Geospatial Analysis

According to the Köppen-Geiger climate classification, Europe is under the influence of at least ten different climate types. Thus, various climates can be found, from the polar tundra and cold climate in the Alps and northern European regions, to hot-arid climate in southern parts of Spain. This level of climate diversity makes the European territory interesting for the analysis from the bioclimatic building design perspective. Therefore, the purpose of the research was to assess the bioclimatic potential of selected European locations. The calculation of bioclimatic potential was done by acquiring the typical meteorological year (TMY) data comprised of climate characteristics, such as air temperature, air relative humidity and received solar irradiance, which was later processed by BcChart tool. In order to make bioclimatic potential maps of Europe, the points with uniform point sampling were generated. Furthermore, several additional locations of great interest were selected based on population density. The bioclimatic potential was used to define the prevailing passive building design strategies and measures at the analysed locations. At the same time, the in-depth analysis was conducted using the geospatial data and GIS tools, where the bioclimatic potential results at the selected locations were additionally analysed in relation to Köppen-Geiger climate types. The resulting bioclimatic potential maps can be used as a relevant onset for the policy makers in order to improve regional development strategies for building design.

This paper provides the innovative approach of using a spatial extract, transform, load (ETL) solution for 3D building modelling, based on an unmanned aerial vehicle (UAV) photogrammetric point cloud. The main objective of the paper is to present the holistic workflow for 3D building modelling, emphasising the benefits of using spatial ETL solutions for this purpose. Namely, despite the increasing demands for 3D city models and their geospatial applications, the generation of 3D city models is still challenging in the geospatial domain. Advanced geospatial technologies provide various possibilities for the mass acquisition of geospatial data that is further used for 3D city modelling, but there is a huge difference in the cost and quality of input data. While aerial photogrammetry and airborne laser scanning involve high costs, UAV photogrammetry has brought new opportunities, including for small and medium-sized companies, by providing a more flexible and low-cost source of spatial...

According to the Köppen-Geiger climate classification, Europe is under the influence of at least ten different climate types. Thus, various climates can be found, from the polar tundra and cold climate in the Alps and northern European regions, to hot-arid climate in southern parts of Spain. This level of climate diversity makes the European territory interesting for the analysis from the bioclimatic building design perspective. Therefore, the purpose of the research was to assess the bioclimatic potential of selected European locations. The calculation of bioclimatic potential was done by acquiring the typical meteorological year (TMY) data comprised of climate characteristics, such as air temperature, air relative humidity and received solar irradiance, which was later processed by BcChart tool. In order to make bioclimatic potential maps of Europe, the points with uniform point sampling were generated. Furthermore, several additional locations of great interest were selected base...

This study investigates landuse and landcover changes in the lower Kumaon region which consists of Nainital, Champawat and Udham Singh Nagar district of the Kumaon Himalayas. Utilizing remote sensing and GIS techniques, the present work assesses the transformation of land patterns over a specified time frame of 2010 and 2020. By analyzing Landsat satellite imagery, the study aims to discern the extent and nature of land use alterations, identifying key drivers and implications for environmental management and sustainability. Through comprehensive spatial analysis and statistical methods, the research seeks to provide valuable insights into the dynamics of landuse change, facilitating planning and decision-making for regional development and conservation endeavors. The outcomes of the study contribute to a deeper understanding of the complex interactions between human activities and natural landscapes in the Himalayan region, offering valuable knowledge for policymakers, planners and environmental stakeholders striving to balance developmental needs with ecological preservation.

We used statistical models to provide the first empirical estimates of riparian buffer effects on the cropland nitrate load to streams throughout the Chesapeake Bay watershed. For each of 1,964 subbasins, we quantified the 1990 prevalence of cropland and riparian buffers. Cropland was considered buffered if the topographic flow path connecting it to a stream traversed a streamside forest or wetland. We applied a model that predicts stream nitrate concentration based on physiographic province and the watershed proportions of unbuffered and buffered cropland. We used another model to predict annual streamflow based on precipitation and temperature, and then multiplied the predicted flows and concentrations to estimate 1990 annual nitrate loads. Across the entire Chesapeake watershed, croplands released 92.3 Gg of nitrate nitrogen, but 19.8 Gg of that was removed by riparian buffers. At most, 29.4 Gg more might have been removed if buffer gaps were restored so that all cropland was buffered. The other 43.1 Gg of cropland load cannot be addressed with riparian buffers. The Coastal Plain physiographic province provided 52% of the existing buffer reduction of Bay-wide nitrate loads and 36% of potential additional removal from buffer restoration in cropland buffer gaps. Existing and restorable nitrate removal in buffers were lower in the other three major provinces because of less cropland, lower buffer prevalence, and lower average buffer nitrate removal efficiency.

The growth of urban structures in Ibadan, Nigeria, is a major challenge to urban renewal and good governance, which has led to the urban erosion, economic wastage and social decline. The current research evaluates the spatial distribution, physical conditions as well as governance aspects of abandoned structures in Ibadan so as to come up with evidenced based strategies to revitalize the urban areas. Using  mixed-methodology,  the  research  combines  spatial  analysis  with Geographic Information Systems (GIS), field surveys and statistical analysis in mapping  30  abandoned  public  buildings  in  ten  Local  Government  Areas (LGAs). The  results  indicate  a  great  concentration  of  the  number  of  abandoned buildings  in  the  center  cities,  especially  Ibadan  North,  North  East  and  North West  with  administrative  buildings  (30%),  and  residential  facilities  (20%), being  most  affected.  About  66.7 percent  of  these  buildings  are  completely vacated  with  56.7  percent  in  bad  structural  conditions  which  is  a  systemic problem in the management of the public assets. Some of the reasons that lead to the abandonment of some facilities are poor maintenance practices, evolving priorities of the state and poor institutional structures. They include creation of  Urban  Renewal  Authority,  incorporation  of  adaptive  reuse  policies,  and creating    a    GIS-based  asset  management    system,    which    will    improve governance and transform abandoned buildings, which the study suggests the creation  of.  The  objective  of  these  interventions  is  to  convert  the  forsaken buildings    into    a    useful    asset  to  grow    the    cultures,    economies,    and neighborhoods, and to provide a precedent to  the sustainable urban renewal in Ibadan and other urban settings of developing countries.

Аннотация. Предметом исследования выступает процесс инвестиционной деятельности корпораций, способствующего повышению уровня экономической эффективности инвесторов, корпораций и государства. Объектом исследования является развития инвестиционной деятельности корпораций в Российской Федерации. Цель данной работы -роль инвестиционной деятельности в процессе формирования и приращения капитала корпораций в российской экономике. В статье подробно описаны стадии процесса инвестиционной деятельности корпорации, позволяющие более детально изучить объекты инвестирования, скорректировать показатели экономической эффективности, определить уровень и возможность минимизации финансовых рисков на каждой из стадий планирования и инвестирования. Экономическая эффективность оценивается на заключительной стадии процесса инвестиционной деятельности корпорации, конечным уровнем которой является определение направления использования накопленного капитала (потребление, накопление, реинвестирование). По результатам проведенного исследования автор пришел к выводу о том, что процесс инвестиционной деятельности корпораций способствует минимизации финансовых рисков и создает условия для эффективной деятельности в долгосрочной перспективе. Таким образом, обоснована актуальность и эффективность созданного и представленного автором процесса инвестиционной деятельности корпорации, способствующего сохранению структуры капитала корпорации в положительной динамике и максимизации экономического эффекта для инвесторов.

In July 2011, YAP Films retained Archaeological Services Inc. to carry out a research-based archaeological investigation to identify the remains of the Government House on the grounds of the Fort York National Historic Site (AjGu-26) located within the City of Toronto (Figure 1). The archaeological fieldwork was used to tie together the storyline of the documentary film, Explosion 1812, that was released in June of this year.

Development and urbanization are inevitable phenomena, especially in rapidly economically advancing countries like Bangladesh, and have a substantial impact on changing the surface temperature. The research attempts to find the relationship between land use land cover changes and land surface temperature and the probable reasons for these changes during the period 2000 to 2020 employing GIS and remote sensing techniques and Landsat 5 (TM), 7 (ETM+) and 8 (OLI/TIRS) satellite imagery. The kappa coefficient ranges between 0.80 to 0.90, indicating the satisfactory accuracy of the work. Urbanization increased overwhelmingly in the area, enlarging from 14.77 km 2 in 2000 to 61.32 km 2 in 2020, showing a rise in the study period more than 4 times. The agricultural land was lowered by around 2.5 times at the same time. The overall land surface temperature implies an increasing trend from 22.82°C to 28.30°C i.e., approximately an increase of 6°C in the research period. Hence, policymakers and stakeholders can consider the consequences of the research to realize the current development scenario and rethink their verdicts to make the country suitable and more habitable for future generations.

Coastline Modeling Accuracy Assessment Developed By Using from Multi-Source Data. The coastal regions need to be developed because many big cities in Indonesia are located in these areas. However, it is crucial to determine the distance from the beach that is safe as the requirement for development along the coastal zone. The term of the beach is very closely affiliated with the coastline. The method of determining the coastline continues to be developed to fulfill the many needs related to the coastline. The coastline has a dynamic position. The land contour along the coast and the tide's state become several things that affect the coastline. Therefore, a dynamic model is required to define coastline positioning because both conditions are easy to change. The coastline determination from multi-source data modeling using DEM results is rarely done. In this study, coastline determination uses land height contours combined with sea depth contours and uses Mean Sea Level (MSL) valu...

The use of fishing aids called Fish Aggregating Devices (FADs) has become a polemic in the context of realizing sustainable fisheries management. On the one hand, the use of FADs can increase fishermen’s catches. However, on the other hand, the use of FADs without proper management will result in unsustainable fish availability due to overfishing. The FADs used are also not always correlated with increased catches. At certain times, FADs used by fishermen can be lost because they are carried away by the current, which is clearly very detrimental to both fishermen and the environment. Utilization of geospatial information is one of the efforts to connect existing policies regarding the use of FADs, safety aspects, and optimizing the number of fishermen’s catches. The main focus of this study is the utilization of geospatial information such as the modeling of ocean currents, analysis of potential fishing zones, analysis of sea depth positions, and analysis of prohibited zones for the...

Outdoor ambient sound levels can be predicted from machine learning-based models derived from geospatial and acoustic training data. To improve modeling robustness, median predicted sound levels have been calculated from an ensemble of tuned models from different supervised machine learning modeling classes. The ensemble is used to predict ambient sound levels throughout the contiguous United States. The training data set consists of 607 unique sites, where various acoustic metrics, such as overall daytime L 50 levels and one-third octave frequency band levels, have been obtained. Data for 117 geospatial features, which include metrics such as distance to the nearest road or airport, are used. The spread in the ensemble provides an estimate of the modeling accuracy. Results of an initial leave-one-out and leave-four-out validation study are presented.

Using Machine Learning to Accurately Predict Ambient Soundscapes KATRINA PEDERSEN, BROOKS BUTLER, KENT GEE, MARK TRANSTRUM, Brigham Young University -The ability to accurately characterize the soundscape, or combination of sounds, of various areas is valuable to the United States military and the National Park Service. It also holds weight in areas such as epidemiology and ecology. We attempt to use machine learning to correctly predict ambient sound levels throughout the contiguous United States. Our data set consists of roughly 500 training sites, where various acoustic metrics, such as overall daytime L50 levels and frequency band levels, have been obtained. We also have roughly 115 geospatial features, which include distances to the nearest roads or airports, and the percentage of industrialization or forest in a specific area. Selecting useful and physically significant features is an important step in the process of creating a good model. Feature importance measures, such as the Gini importance, help guide feature reduction. Ideally, the feature importance rankings will have physical significance and vary depending on which acoustic metric we have created the model for. We discuss the results and process of feature reduction on our model.

This paper describes the development of an automated classification algorithm for detecting instances of focused crowd involvement present in crowd cheering. The purpose of this classification system is for situations where crowds are to be rewarded for not just the loudness of cheering, but for a concentrated effort, such as in Mardi Gras parades to attract bead throws or during critical moments in sports matches. It is therefore essential to separate non-crowd noise, general crowd noise, and focused crowd cheering efforts from one another. The importance of various features-both spectral and low-level audio processing features-are investigated. Data from both parades and sporting events are used for comparison of noise from different venues. This research builds upon previous clustering analyses of crowd noise from collegiate basketball games, using hierarchical clustering as an unsupervised machine learning approach to identify low-level features related to focused crowd involvement. For Mardi Gras crowd data we use a continuous thresholding approach based on these key low-level features as a method of identifying instances where the crowd is particularly active and engaged.

Outdoor ambient sound levels can be predicted from machine learning-based models derived from geospatial and acoustic training data. To improve modeling robustness, median predicted sound levels have been calculated from an ensemble of tuned models from different supervised machine learning modeling classes. The ensemble is used to predict ambient sound levels throughout the contiguous United States. The training data set consists of 607 unique sites, where various acoustic metrics, such as overall daytime L 50 levels and one-third octave frequency band levels, have been obtained. Data for 117 geospatial features, which include metrics such as distance to the nearest road or airport, are used. The spread in the ensemble provides an estimate of the modeling accuracy. Results of an initial leave-one-out and leave-four-out validation study are presented.

У статті наголошено на актуальності досліджень концепту “геопросторова організація суспільства” як теоретичної основи реформування адміністративно-територіального устрою України, здійснення регіональної політики держави, ухвалення рішень у сфері розміщення виробництва та інфраструктури, природокористування та екологічної безпеки. З’ясовано, що класичні теоретичні напрацювання в сфері геопросторової організації суспільства стосуються переважно реального геопростору і не відображають
функціонування суспільства за умов його цифровізації, мережевізації та віртуалізації.
Проаналізовано теоретичну спадщину класиків української суспільної географії щодо предметної сутності концепту “геопросторова організація суспільства”. Зокрема, висвітлено концептуалізацію поняття за допомогою термінолексем “розміщення”, “зв’язки”, “збалансованість”, “реструктуризація”, “управління” у працях М. Пістуна. Зазначено, що О. Шаблій вважав ключовими поняттями геопросторової організації суспільства “взаєморозташування”, “зв’язки”, “територіальні утворення” та “функціонування об’єктів взаємодії”. Висвітлено тринітарну сутність концепту через геосферну взаємопов’язаність земних явищ, мінливість земних явищ, геопросторову організованість суспільства в працях В. Грицевича. З’ясовано конструктивізм поняття геопросторова організація суспільства в працях О. Топчієва.
Зроблено висновок, що класичні означення геопросторової організації суспільства описують реальні просторові форми, яким властива точність, визначеність меж та умовна статичність. Виявлено формування віртуальних геопросторових форм організації суспільства (соціальні та наукові інтернет-мережі, науково-інноваційні та хмарні виробничі мережі), що засвідчує детериторіалізацію класичних суспільних структур та становлення мережевої топології сучасного геопростору. Обґрунтовано розширення змісту концепту через інтеґрацію інструментів державної політики, екологічних імперативів та можливостей цифрової інфраструктури. Спрогнозовано синергію регіональних та віртуальних геопросторових форм, формування гібридної геореальності як основи сучасної трансформації поняття “геопросторова організація суспільства”.

Unmanned aerial vehicles (UAVs), also known as drones, are being increasingly applied in different demands and applications, mainly in mapping. Despite the agility and practicality provided by this technology, the image processing software programs currently available on the market are costly and cannot meet teaching/research demands, especially in Brazilian public universities. In this context, this study aimed to evaluate the positional accuracy of products resulting from the digital processing of UAV images using commercial software (Agisoft Metashape) and open-source software (Opendronemap). The planimetric accuracy of the orthophoto mosaic resulting from the two software was not acceptable according to the tolerances defined in the standardization document for planimetric and altimetric accuracy for digital geospatial data, established by the ASPRS (American Society for Photogrammetry and Remote Sensing). Only the altimetric accuracy corresponding to the DEM produced by Opendro...

The Earth's environments confront serious stress as a result of unsustainable socioeconomic development, which is related to urbanization, industrialization and inhabitancy growth. Sustainable growth is the achievement of the present requirements without impacting the future's needs or choices. Utilization of remote sensing technologies for deriving environmental indicators could enable the status and condition of natural and controlled habitats to be mapped, monitored and determined so as to provide the required near-real-time data to identify if the environment is at-risk. This needs an interdisciplinary approach that recognizes at-risk landscapes and habitats. With the remote sensing data utilization, the distribution of green spaces and vegetation throughout Baghdad City have been studied through the analysis of normalized vegetation index (NDVI). Land cover and land surface temperature (LST) relationship study revealed a weak negative relationship between these two variables. Using Landsat 8, LST and land cover data were retrieved for the month May in the years 2013, and 2021. The outcome reveals that vegetation dropped by 8.6 percent, while a built-up region experienced the biggest increase in land cover changes of 13.1%. The areas with the LST highest distribution were in the 35-39°C class, with the average LST value changing by 0.39°C over the past 8 years. The results of the LST research in 2021 demonstrate that Baghdad's downtown and several other densely populated areas have experienced the Urban Heat Island phenomena. The government is intended to increase public knowledge of the UHI phenomenon in Baghdad so that preventative measures can be done so the consequences of the Urban heat island (UHI) do not spread further. The average surface temperature in four LULC classes was analyzed, and the results showed that heat islands fully complied with the LULC categories.

Protection of coastal cultural heritage is among the most urgent global priorities, as these sites face increasing threats from climate change, sea level rise, and human activity. This study emphasises the value of innovative geospatial tools and data ecosystems for timely risk assessment. The role of land administration systems, geospatial documentation of coastal cultural heritage sites, and the adoption of innovative techniques that combine various methodologies is crucial for timely action. The coastal management infrastructure in Greece is presented, outlining the key public authorities and national legislation, as well as the land administration and geospatial ecosystems and the various available geospatial ecosystems. We profile the Hellenic Cadastre and the Hellenic Archaeological Cadastre along with open geospatial resources, and introduce TRIQUETRA Decision Support System (DSS), produced through the EU's Horizon project, and a Digital Twin methodology for hazard identification, quantification, and mitigation. Particular emphasis is given to the role of Digital Twin technology, which acts as a continuously updated virtual replica of coastal cultural heritage sites, integrating heterogeneous geospatial datasets such as cadastral information, photogrammetric 3D models, climate projections, and hazard simulations, allowing for stakeholders to test future scenarios of sea level rise, flooding, and erosion, offering an advanced tool for resilience planning. The approach is validated at the coastal archaeological site of Aegina Kolona, where a UAV-based SfM-MVS survey produced using high-resolution photogrammetric outputs, including a dense point cloud exceeding 60 million points, a 5 cm resolution Digital Surface Model, high-resolution orthomosaics with a ground sampling distance of 1 cm and 2.5 cm, and a textured 3D model using more than 6000 nadir and oblique images. These products provided a geospatial infrastructure for flood risk assessment under extreme rainfall events, following a multi-scale hydrologichydraulic modelling framework. Island-scale simulations using a 5 m Digital Elevation Model (DEM) were coupled with site-scale modelling based on the high-resolution UAVderived DEM, allowing for the nested evaluation of water flow, inundation extents, and velocity patterns. This approach revealed spatially variable flood impacts on individual structures, highlighted the sensitivity of the results to watershed delineation and model resolution, and identified critical intervention windows for temporary protection measures. We conclude that integrating land administration systems, open geospatial data, and Digital Twin technology provides a practical pathway to proactive and efficient management, increasing resilience for coastal heritage against climate change threats.

The 2005 "Last Chance Bravo" bioterrorism exercise drill was accomplished in the state of Montana between June 20th and June 24th, 2005, providing a forum for demonstrating the efficacy of technologies such as geographic and geospatial information systems, improved telemedicine and telehealth solutions, and wireless robotics. Federal, state, and local agencies, technology professionals and vendors, and healthcare organizations joined forces in a collaborative effort to share information, test solutions and develop shared management processes to better facilitate future disaster response activities.

Accurate soil moisture prediction is essential for advancing agricultural productivity, optimizing water resource management, and strengthening climate adaptation strategies, particularly in hydrologically vulnerable regions such as Louisiana. This study investigates the integration of the newly developed OPtical TRApezoid Model (OPTRAM) with conventional moisture-related vegetation indices to enhance soil moisture prediction. A suite of indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Enhanced Vegetation Index (EVI), Structure Insensitive Pigment Index (SIPI), Atmospherically Resistant Vegetation Index (ARVI), and Normalized Difference Moisture Index (NDMI), was compared against OPTRAM to evaluate predictive capacity. Support Vector Machine with Radial Basis Function (SVM-RBF) kernel and Random Forest (RF) algorithms were employed using Sentinel-2 imagery coupled with Louisiana weather station records. Model performance was validated using statistical metrics (R², RMSE, and MAE). Results revealed that RF achieved MAE = 0.054, RMSE = 0.069, and R² = 0.539, while SVM-RBF achieved MAE = 0.065, RMSE = 0.079, and R² = 0.690. OPTRAM demonstrated superior performance with RF, whereas indices such as NDMI, EVI, SIPI, and NDWI outperformed OPTRAM in moisture prediction with SVM-RBF. These findings highlight the importance of integrating advanced optical models with machine learning approaches to improve soil moisture monitoring and support sustainable land management in Louisiana.

Health and Sanitation are the two major limbs that support Swachh Bharat Abhiyaan. This scheme was launched by the Indian Government targeting towards achieving a more clean and healthy India. The present study was done for estimating the cleanliness and development of Tiruchirapalli city located in the state of Tamil Nadu. Geospatial & Statistical tools were used for analyzing and ranking different wards. Panchromatic & MSS merged image and ancillary data obtained from Trichy Municipal Corporation was used to derive necessary GIS layers. Adequacy of the public toilets for each ward was found out using spatial layers and mathematical equation derived from census and sanitation data. Development Index was generated for each ward giving appropriate ranking by considering parameters such as public toilets, population, built-up densities and NDVI for each ward. The results were used for estimating the adequacy of public toilets in each ward and deciding the best wards based on ranks obtained from weighted values of different developmental parameters.

Recent advancements in smart technologies tender the use of artificial technologies in the energy systems more practical. The focus of this paper is on designing an innovative smart energy system with the aid of cloud computing. It aims to address certain primary issues in energy demand forecasting, load balancing, and automation in decentralized control management systems. A proposed system which utilizes hybrid architecture based on integrated predictive analytics and control distributed automation and decision systems, across multiple diverse energy resources, demonstrates transformative control automation and predictive control capabilities, using edge artificial intelligence, such energy clouds, and federated control. The artificial intelligence system provides edge control within federated control architecture on the energy cloud, the system analytics predictive control and control functions which offer automated decision-making. The decisions made in the automation of several energy assets boost the system predictive control capabilities. The enhanced predictive control capabilities offer reduction in operational energy systems, operational cost and carbon, and overall system carbon footprint the operational energy system efficiency. The cloud artificial intelligence systems bring transformative changes to the operational energy system efficiency, operational cost, and overall system carbon footprint. It paves the way toward a new, self-governing paradigm for smart and autonomous ecosystems sustainable energy systems.

Crime against women (CAW) is a persistent challenge in West Bengal. This study provides actionable insights through a comprehensive spatio-temporal analysis of six major forms of CAW across West Bengal, using current data from the National Crime Records Bureau (NCRB) for 2017-2022. These crimes include 'dowry deaths', 'cruelty by husband or his relatives', 'kidnapping and abduction of women', 'human trafficking', 'rape', and 'assault on women with intent to outrage her modesty.' Focusing on these crimes, this study adopted a multifaceted methodological approach to uncover their spatial and temporal patterns in the state. This study employs advanced spatial analysis techniques, such as the Crime Concentration Index to measure crime clustering, the Getis-Ord Gi* statistic to identify local clusters of high or low crime, and Anselin's Local Moran's I to pinpoint local clusters and outliers. By integrating these methods, this study overcomes the limitations of prior research that lacks sophisticated spatial analysis, providing a deeper and more precise understanding of spatial crime patterns across the districts of West Bengal at a higher spatial resolution. This enhanced insight supports evidence-based policy formulation, facilitates targeted interventions, and ultimately contributes to building a safer and more equitable society for women.

Background Lymphatic filariasis is a neglected tropical disease that can cause permanent disability through disruption of the lymphatic system. This disease is caused by parasitic filarial worms that are transmitted by mosquitos. Mass drug administration (MDA) of antihelmintics is recommended by WHO to eliminate lymphatic filariasis as a public health problem. This study aims to produce the first geospatial estimates of the global prevalence of lymphatic filariasis infection over time, to quantify progress towards elimination, and to identify geographical variation in distribution of infection. A global dataset of georeferenced surveyed locations was used to model annual 2000-18 lymphatic filariasis prevalence for 73 current or previously endemic countries. We applied Bayesian model-based geostatistics and time series methods to generate spatially continuous estimates of global all-age 2000-18 prevalence of lymphatic filariasis infection mapped at a resolution of 5 km² and aggregated to estimate total number of individuals infected. Findings We used 14 927 datapoints to fit the geospatial models. An estimated 199 million total individuals (95% uncertainty interval 174-234 million) worldwide were infected with lymphatic filariasis in 2000, with totals for WHO regions ranging from 3•1 million (1•6-5•7 million) in the region of the Americas to 107 million (91-134 million) in the South-East Asia region. By 2018, an estimated 51 million individuals (43-63 million) were infected. Broad declines in prevalence are observed globally, but focal areas in Africa and southeast Asia remain less likely to have attained infection prevalence thresholds proposed to achieve local elimination. Interpretation Although the prevalence of lymphatic filariasis infection has declined since 2000, MDA is still necessary across large populations in Africa and Asia. Our mapped estimates can be used to identify areas where the probability of meeting infection thresholds is low, and when coupled with large uncertainty in the predictions, indicate additional data collection or intervention might be warranted before MDA programmes cease. Funding Bill & Melinda Gates Foundation.

In recent decades, geospatial modeling has played an increasingly central role in how electricity is planned and delivered in Sub-Saharan Africa (SSA). This systematic review synthesizes findings from 57 peer-reviewed studies and institutional reports published between 2010 and early 2024. It evaluates the use of spatial tools, electrification strategies, and the social impacts of energy planning. The review adopts the PRISMA and SALSA frameworks to analyze the types of modeling tools applied, including OnSSET, HOMER Pro, and PyPSA-Earth, and their effectiveness in generating least-cost electrification pathways. Findings highlight that hybrid approaches that combine grid, mini-grid, and off-grid systems are generally the most cost-effective. The geospatial strategies demonstrate measurable impacts on education, health, income, and gender equity, although gender metrics remain underreported. Persistent spatial inequalities especially in rural and marginalized areas are exacerbated by policy gaps, weak data governance, and institutional fragmentation. This paper emphasizes that the success of geospatial electrification tools depends not only on technical innovation but also on governance reforms and a deliberate focus on spatial justice. It offers practical insights for policymakers, researchers, and development partners to ensure electricity planning in SSA is inclusive, data-driven, and development-oriented.

У статті з'ясовано сутність поняття «наукова мова» як складної, відкритої, динамічної системи мовних засобів для здобуття, зберігання та поширення наукового знання. Апробовано мовно-науковий метод репрезентації суспільно-географічного знання. На основі комплексного аналізу наукових текстів професора Олега Шаблія розкрито такі змістово-функціональні риси и ого наукової мови: об'єктивність, історизм, синкретизм, узгодження філософської , загально-і конкретно наукової термінології , метатеоретичний рівень, системність, логічна формалізація, міждисциплінарність, інтертекстуальність, геогуманізм, геософійність, україноцентризм, адаптивність, комунікативна ефективність. Наголошено на конструктивізмі наукової мови О. Шаблія та її значенні для подальшого розвитку теорії і методології географічної науки, організації конкретних суспільно-географічних досліджень.

Dynamics of tourism is a complex thing requires geospatial approach for effective planning and assessment. Geospatial technique for visualization is an innovation and emerging discipline and a valuable tool to recognized the dynamic assessment of tourism, by integrating geospatial data as well as non spatial data in the form of a map display as an outcome. Geospatial visualization approach necessitates the planning which is based on the needs, goals, formulation, monitoring and evaluation of an action plan or programmes. Present study practices a geovisualization of dynamic assessment of tourism in Bageshwar district by integration of spatial and non-spatial data and also explores the suggestions to develop the tourism industry. The study investigated a case study in Bageshwar district. Study area has extensive potentialities for expansion of tourism industry, so in the region there is need to make efforts in this direction and make region more attractive and suitable for tourist as...

An Unpublished Cartography of Granada (Spain) From the Beginning of the XXTH Century: Bases for its data integration in a GIS Currently, the analysis, planning and management of cities and territories use some tools -nowadays nearly unavoidable-that are experiencing an extraordinary development in the last decades. All these tools are related to the Geographic Information Technology (GIT). With GIT we are talking about the Geographic Information Systems (GIS) and the Spatial Data Infrastructure (SDI). Recent studies carried out about historical cartography are demonstrating that they are not beyond this trend. Many are the advantages on the use of these tools: the georeferencing of the information contained, the variety of geospatial analysis permitted, and above all, the dissemination which this information can experience through the SDI. These, are precisely the tools we want to use in the unpublished historical cartography of Granada. Nevertheless, this study is also concerned on proposing a methodology that will meet the former objectives. And will also take into account previous experiences

Modern handheld devices, i.e. smartphones, have gone beyond their basic purpose for some time now. Beside powerful screens, which usually surpass those we use on our desktops, powerful cameras, and internet connectivity, these devices usually integrate many sensors. In this paper, we discuss how we can combine smart phone’s camera, connectivity, GPS receiver, accelerometer, and magnetometer in order to transform this kind of a device into geospatial video source. To validate and test suggested solution, we implemented a simple mobile app that fetches frames and sensors’ data, calculates georeference and streams geospatial video. In the paper, we also discussed limitations that are caused by the limited precision of used sensors.

A Smart City is the integration of technology into a strategic approach to sustainability. 21st Century has brought with it a new global trend of "sustainable urban development" and this concept adds new dimensions to urbanization which require a quick need to upgrade existing cities. The concept of a smart city is a relatively new one. Throughout the years, with the significant contribution from various technologies like computer science, information technology, remote sensing, advance multimedia world etc, GIS evolved from traditional geographer's or cartographer's tool for surveying and planning to a rapidly expanding primary technology for understanding our planet and related geospatial opportunities to foster a sustainable world. This paper describes smart city projects in India and thoroughly explores the existing and extended capability of GIS to include modeling and visualization in order that decision makers will be able to see the state of the resources s...

The study is a geospatial surface area mapping of Bokkos LGA physical features through the aid of geographic information system (GIS) application that was used to analyze surface conditions plotted from reported data which dwell the contour, slope, aspect, curvature and hill shade maps as well as a land-use map. The morphometric parameters were derived from the Digital Elevation Model (DEM) using ArcGIS. The result revealed 433 contours with 100m intervals with less around Kwatas, Bokkos, and Butura and very high around Mushere west and central. The slope was very high around Mushere west/ central, Mangar/Damwai, Dafffo, Sha and Mangunahave moderate slope. In areas of Mushere, Mangar and Damwai runoff was high, soil erosion indicated sign of very high adverse effects on the environment. Aspect revealed red colour, North, orange Northeast and yellow colour tilt to the east while the green southeast. Hillshade was observed lesser in Mushere west/central, Damwai with a curvature map for soil erosion patterns and Vegetation covers were predominant certain areas of Bokkos and the study gave appropriate recommendation to agency concerned.

The study examined the application of Remote Sensing and Geographic Information Systems in estimating the potential health effects of solid waste dump sites, located close to the residences within Jalingo town, in mapping out the existing dumpsites, in carrying out proximity analysis, and in assessing the impact of these dump sites on residences within the study area. Both, primary and secondary data were used to achieve the objectives of the study. The primary data were the geographical coordinates of all the dumpsites from the selected five wards in the study area. This was obtained using hand-held GPS. The secondary data applied included administrative boundary maps (Ancillary data) and recorded cases of malaria incidence from health facilities located in the area. Data were analyzed in ArcGIS 10.3 environment to display the X and Y coordinates and to obtain the spatial distribution of the dumpsites on a composites map. Geo-processing using ring buffering was carried out to form ...

Though environmentally friendly in many regards, local supply chains are often inefficient due to lack of proper infrastructure. This paper explores the use and placement of mobile hubs for consolidation and distribution of goods in local supply chains. Specifically, we look at local food supply chains where food typically travels from rural farms to suburban and urban restaurants. Currently, consolidation is minimal and not optimized in these supply chains. This paper computes suitability and location analysis through a novel multi-criterion scoring methodology utilizing kernel density and network analysis. The effectiveness of these mobile hubs is assessed through strategic routing, where the routes are optimized for time and distance. Results indicate that on average mobile hubs do in fact reduce mileage and number of stops, lessening emissions in addition to saving time and money. The proposed methodology can be implemented in other local supply chains to better consolidate and distribute goods.

This paper deals with the issue of optimizing geospatial search algorithms in large volumes of geodata by integrating geospatial data into the structure of the Semantic Web. In this context, integration means transforming information from geodata sets into an RDF data model. This means that the information is structured by describing it in a vocabulary and correlating it with other information. A significant distinction is made between vector and raster data, as approaches to their integration are at different stages of development. Regarding the basic principles of vector data conversion, geometries are stored according to a specific syntax, usually in the form of a string. This string can, for example, contain information about the type of geometry (e.g., point, line, polygon) as well as the anchor points (as pairs of coordinates). Spatial relationships are defined before conversion to RDF and are explicitly stored as triples. There is no single standard for conversion, so the syntax may differ from organization to organization. To create geospatial queries, a modified version of SPARQL, GeoSPARQL, is used to work with geospatial data, which is both a query language and a data format. There are several approaches to transforming raster data, and, accordingly, methods should first be developed on how to implement such a transformation. This article presents a tool designed to enable users to seamlessly convert spatial data from common GIS formats to the RDF model, thereby greatly increasing the versatility and interoperability of spatial data representation. This tool not only facilitates the generation of geometries represented as GeoSPARQL WKT literals, but also offers structured geometries accessible exclusively through SPARQL queries, thus giving users full access to spatial data in a very adaptive and efficient way.

PURPOSE Cervical cancer (CC) is the most common and second-most deadly cancer among Peruvian women. Access to services is strongly associated with CC screening uptake. This study investigated geospatial features contributing to utilization of screening. We used geolocated data and screening information from a Knowledge, Attitudes, and Practice (KAP) survey implemented in Iquitos, Peru in 2017. MATERIALS AND METHODS The KAP collected cross-sectional CC screening history from 619 female interviewees age 18-65 years within 5 communities of varying urbanization levels. We used spatial statistics to determine if screened households tended to cluster together or cluster around facilities offering screening in greater numbers than expected, given the underlying population density. RESULTS On the basis of K-functions, screened households displayed greater clustering among each other as compared with clustering among unscreened households. Neighborhood-level factors, such as outreach, commun...

Accessibility to an urban medical emergency facility in the metropolis is provided to enhance the city's medical emergency preparedness and response capabilities to disaster and critical emergencies. The purpose is to save the lives of the urban dwellers within the city and its environs. Urban medical services are essential for ensuring a better quality of life and advancing social welfare. This study seeks to evaluate the efficiency and effectiveness of emergency response operations in Port Harcourt. The effectiveness and fairness of medical facility allocation are determined by assessing the spatial accessibility of these facilities within a coverage area. Urban residents can exchange information regarding medical services and facilities thanks to this type of spatial evaluation. By examining the relationships between the primary, secondary, and tertiary medical facilities located inside metropolitan regions, this study seeks to close this research gap. The research on accessibility and spatial distribution with the interrelationship of these facilities in the city of Port Harcourt employs Geographic Information System (GIS) technology to analyze the location and accessibility of key urban medical emergency facilities such as hospitals. This paper aims to analyze the spatial distribution of urban medical emergency response services and facilities in the study area, as classified into; Primary healthcare centers (PHC), Secondary Healthcare Facilities and Tertiary Health facilities. Nearest Neighbor Analysis (NNA) was employed, and the findings revealed both clustered and dispersed distribution patterns

Decision making is one of the subfields of Artificial Intelligence and has been used for various applications. One such application is Disaster Mitigation and Management. This study is on Landslide Zonation using different Decision Making Algorithms. Landslides are one of the most epoch-making hazards that affect different parts of India every year, during the rainy season, which cause not only colossal destruction to roads, bridges, and houses but also lead to loss of life. Mapping of the hazardous zones is one method by which awareness can be created among the localities and guiding the authorities in management of the disaster event. There are lots of literature discussing various methods to map landslide hazardous zones, from those studies, influential factors are identified and two multi criteria decision making algorithms namely TOPSIS and AHP are used to rank the factors, to assign weights to corresponding factors, finally Landslide Susceptibility Map of Saklespur, located in...

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Climate change is accelerating soil erosion, presenting a significant threat to food security and ecosystem health globally. This review investigates the impact of climate change on soil erosion using advanced geospatial methods and the Revised Universal Soil Loss Equation (RUSLE). An analysis of over 100 recent peer-reviewed articles (including research from 1990 to 2024) explores

The desert date seeds were collected from Gomo, washed, decocted, dried, and pulverized. The seed oil was extracted using soxhlet apparatus with n- hexane as solvent. The percentage yield of the extracted oil was found to be 40.5%. While the physicochemical analysis of the seed oil was conducted using standard procedures with the following results; specific density 0.98g/cm3, saponification value 134mgKOH/g, peroxide value 38meq/kg, acid value 12.6mgKOH/g, free fatty acid value 12.7mgKOH/g. These results indicate that Desert date seed oil could be a rich source of oil for industrial and domestic purposes if exploited richly.