder. The lower concentration of Na with respect to Cl (64%) and Ca (100%) showed the presence of a reverse ion exchange process. Further, the correlation matrix showed a strong relationship between Cl, K, Ca, and Na. The analysis of X-ray diffraction of the rock samples confirmed the presence of limestones such as Aragonite, Calcite, Chlorite, Chromite, Dolomite, Magnetite, and Pyrite in the study area. The integration of ionic ratios showed moderately affected and slightly affected saline regions in 44% and 54% of the region respectively. Finally, the role of tectonic activities and active lineaments connected to the sea was found to play a major role in the intrusion of seawater where interconnected faults created an opening for surface water to recharge groundwater leading to the deep aquifer.

A geospatial method for estimating the levelised cost of hydrogen production from offshore wind

This paper describes the development of a general-purpose geospatial model for assessing the economic viability of hydrogen production from offshore wind power. A key feature of the model is that it uses the offshore project's location characteristics (distance to port, water depth, distance to gas grid injection point). Learning rates are used to predict the cost of the wind farm's components and electrolyser stack replacement. The notional wind farm used in the paper has a capacity of 510 MW. The model is implemented in a geographic information system which is used to create maps of levelised cost of hydrogen from offshore wind in Irish waters. LCOH values in 2030 spatially vary by over 50% depending on location. The geographically distributed LCOH results are summarised in a multivariate production function which is a simple and rapid tool for generating preliminary LCOH estimates based on simple site input variables.