Geospatial Model Research Articles

Distribution of the Culex pipiens mosquito in mainland Portugal: a geospatial modelling study

Abstract Background The mosquito Culex pipiens is a vector for several pathogens, such as the West Nile virus. As the most abundant mosquito in Portugal, understanding its spatial distribution can support vector-borne diseases control and robust public health planning, given the mainland’s favourable climate for this vector. Information on its spatial distribution at a national level is limited, especially its association with climate variables. Thus we aimed to predict the suitability of Culex pipiens distribution in mainland Portugal, considering climate factors. Methods We used presence records for adult and larvae of Culex pipiens mosquitoes sampled across mainland Portugal from January 2017 to October 2023, as part of the Portuguese Vector Surveillance Network program (REVIVE). Between May-October, in random locations and throughout the year in point-of-entry sites, adult mosquito collection used CDC light traps and BG-Sentinel traps, and larvae collection used dippers in breeding sites. Sampling bias was corrected by filtering presence sites, retaining one record per each cell grid of 1 km2. Climatic data (including temperature, precipitation and elevation) were used as predictors in a maximum entropy (MaxEnt) model of the Culex pipiens geographical distribution. Results From the dataset of 6859 records, 1416 unique sites were obtained after filtering. Suitable habitats seem to be mainly distributed on the coast, particularly in Minho, Porto and Lisbon metropolitan areas, and Tagus Valley, with precipitation as the most important predictor. Conclusions Most West Nile Virus case reports come from the south of Portugal, but the potential distribution of Culex pipiens covers the whole mainland territory, with seemingly higher distribution on the north coast. However, bias due to convenience sampling could be present. West Nile vector surveillance should be a priority across all regions to accurately assess transmission risk and implement effective control measures. Key messages • The suitability of Culex pipiens distribution covers the entire mainland of Portugal, with higher presence in the north. • The distribution of Culex pipiens in the country points towards a need for developing region-specific strategies regarding West Nile Virus prevention and control.

A Spatiotemporal Empowerment Framework for China’s National 3D Mapping Program 3dRGLM

Abstract. China has launched a national 3D mapping program to build 3D Realistic geospatial Landscape Model (3dRGLm) over the whole country, covering both cities and countryside. The 3dRGLM is a digital description and representation of the real 3D geospatial spaces, and is defined as a new generation geospatial data product with 3D structures, realistic scenes, and geospatial entities. Such a national or local 3dRGLm will set up a digital 3D realistic geospatial space which can facilitate the vital connection with the real geospatial space, and will serve as a new generation of geospatial information infrastructure for our society. With the acquisition of complex 3D spatial data continues to advance, managing and utilizing the massive volumes of 3D data to provide spatiotemporal empowerment applications has become a significant technological challenge. This paper analyzes the technical characteristics of 3d realistic geospatial landscape model, and then proposes the main content and basic structure of a 3d realistic geospatial landscape model database, as well as the spatiotemporal service empowerment methods based on the database. Finally, a case analysis is provided to serve as a reference for the construction and application of 3d realistic geospatial landscape.

Profiling Standards to Improve Practical Interoperability

Abstract. Standard data models are key to enable a set of data integration functionalities, often characterised using the Findability, Accessibility, Interoperabilty and Reusability (FAIR) principles. However, standardisation is a process of trying to meet many requirements, and standard data models are inherently either very abstract or very comprehensive in the details. This results in several ambiguity pitfalls, inconsistent implementation of standard data models, which in turn hinders trust in the interoperability potential of standardised data, and complicates any integration processes. In practice profiling such standards is useful to overcome such issues to create more useful forms of standardised data for specific applications. However defining custom profiles typically requires a great deal of technical expertise in the underlying expression language of the standard. Maintaining access to this level of expertise is a challenge as profiles become outdated through the time and lose connection with the maintenance of the parent standard from which they originate. Therefore, in this paper, a scalable methodology is proposed, built on the OGC Building Blocks Model approach, that uses semantic modelling to support an easier composition of geospatial data models profiles which directly derive from available standards without losing the relevant dependencies that inform stakeholders which components are interoperable with other standards. The approach is tested within a digital building permit project (CHEK), in which data requirements derive from the semantics of city regulations and common geospatial standards (i.e., CityGML and INSPIRE) are used as reference.

Development of electromagnetic pollution maps utilizing Gaussian process spatial models

The rapid proliferation of wireless technologies in everyday environments demands the quick and precise estimation of electromagnetic field distribution. This distribution is commonly depicted through the electric field strength across various geographical areas. The objective of this research is to determine the most effective geospatial model for generating a national-level electric field strength map within the 30 MHz–6 GHz frequency range. To achieve this, we employed five different methodologies for constructing the electric field strength map. Four of these methodologies are based on Gaussian process regression, while the fifth utilizes the classical weighted-average method of the nearest neighbor. Our study focused on a country with a total area of 9251 km2, using a dataset comprising 3621 measurements. The findings reveal that Gaussian process spatial models, also known as Kriging models, generally outperform other methods when applied to spatial data. However, it was observed that, after excluding some outlier data points, the performance of the classical nearest neighbor models becomes comparable to that of the Gaussian process models. This indicates the potential for both approaches to be effective, depending on the data quality and the presence of outliers.

Geometry, Topology and p-Adic Numbers in Geospatial Data Modelling, Management and Processing: Review and Future Approaches

Abstract. Geometry and topology are both central concepts in geospatial data modelling and management. While geometry is the central concept for computational geometry applications, e.g. to intersect surfaces and solids in 3D space, topology is helpful for many application classes starting from city modelling to subsurface modelling and indoor navigation. Furthermore, p-adic numbers are useful for describing hierarchical processes on topological models. In this paper we first shortly review geometry- and topology-based approaches used for geospatial applications. We then describe our approach on turning geometry “upside down” focusing on topology during the whole process of distributed geospatial computing, data modelling, data management, and simulation. Furthermore, the way to use p-adic numbers for the description of hierarchical processes on topological models is shown for the example of simulation and the idea of p-adic analysis in distributed simulations is presented in the context of topological city and building models. The approach opens new insights such as topological relationships, components, and efficiently studying of the approximate behaviour of processes in the built environment using distributed computational systems. Finally, exemplary applications are presented to underline the importance of this new approach.

Low cost and real-time surveillance of enteric infection and diarrhoeal disease using rapid diagnostic tests in Cox’s Bazar, Bangladesh

BackgroundReal-time disease surveillance is an important component of infection control in at-risk populations. However, data on cases or from lab testing is often not available in many low-resource settings. Rapid diagnostic tests (RDT), including immunochromatographic assays, may provide a low cost, expedited source of infection data.MethodsWe conducted a pilot survey-based prevalence mapping study of enteric infection in Camp 24 of the camps for the forcibly displaced Rohingya population from Myanmar in Cox’s Bazar, Bangladesh. We randomly sampled the population and collected and tested stool from under-fives for eight pathogens using RDTs in January–March 2021 and September–October 2021. A Bayesian geospatial statistical model allowing for imperfect sensitivity and specificity of the tests was adapted.ResultsWe collected and tested 396 and 181 stools in the two data collection rounds. Corrected prevalence estimates ranged from 0.5% (Norovirus) to 27.4% (Giardia). Prevalence of Escherichia coli O157, Campylobacter, and Cryptosporidium were predicted to be higher in the high density area of the camp with relatively high probability (70–95%), while Adenovirus, Norovirus, and Rotavirus were lower in the areas with high water chlorination. Clustering of cases of Giardia and Shigella was also observed, although associated with relatively high uncertainty.ConclusionsWith an appropriate correction for diagnostic performance RDTs can be used to generate reliable prevalence estimates, maps, and well-calibrated uncertainty estimates at a significantly lower cost than lab-based studies, providing a useful approach for disease surveillance in these settings.

National-Scale Geospatial Modelling of Pyrrhotite in Bedrock Across Canada: A Pilot Study

The most abundant iron sulphide minerals in igneous, metamorphic, and some sedimentary rocks are FeS2 (pyrite/marcasite) and Fe1-xS (pyrrhotite). The oxidation of pyrite and pyrrhotite in bedrock aggregates and mine tailings is undesirable to infrastructure and the environment because such chemical reactions are linked with concrete deterioration and acid mine drainage, respectively. The oxidation rate of pyrrhotite is up to one hundred times greater than that of pyrite; thus, pyrrhotite oxidation is of particular concern to society. Pyrrhotite-bearing concrete aggregates may lead to rapid expansion cracking and failure of critical concrete infrastructure including bridges, buildings, and houses, posing a significant safety concern. Additionally, premature disintegration of concrete requires replacement of concrete, leading to excessive aggregate resource extraction and associated increases in CO2 emissions. Considering the widespread concrete infrastructure across Canada, the distribution of pyrrhotite in bedrock used for aggregate is of fundamental importance to the short- and long-term safety of Canadians at the national, regional, and local scales. This pilot study details the initial steps taken to generate national-scale geospatial models of pyrrhotite occurrences in bedrock across Canada and illustrates the associated map products. In total, 12,577 known pyrrhotite occurrences were identified from publicly available provincial and territorial mineral occurrence datasets. The overall modelling strategy involved normalizing the number of pyrrhotite occurrences with respect to the total surface area of major bedrock types and characterizing three different classes of pyrrhotite occurrence density (< 1, 1–4, and 4–10 occurrences/1000 km2). The maps illustrate that pyrrhotite occurrence density is highest in volcanic rocks and undifferentiated sedimentary and volcanic rocks, moderate in intrusive and unknown rocks, and lowest in sedimentary and metamorphic rocks. Sedimentary rocks with no pyrrhotite occurrences span large surface areas across central-western Canada thus resulting in an overall low pyrrhotite occurrence density (< 1 occurrence/1000 km2) for this rock type, despite the fact that numerous pyrrhotite occurrences are identified in sedimentary rocks and may be abundant locally or regionally. Volcanic, undifferentiated sedimentary and volcanic, intrusive, and unknown rocks occur throughout the Canadian Shield of central and northern Canada, the Cordillera of western Canada, and the Appalachians of eastern Canada, but bedrock type and associated occurrence density are highly variable within these geological domains. Comparison of pyrrhotite occurrence density maps for Canada with pyrrhotite permissive geology maps for the United States of America illustrates that rocks with a high pyrrhotite occurrence density in Canada (volcanic rocks and undifferentiated sedimentary and volcanic rocks), are contiguous overall with areas of pyrrhotite potential in the United States. Inconsistencies across the international border reflect the differing methodologies and assumptions consisting of a statistically based approach for Canada and a qualitative approach for the United States. In the Cordillera and Appalachians, such discontinuities across the international border may reflect the underestimation of pyrrhotite occurrences in sedimentary rocks of Canada because of the impact of high surface area on the pyrrhotite occurrence density calculations. The maps presented herein are a first step in illustrating the distribution of pyrrhotite-bearing bedrock across Canada and greater North America. These national-scale map products are useful first-order references for selecting regions for follow-up studies on bedrock pyrrhotite occurrences. Regional and local geospatial analysis combined with field work for ground truthing will be important aspects of future research, especially in the vicinity of population centres where bedrock is utilized for concrete aggregate. Detailed regional and local studies of pyrrhotite occurrences in bedrock will help guide the extraction of safe concrete aggregate and contribute to the long-term sustainability of bedrock resources, safe infrastructure, and a habitable climate.

A global mathematical model of climatic suitability for Plasmodium falciparum malaria

Climatic conditions are a key determinant of malaria transmission intensity, through their impacts on both the parasite and its mosquito vectors. Mathematical models relating climatic conditions to malaria transmission can be used to develop spatial maps of climatic suitability for malaria. These maps underpin efforts to quantify the distribution and burden of malaria in humans, enabling improved monitoring and control. Previous work has developed mathematical models and global maps for the suitability of temperature for malaria transmission. In this paper, existing temperature-based models are extended to include two other important bioclimatic factors: humidity and rainfall. This model is combined with fine spatial resolution climatic data to produce a more biologically-realistic global map of climatic suitability for Plasmodium falciparum malaria. The climatic suitability index developed corresponds more closely than previous temperature suitability indices with the global distribution of P. falciparum malaria. There is weak agreement between the Malaria Atlas Project estimates of P. falciparum prevalence in Africa and the estimates of suitability solely based on temperature (Spearman Correlation coefficient of ρ=0.24\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\rho = 0.24$$\\end{document}). The addition of humidity and then rainfall improves the comparison (ρ=0.62\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\rho = 0.62$$\\end{document} when humidity added; ρ=0.70\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\rho =0.70$$\\end{document} when both humidity and rainfall added). By incorporating the impacts of humidity and rainfall, this model identifies arid regions that are not climatically suitable for transmission of P. falciparum malaria. Incorporation of this improved index of climatic suitability into geospatial models can improve global estimates of malaria prevalence and transmission intensity.

Rapid Appraisal of Wildlife Corridor Viability with Geospatial Modelling and Field Data: Lessons from Makuyuni, Tanzania

Connectivity between protected areas is necessary to prevent habitat fragmentation. Biodiverse countries like Tanzania craft legislation to promote habitat connectivity via the creation of ecological corridors, but their viability for wildlife often remains unknown. We therefore develop a scalable and replicable approach to assess and monitor multispecies corridor viability using geospatial modeling and field data. We apply and test the approach in the Makuyuni study area: an unprotected ecological corridor connecting Tarangire National Park to Essmingor mountain, Makuyuni Wildlife Park and Mto Wa Mbu Game Controlled Area. We analyzed the viability of Makuyuni as an ecological corridor by creating and validating a geospatial least-cost corridor model with field observations of wildlife and livestock. We created the model from publicly available spatial datasets augmented with manual digitization of pastoral homesteads (bomas). The least-cost corridor model identified two likely pathways for wildlife, confirmed and validated with field observations. Locations with low least-cost values were significantly correlated with more wildlife observations (Spearman’s rho = −0.448, p = 0.002). Our findings suggest that Makuyuni is a viable ecological corridor threatened by development and land use change. Our methodology presents a replicable approach for both monitoring Makuyuni and assessing corridor viability more generally. The incorporation of manually digitized homesteads (bomas) and field-based livestock observations makes corridor assessment more robust by taking into account pastoral land uses that are often missing in land cover maps. Integration of geospatial analysis and field observations is key for the robust identification of corridors for habitat connectivity.

Predicting potential biomass production by geospatial modelling: The case study of citrus in a Mediterranean area

Residual biomass from agricultural production and processing, such as citrus pulp and olive pomace, is an important resource for energy production. In particular, this is the case in regions where transformation industries are concentrated.Current biomass estimates often focus on actual production data, that may not fully capture the biomass potential across all suitable cultivation areas. To bridge this gap, the study predicts the overall potential biomass available for energy production, taking into account the total area suitable for citrus cultivation.The research is focused on the study of citrus species in the province of Syracuse, Sicily, Italy. The methodology combines Geographic Information System (GIS) tools, for data interpolation and map overlays, with Software for Assisted Habitat Modelling (SAHM) for local level simulations.The results of the different models showed accurate and spatially coherent predictions, with AUC values ranging from 0.85 to 0.90, and highest potentialities in the northern and eastern regions of the study area. The results highlighted potential citrus cultivation on 47,706 ha and estimated 184,340 t of biomass, 16,461,520.82 Nm3 of biogas, and 8110 t of digestate. The results of the study identified potential areas for both increasing biomass production and optimising the distribution of digestate, thus demonstrating the utility of these thematic maps as a decision support tool for land management. The simulations and the methodology applied in this study indicated potential economic and environmental benefits to be gained from sustainable by-product management. This approach facilitates the optimisation of decision-making processes for land planning, thereby contributing to the broader objective of improving resource efficiency and sustainability in the agricultural production and processing sectors.

High-resolution spatio-temporal risk mapping for malaria in Namibia: a comprehensive analysis

BackgroundNamibia, a low malaria transmission country targeting elimination, has made substantial progress in reducing malaria burden through improved case management, widespread indoor residual spraying and distribution of insecticidal nets. The country's diverse landscape includes regions with varying population densities and geographical niches, with the north of the country prone to periodic outbreaks. As Namibia approaches elimination, malaria transmission has clustered into distinct foci, the identification of which is essential for deployment of targeted interventions to attain the southern Africa Elimination Eight Initiative targets by 2030. Geospatial modelling provides an effective mechanism to identify these foci, synthesizing aggregate routinely collected case counts with gridded environmental covariates to downscale case data into high-resolution risk maps.MethodsThis study introduces innovative infectious disease mapping techniques to generate high-resolution spatio-temporal risk maps for malaria in Namibia. A two-stage approach is employed to create maps using statistical Bayesian modelling to combine environmental covariates, population data, and clinical malaria case counts gathered from the routine surveillance system between 2018 and 2021.ResultsA fine-scale spatial endemicity surface was produced for annual average incidence, followed by a spatio-temporal modelling of seasonal fluctuations in weekly incidence and aggregated further to district level. A seasonal profile was inferred across most districts of the country, where cases rose from late December/early January to a peak around early April and then declined rapidly to a low level from July to December. There was a high degree of spatial heterogeneity in incidence, with much higher rates observed in the northern part and some local epidemic occurrence in specific districts sporadically.ConclusionsWhile the study acknowledges certain limitations, such as population mobility and incomplete clinical case reporting, it underscores the importance of continuously refining geostatistical techniques to provide timely and accurate support for malaria elimination efforts. The high-resolution spatial risk maps presented in this study have been instrumental in guiding the Namibian Ministry of Health and Social Services in prioritizing and targeting malaria prevention efforts. This two-stage spatio-temporal approach offers a valuable tool for identifying hotspots and monitoring malaria risk patterns, ultimately contributing to the achievement of national and sub-national elimination goals.

The map behind the roadmap—Introducing a geospatial energy model for utility-scale solar and wind power buildout in Kenya

The map behind the roadmap—Introducing a geospatial energy model for utility-scale solar and wind power buildout in Kenya

Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis

Cooking with traditional fuels can lead to severe health issues caused by household air pollution, and can also affect gender equality and drive environmental degradation. In Nepal, despite government efforts to promote electric cooking, more than half of the population still uses traditional fuels, with electric cooking adoption remaining below 1%. Several of the barriers to and enablers of clean cooking vary geographically; however, few studies have considered spatial explicit information in planning national-scale transitions to clean cooking. In this study we provide a spatially explicit roadmap to estimate the required investments and benefits gained from the transition across Nepal. This study uses geospatial modelling methods to evaluate strategies to achieve the Government of Nepal's vision for a national-scale transition to clean cooking. We integrate the open-source clean cooking geospatial assessment tool OnStove and a spatial multicriteria analysis model. With OnStove, we evaluate which cooking technologies and fuels maximise the net benefits of a clean-cooking transition across each km2 of the region. With the multicriteria analysis, we weigh stakeholder preferences and prioritise areas of action where policy should be implemented. We used the most up-to-date geospatial data to the year 2023, such as the High Resolution Settlement Layer, Open Street Maps' road networks, the Global Human Settlement Layer, NASA/USGS forest cover maps, and Facebook's Relative Wealth Index, among others. We also relied on data from the Nepal Oil Corporation, the Nepal Electricity Agency, the Central Bureau of Statistic's 2021 national census, and the Alternative Energy Promotion Center. We evaluate four scenarios capturing advances on clean cooking policy up to the year 2022, current market inefficiencies, and the potential effects of new policies for clean-cooking transition in Nepal. Our results show that transitional and clean cooking technologies provide higher net benefits than traditional options everywhere across Nepal in all scenarios. Our net-benefit analysis shows that around 9563 deaths could be averted yearly if benefits and externalities were perceived and valued correctly. Furthermore, substantial benefits could be achieved in regard to greenhouse gas emissions avoidance, time saved, and health-cost reductions. Our results also show that the current subsidy strategy from the Government of Nepal is well aligned with the benefits achieved under a cost-benefit analysis. In this context, electric cooking can bring the highest benefits to the largest part of the population. The analysis showed how high subsidies for liquefied petroleum gas in Nepal can present trade-offs with energy security and independence, and how this could be avoided by transferring part of the subsidy to cover differentiated electric cooking tariffs. Accounting for stakeholder preferences and sociodemographic and geographical differences to prioritise areas of focus can balance affordability constraints and target the most vulnerable people first, thus achieving integrated and inclusive planning. Using spatially explicit modelling approaches to evaluate strategies for a clean cooking transition can provide more nuanced results that have not been possible before. This approach can enable data-driven and integrated planning to help to understand which locations of a study area should be prioritised for policy application. Integrated planning can help to reduce affordability constraints on the population and design strategies for a sustainable and inclusive transition. These strategies allow financial institutions, donors, impact investors, development organisations, and government agencies to use their resources, funds, and assistance to create a large impact. Clean Cooking Alliance.

Techno-economic and environmental assessment of green hydrogen and ammonia production from solar and wind energy in the republic of Djibouti: A geospatial modeling approach

This study conducts a thorough economic and technical analysis to assess the viability of green hydrogen and green ammonia production using renewable energy sources in the Republic of Djibouti. We explore the economic competitiveness of utilizing wind and solar power for sustainable energy production through various measures including levelized cost of energy (LCOE), hydrogen (LCOH), and ammonia (LCOA). Our analysis incorporates sensitivity assessments and Monte Carlo simulations to predict financial feasibility and environmental impact, focusing on CO2 emission reductions. A cost mapping of electricity, green hydrogen, and green ammonia from solar and wind resources was created to find Djibouti's most cost-effective production sites. The feasibility of exporting green ammonia to neighboring countries with high fertilizer demand was also evaluated using rail and truck transport scenarios. Key findings demonstrate that Moulouhlé, endowed with robust wind and solar resources, presents a strategic site for deploying renewable energy technologies. Wind energy, with a lower LCOE of 0.066 $/kWh compared to solar's 0.093 $/kWh, emerges as a more cost-effective solution for both hydrogen and ammonia production. Notably, wind-powered hydrogen production costs are 2.25 $/kgH2, significantly lower than solar-powered costs at 4.17 $/kgH2. In terms of green ammonia, wind power achieves production costs of 399.76 $/tonNH3, outperforming solar power which stands at 537.11 $/tonNH3. Additionally, our study evaluates the logistics of exporting green ammonia, determining rail transport as a more economically viable option than trucking. This research not only underscores Djibouti's potential as a leader in green energy exportation but also aligns with global decarbonization efforts, showcasing significant CO2 savings—154.94 Mt annually from wind. This work emphasizes the importance of strategic resource utilization in Djibouti, offering insights critical for stakeholders in making informed decisions about investing in renewable energy infrastructures. The outcomes suggest a promising future for regional energy sustainability and economic resilience, fostering a transition towards low-carbon energy systems.

Geospatial Modelling of Flood Susceptibility of the Calabar City, Cross River State, Nigeria

Flooding remains a major environmental problem in many parts of the world including Nigeria, causing untold losses. This study modelled the flood susceptibility of Calabar Metropolis using frequency ratio and geographical information system (GIS). Direct field observation and key informant approaches were used to obtain 127 previous and current flood locations in the study area from which a database of historical flood occurrence was developed. Using the geostatistical analyst tool within a geographical information environment the flood locations were split into two parts in the ratio of 70:30 percent for the training and testing processes, respectively. Eight flood conditioning factors (elevation, slope, aspect, curvature topographic position index, topographic wetness index, land cover and normalized difference vegetation index) were extracted from SRTM-30m DEM and Landsat 8 data accordingly and used in the geospatial analysis of flood occurrence within the GIS. The frequency ratio model was then developed using the training dataset. The final product was the flood susceptibility map of the study area. The results revealed that 9.3 percent of the study area was considered to have very high flood susceptibility, 19.97 percent was classified as high flood susceptibility while 31.79 percent was under moderate risk area. About 39 percent of the study area was classified at least under low flood susceptibility. The order of contribution of the conditioning factors to the model were topographic position index (TPI), elevation, normalized difference vegetation index (NDVI), land cover, curvature, aspect, slope, and topographic wetness index (TWI). The validation of the model using the receiver operating characteristics (ROC) curve returned an AUC value of 74.81 percent. Hence, the result was acceptable for the prediction of flood locations in the study area. The findings would assist urban planners, environmental managers, risk reduction and management agencies, land developers, policy makers, and indeed the general public.

Potentials of Green Hydrogen Production in P2G Systems Based on FPV Installations Deployed on Pit Lakes in Former Mining Sites by 2050 in Poland

Green hydrogen production is expected to play a major role in the context of the shift towards sustainable energy stipulated in the Fit for 55 package. Green hydrogen and its derivatives have the capacity to act as effective energy storage vectors, while fuel cell-powered vehicles will foster net-zero emission mobility. This study evaluates the potential of green hydrogen production in Power-to-Gas (P2G) systems operated in former mining sites where sand and gravel aggregate has been extracted from lakes and rivers under wet conditions (below the water table). The potential of hydrogen production was assessed for the selected administrative unit in Poland, the West Pomerania province. Attention is given to the legal and organisational aspects of operating mining companies to identify the sites suitable for the installation of floating photovoltaic facilities by 2050. The method relies on the use of GIS tools, which utilise geospatial data to identify potential sites for investments. Basing on the geospatial model and considering technical and organisational constraints, the schedule was developed, showing the potential availability of the site over time. Knowing the surface area of the water reservoir, the installed power of the floating photovoltaic plant, and the production capacity of the power generation facility and electrolysers, the capacity of hydrogen production in the P2G system can be evaluated. It appears that by 2050 it should be feasible to produce green fuel in the P2G system to support a fleet of city buses for two of the largest urban agglomerations in the West Pomerania province. Simulations revealed that with a water coverage ratio increase and the planned growth of green hydrogen generation, it should be feasible to produce fuel for net-zero emission urban mobility systems to power 200 buses by 2030, 550 buses by 2040, and 900 buses by 2050 (for the bus models Maxi (40 seats) and Mega (60 seats)). The results of the research can significantly contribute to the development of projects focused on the production of green hydrogen in a decentralised system. The disclosure of potential and available locations over time can be compared with competitive solutions in terms of spatial planning, environmental and societal impact, and the economics of the undertaking.

Enhancing the accuracy of wind power projections under climate change using geospatial machine learning models

This paper presents a geospatial artificial intelligence (GeoAI) approach for generating wind power projection maps employing various Machine Learning (ML) models. These models include Artificial Neural Network (ANN), Decision Tree (DT), Gaussian Process Regression (GPR), and Support Vector Regression (SVR), which collectively aim to provide insightful wind power forecasts under the effects of climate change. The framework considers different influential parameters affecting wind speed, including pressure gradient, temperature gradient, humidity, and topography. The study’s geographic focus is Cork City, Ireland. The investigation covers a historical period from 2000 to 2014 and extends to encompass two future climate scenarios, between 2015 and 2050. A comprehensive set of statistical skill scores is computed to gauge the models’ performance. The study’s findings underscore the efficacy of the ML models in generating dependable estimates of wind power fluctuations. Notably, the SVR model emerges as the frontrunner in performance across most pixels examined. Despite the inherent complexity of wind power dynamics, this research highlights that the SVR model can produce accurate wind power maps, even when operating with limited input data. The results emphasize the importance of considering influential factors in wind speed projections. This approach opens up promising avenues for improving the management of renewable energy resources.

Spatially explicit simulation and forecasting of urban growth using weights of evidence based cellular automata model in a millennium city of India

The present study focuses on quantifying and simulating the future urban growth based on the land use/land cover (LULC) data created from the Landsat images of the year 1999, 2011, and 2022. These LULC maps help in analysing the expansion of urban areas over the years and forecast their potential growth in the future. The spatio-temporal processes of urban growth are quantified, and future patterns are simulated and forecasted using Weights of Evidence based Cellular Automata model built in Dinamica EGO (Environment for Geoprocessing Objects) platform. The process of urban growth was manifested through prominent contributing factors of infill expansion namely, distance to built-up areas, distance to main roads, population density, and public services etc. The model's performance was evaluated using Kappa statistics and the percentage of correct prediction (PCP) based two-way comparison method. For this purpose, the simulated map was first compared with the observed information of year 2022 using Kappa indices followed by the PCP value (90.40%) exhibiting high predictive ability of the model. These findings corroborate that the model can forecast the future urban growth scenarios effectively with reasonable accuracy. Based on the outcomes, the forecasting of future urban growth scenarios for years 2033 and 2044 was accomplished. Analysis of the LULC changes displays that urban land use will experience the highest increase. Growth in the study area is predicted to increase by 23.5% and 26.7% in year 2033 and 2044 respectively where new urban settlements can appear. The results demonstrated that an integrated geospatial model provides essential information about the pattern, simulation, and prediction of urban growth associated with various driving variables.

Geospatial modelling of ambient air pollutants and chronic obstructive pulmonary diseases at regional scale in Pakistan.

Pakistan is among the South Asian countries mostly vulnerable to the negative health impacts of air pollution. In this context, the study aimed to analyze the spatiotemporal patterns of chronic obstructive pulmonary disease (COPD) incidence and its relationship with air pollutants including aerosol absorbing index (AAI), carbon monoxide, sulfur dioxide (SO2), and nitrogen dioxide. Spatial scan statistics were employed to identify temporal, spatial, and spatiotemporal clusters of COPD. Generalized linear regression (GLR) and random forest (RF) models were utilized to evaluate the linear and non-linear relationships between COPD and air pollutants for the years 2019 and 2020. The findings revealed three spatial clusters of COPD in the eastern and central regions, with a high-risk spatiotemporal cluster in the east. The GLR identified a weak linear relationship between the COPD and air pollutants with R2 = 0.1 and weak autocorrelation with Moran's index = -0.09. The spatial outcome of RF model provided more accurate COPD predictions with improved R2 of 0.8 and 0.9 in the respective years and a very low Moran's I = -0.02 showing a random residual distribution. The RF findings also suggested AAI and SO2 to be the most contributing predictors for the year 2019 and 2020. Hence, the strong association of COPD clusters with some air pollutants highlight the urgency of comprehensive measures to combat air pollution in the region to avoid future health risks.

Geospatial Analysis of the Socioeconomic and Demographic Effects of Historic Coal Mining in the Greater Pittsburgh Region, Pennsylvania, USA

A geospatial model was developed to statistically assess the socioeconomic effects of coal mining in the greater Pittsburgh, Pennsylvania area by integrating home sale data, abandoned mine lands (AML) inventory “problem area” sites, and census demographic information. Results indicated that homes located within problem areas sold for an average of 28% ($58,600) less than homes outside of these regions. Demographic data revealed a notable disparity in the population distribution within Allegheny County mining problem areas as having a statistically significant larger Black population. This same trend was even more pronounced in urban areas. The study also established that areas influenced by past mining activities had a higher proportion of individuals without formal postsecondary education. Logistic regression models were created to analytically evaluate the relationship between predictor variables, specifically home sale price and Community Needs Index, to the probability of being situated within mining problem areas. The home sale analysis indicated a negative correlation between sale prices and the likelihood of residing in a mining-affected zone, implying that properties with lower prices are more commonly situated in these impacted areas. The CNI logistic regression model revealed a correlation between the probability of living in a mining problem area and overall higher community needs.