Urban Pedestrian Routes' Accessibility Assessment Using Geographic Information System Processing and Deep Learning-Based Object Detection.

The need to establish safe, accessible, and inclusive pedestrian routes is considered one of the European Union's main priorities. We have developed a method of assessing pedestrian mobility in the surroundings of urban public buildings to evaluate the level of accessibility and inclusion, especially for people with reduced mobility. In the first stage of assessment, artificial intelligence algorithms were used to identify pedestrian crossings and the precise geographical location was determined by deep learning-based object detection with satellite or aerial orthoimagery. In the second stage, Geographic Information System techniques were used to create network models. This approach enabled the verification of the level of accessibility for wheelchair users in the selected study area and the identification of the most suitable route for wheelchair transit between two points of interest. The data obtained were verified using inertial sensors to corroborate the horizontal continuity of the routes. The study findings are of direct benefit to the users of these routes and are also valuable for the entities responsible for ensuring and maintaining the accessibility of pedestrian routes.

Assessment of vulnerability to flood risk in the Padma River Basin using hydro-morphometric modeling and flood susceptibility mapping.

An evaluation of flood vulnerability is needed to identify flood risk locations and determine mitigation methods. This research introduces an integrated method combining hydro-morphometric modeling and flood susceptibility mapping to assess Padma River Basin's flood risk. Flood zoning, flooding classes, and resource flood risk were explicitly analyzed in this river basin study. Flood risk was calculated using GIS-based hydro-morphometric modeling. Using Horton's and Strahler's methods, drainage density, stream density, and stream order of the Padma River Basin were determined. The Padma River Basin has five sub-basins: A, B, C, D, and E, with stream densities of 0.53 km-2, 0.13 km-2, 0.25 km-2, 0.30 km-2, and 0.28 km-2 and drainage densities of 0.63 km-1, 0.16 km-1, 0.29 km-1, 0.35 km-1, and 0.33 km-1, respectively. Sub-basin A is the most prone to floods due to its high stream and drainage density, whereas B and C are the least susceptible. This study used elevation, TWI, slope, precipitation, NDVI, distance from road, drainage density, distance from river, LU/LC, and soil type to create a flood vulnerability map incorporating GIS and AHP with pair-wise comparison matrix (PCM). The study's flood zoning shows that the northeastern part of this basin is more likely to flood than the southwestern part due to its elevation and high-order streams. Moderate River Flooding, the region's most hazardous flood class, covers 48.19% of the flooding area, including 1078.30 km2 of agricultural land, 94.86 km2 of bare soil, 486.39 km2 of settlements, 586.42 km2 of vegetation cover, and 39.34 km2 of water bodies. The developed hydro-morphometric model, the flood susceptibility map, and the analysis of this data may be utilized to offer long-term advance alarm insight into areas potentially to be invaded by a flood catastrophe, boosting hazard mitigation and planning.

GIS-based spatial analysis for lightning scenario in Bangladesh.

Bangladesh has witnessed alarmingly rising lightning frequency, particularly during pre-monsoon and monsoon seasons. This has resulted in significant annual death tolls from lightning strikes over the past decade. Recognizing this crisis, the country officially declared lightning casualties a natural disaster in 2016. This study delves deeper into the landscape of lightning fatalities and causalities in Bangladesh. Utilizing secondary data sources, this research introduces a unique approach by integrating Bangladesh Meteorological Department (BMD) data and NASA's Lightning Imaging Sensor (LIS) data from the International Space Station's (ISS) Near-real Time (NRT) mission. This combined dataset allows for a more comprehensive analysis. Furthermore, Geographic Information Systems (GIS) was employed to analyze spatial distributions and generate maps. The Inverse Distance Weighted (IDW) interpolation tool was used to create detailed spatial distribution maps of lightning fatalities, thunderstorm days (TSDs), and lightning flash frequency (LFF) across Bangladesh. The analysis revealed that farmers and fishermen were the most vulnerable populations, with the northeastern regions experiencing the highest impact. Sylhet division emerged as the area with the most fatalities, highlighting the northeastern zone's susceptibility. The study also identified monsoons as the period with the highest occurrences of lightning deaths and injuries. By combining innovative data integration and spatial analysis, this study offers valuable insights into the alarming trend of lightning fatalities in Bangladesh. These findings can inform targeted prevention strategies and interventions to safeguard vulnerable populations and communities.

Locustamigratoria (L.) (Orthoptera) in a warming world: unravelling the ecological consequences of climate change using GIS.

The migratory locust, Locustamigratoria (L.), a significant grasshopper species known for its ability to form large swarms and cause extensive damage to crops and vegetation, is subject to the influence of climate change. This research paper employs geographic information system (GIS) and MaxEnt ecological modelling techniques to assess the impact of climate change on the distribution patterns of L.migratoria. Occurrence data and environmental variables are collected and analysed to create predictive models for the current and future distribution of the species. The study highlights the crucial role of climate factors, particularly temperature and precipitation, in determining the locust's distribution. The MaxEnt models exhibit high-performance indicators, accurately predicting the potential habitat suitability of L.migratoria. Additionally, specific bioclimatic variables, such as mean temperature and annual precipitation, are identified as significant factors influencing the species' presence. The generated future maps indicate how this species will invade new regions especially in Europe. Such results predict the risk of this destructive species for many agriculture communities as a direct result of a warming world. The research provides valuable insights into the complex relationship between locust distribution and environmental factors, enabling the development of effective strategies for locust management and early warning systems to mitigate the impact on agriculture and ecosystems.

Healthcare equity in focus: bridging gaps through a spatial analysis of healthcare facilities in Irbid, Jordan.

In the Irbid Governorate, Jordan, equitable healthcare facility distribution is vital to ensuring healthcare accessibility and improving public health outcomes. This study investigated the spatial distribution, accessibility, and conformity of healthcare facilities to the Ministry of Health standards to identify areas requiring improvement. Using geographic information systems (GIS), three spatial analyses were conducted: nearest neighbor analysis, buffer analysis, and service area analysis. These analyses comprehensively assessed the healthcare landscape, revealing a random spatial distribution pattern of healthcare facilities; and indicating an absence of structured organization. The buffer analysis revealed concentrations in specific regions, while others were underserved. The Service Area Analysis revealed significant healthcare access challenges, especially in remote areas. The healthcare resource distribution of the Irbid governorate fell short of national and international standards, emphasizing the need for improvements. To address these disparities, policymakers and healthcare authorities should focus on equitably redistributing resources, tailoring allocation to local needs, improving remote area infrastructure, and refining government policies. Continuous monitoring and evaluation are imperative to ensure alignment with international standards and achieve healthcare equity. The insights from this case study provide valuable guidance for regions facing similar healthcare distribution challenges.

Resilient critical infrastructures: An innovative methodological perspective for critical infrastructure (CI) integrated assessment models by inducing digital technologies during multi-hazard incidents.

Over the last decade, the notion of community resilience, which encompasses planning for, opposing, absorbing, and quickly recovering from disruptive occurrences, has gained momentum across the world. Critical Infrastructures (CI) are seen as critical to attaining success in today's densely populated countries. Such infrastructures must be robust in the face of multi-hazard catastrophes by implementing appropriate disaster management and recovery plans. Given these facts, it is critical to establish a new methodological perspective with an integrated system for effective disaster management of CI, as well as an intelligent application that will aid in the construction of more resilient and sustainable cities and communities. This perspective proposes a holistic gaming scenario application for assessing the vulnerability and accessibility of critical infrastructures during multi-hazard events, with a primary focus on conducting an integrated assessment for critical infrastructures and their assets. Mainly, the perspective includes a holistic gaming scenario application that will aid in accurately quantifying geographical spatial information and integrating big data into predictive and prescriptive management tools using virtual reality.•Conducting Integrated Assessment Models for evaluating vulnerability of Critical Infrastructures.•Inducing Digital Technologies during Multi-Hazard Incidents for improving Natural hazard assessment models.•Developing an open-world gaming scenario that is considered with high visual motion pictures and scenes.

Measuring the 3-30-300 rule to help cities meet nature access thresholds.

The 3-30-300 rule offers benchmarks for cities to promote equitable nature access. It dictates that individuals should see three trees from their dwelling, have 30 % tree canopy in their neighborhood, and live within 300 m of a high-quality green space. Implementing this demands thorough measurement, monitoring, and evaluation methods, yet little guidance is currently available to pursue these actions. To overcome this gap, we employed an expert-based consensus approach to review the available ways to measure 3-30-300 as well as each measure's strengths and weaknesses. We described seven relevant data and processes: vegetation indices, street level analyses, tree inventories, questionnaires, window view analyses, land cover maps, and green space maps. Based on the reviewed strengths and weaknesses of each measure, we presented a suitability matrix to link recommended measures with each component of the rule. These recommendations included surveys and window-view analyses for the '3 component', high-resolution land cover maps for the '30 component', and green space maps with network analyses for the '300 component'. These methods, responsive to local situations and resources, not only implement the 3-30-300 rule but foster broader dialogue on local desires and requirements. Consequently, these techniques can guide strategic investments in urban greening for health, equity, biodiversity, and climate adaptation.

Technology-enhanced community forest management in tropical regions: A state of the art.

Tropical forests provide ecosystem services to around 2.7 billion people. Yet they are reaching tipping points due to social, economic, and environmental pressures. Technology is increasingly being leveraged to expand Community Forest Management (CFM) monitoring capabilities and to potentially increase its effectiveness, but a systematic accounting of this is lacking in the scientific literature. This study employed a mixed-methods approach combining a systematic literature review (SLR) with semi-structured interviews of technology-enhanced CFM (tech-CFM) case studies in tropical forests. From the SLR, evaluation criteria were identified and applied to 23 case studies that employed one or more novel technologies, 8 on the African continent, 9 in the Asia Pacific region, 5 in Latin America, and 1 in multiple regions. The results include classifying 22 monitoring technologies, with satellite remote sensing technology being the most common (17 case studies), followed by mobile devices (10 case studies), which are often integrated with geographic information system (8 case studies) analysis and data platforms. These technologies tend to be deployed in packages that augment each technology's capabilities, beyond their individual uses. Nonetheless, they are limited by poor internet coverage in remote regions, impeding the ability to develop real-time integrated monitoring systems. Tech-CFM shows potential for complementing and integrating with national monitoring system when adequate data collection protocols are in place. Practical social-cultural, technical, and project design recommendations are made for the integration of technology into CFM. Finally, a multi-criteria decision-making framework is developed from the literature-based evaluation criteria to assist practitioners in selecting appropriate technology suites.

The correlation between small papillary thyroid cancers and gamma radionuclides Cs-137, Th-232, U-238 and K-40 using spatially-explicit, register-based methods.

A steep increase of small papillary thyroid cancers (sPTCs) has been observed globally. A major risk factor for developing PTC is ionizing radiation. The aim of this study is to investigate the spatial distribution of sPTC in Sweden and the extent to which prevalence is correlated to gamma radiation levels (Caesium-137 (Cs-137), Thorium-232 (Th-232), Uranium-238 (U-238) and Potassium-40 (K-40)) using multiple geospatial and geostatistical methods. The prevalence of metastatic sPTC was associated with significantly higher levels of Gamma radiation from Th-232, U-238 and K-40. The association is, however, inconsistent and the prevalence is higher in densely populated areas. The results clearly indicate that sPTC has causative factors that are neither evenly distributed among the population, nor geographically, calling for further studies with bigger cohorts. Environmental factors are believed to play a major role in the pathogenesis of the disease.

Analysis of land use and land cover change using machine learning algorithm in Yola North Local Government Area of Adamawa State, Nigeria.

The dynamic use of land that results from urbanization has an impact on the urban ecosystem. Yola North Local Government Area (Yola North LGA) of Adamawa state, Nigeria, has experienced tremendous changes in its land use and land cover (LULC) over the past two decades due to the influx of people from rural areas seeking for the benefits of its economic activities. The goal of this research is to develop an efficient and accurate framework for continuous monitoring of land use and land cover (LULC) change and quantify the transformation in land use and land cover pattern over a specific period (between 2002 and 2022). Land sat images of 2002, 2012, and 2022 were obtained, and the Support Vector Machine classification method was utilized to stratify the images. Land Change Modeler (LCM) tool in Idrissi Selva software was then used to analyze the LULC change. SVM produced a good classification result for all three years, with 2022 having the highest overall accuracy of 95.5%, followed by 2002 with 90% and 2012 with 87.7% which indicates the validity of the algorithm for future predictions. The results showed that severe land changes have occurred over the course of two decades in built-up (37.32%), vegetation (forest, scrubland, and grassland) (-3.27%), bare surface (-33.47%), and water bodies (-0.59%). Such changes in LULC could lead to agricultural land lost and reduced food supply. This research develops a robust framework for continuous land use monitoring, utilizing machine learning and geo-spatial data for urban planning, natural resource management, and environmental conservation. In conclusion, this study underscores the efficacy of support vector machine algorithm in analyzing complex land use and land cover changes.

Long-term bathymetric changes in the submerged delta of the Turia river since the nineteenth century (Western Mediterranean) and their drivers.

Deltas and estuaries are formed through periods of marine transgression and regression, which are the continuity of a river and provide key information about its evolution. However, many of the world's deltas are increasingly exposed to the impacts of human activities. While changes affecting the subaerial parts of deltas have been intensively studied, much less is known of their subaqueous parts, the understanding of which is important in gauging overall potential delta vulnerability. This research evaluates the bathymetric changes in the submerged delta of the Turia river (Western Mediterranean, Spain) before and after the extreme flood event of 1957, after which the riverbed was diverted. Three nautical charts were processed (1878, 1988 and 2022), including georeferencing and Digital Elevation Model (DEM) generation. In order to evaluate changes before and after the event, models for 1878-1988 and 1988-2022 were compared and differences were quantified in order to assess erosion and aggradation trends. The results indicate a more aggradated submerged delta in the surroundings of the old river mouth, favored by the high sediment availability since the end of the Little Ice Age (LIA), and the presence of a smaller delta next to its current river mouth to the south of the harbor of Valencia. Bathymetric reconstructions also made it possible to map some incisions in the inner continental shelf as river channels that migrated eastwards when the sea level was lower during MIS 2. Finally, the comparison of bathymetric models also revealed the scarcity of sediments on the current shelf since 1988, which is attributed to anthropogenic action. The successive extensions of the harbor are increasingly distorting the distribution of sediments along the coast and are thus remodeling seabed sediment distribution. Knowing the sedimentation in deltaic systems means better predicting future alterations due to increased anthropization and of the climate change.

Soil erosion estimation by RUSLE model using GIS and remote sensing techniques: A case study of the tertiary hilly regions in Bangladesh from 2017 to 2021.

Soil erosion is one of the major environmental threats in Bangladesh, especially in the tertiary hilly regions located in the northeastern and southeastern parts of the country. The revised universal soil loss equation (RUSLE), combined with Geographic Information System, is a reliable methodology to estimate the potential soil loss in an area. This research aimed to use the RUSLE model to estimate the soil erosion in the tertiary hill tracts of Bangladesh from 2017 to 2021. The erosivity factor was determined from the annual average precipitation, and erodibility factor was estimated from FAO soil database. The elevation model was used to analyze slope length steepness factors, while land use land cover was used to compute cover management factor. Lastly, land use and elevation were integrated to estimate the support practice factor. Results revealed that the potential mean annual soil loss in 2017, 2019, and 2021 was 68.77, 69.84, and 83.7 ton ha-1 year-1 from northeastern and 101.72, 107.83, and 114.04 ton ha-1 year-1 from southeastern region, respectively. Although total annual rainfall was high in 2017, soil loss was found higher in 2021 which indicates the impact of land use change on erosion. This investigation will help the policymakers to identify the erosion-vulnerable areas in the hill tracts that require immediate soil conservation practices. Additionally, there is no latest field-based data available for the country for the validation, and hence, it is recommended to conduct field-based studies for validating the model-derived results and creating a reliable soil erosion database for the country.

Site suitability analysis for locating construction and demolition waste transfer station: an Indian case study.

Construction and Demolition Waste Management (CDWM) includes collecting, transporting, processing, and disposing construction and demolition (C&D) waste, where collection and transportation of bulky and voluminous C&D waste contribute significantly to economic and environmental impacts. Transfer station (TS) being a link between various waste management (WM) facilities plays a significant role in collection and transportation of waste. Thus, locating TS at suitable site can help in reducing the overall impacts. Employment of Geographic Information System (GIS) analysis tools in CDWM is a powerful strategy for site suitability study. A case study in Coimbatore, India, is presented in this study using GIS-based multi-criteria analysis for locating C&D waste TS. The criteria for site suitability analysis are chosen based on literature review, regulations, and experts' opinions. Weights of the chosen criteria are estimated using analytic hierarchy process (AHP), and the final suitability map is created by weighted overlay analysis (WOA) in GIS environment. Results provide first-hand information for local decision makers to locate C&D waste transfer station in the chosen study region and report that 12% of the entire area is "highly suitable" for transfer station location.

The Effects of Climate Change on the Nesting Phenology of Three Shorebird Species in the United States.

Previous research suggests that a frequent response of organisms to the ongoing climate crisis is the adjustment of their reproductive timing or breeding phenology. Shorebirds may be especially vulnerable to increasing temperatures and precipitation, as many are migratory and depend on coastal habitats for wintering and breeding. These particular habitats could be at risk due to changes in climate, and nesting times often depend on food availability, which is often directly influenced by temperature. We investigated if clutch initiation dates (CID) for three shorebird species in the United States have become earlier over time with increasing temperatures and precipitation. We used nest records from Cornell's NestWatch program and various museum databases and weather station data from the National Oceanic and Atmospheric Administration. We found evidence that CIDs have become earlier over time, though this was only a significant factor for one species. While temperature in our study areas has increased significantly over time, precipitation changes were more variable and not always significantly predicted by time. We found evidence that one species may be responding to increasing temperatures by nesting earlier, but there was no support for our hypothesis that CID has changed due to changes in precipitation for any species. Results varied for each species, indicating the importance of further studies on shorebirds as the effects of climate change on their nesting phenology may not be fully realized and will likely depend on the species' biology and distribution.

Mapping and identification of potential groundwater development zones of an alluvial aquifer in parts of Ghaggar and Upper Yamuna basins in India.

Using an integrated analytical hierarchy process, remote sensing and geographic information system techniques, the current study aims to map and identify the potential groundwater zones of Kurukshetra District of Haryana, which is located in the Ghaggar and Upper Yamuna Basins in India. This is done in the context of a significant change in the use of groundwater pattern, with respect to its continuously increasing demand due to the growing population, expansion of area under irrigation and related economic factors. The amount and quality of groundwater are anticipated to be impacted by anthropogenic activities as well as natural factors such as geomorphology, soil type, lithology and rainfall variance owing to a changing climatic scenario. The potential index of groundwater for this study was calculated by using nine important factors, including geomorphology, rainfall, soil type, depth to groundwater level, lithology, land use land cover, normalized difference vegetation index, cumulative sand thickness and elevation. The integration of multiple thematic layers was accomplished using the overlay weighted method to generate a potential groundwater zonation map and the accuracy of the resulting map was validated against a groundwater resource potential map. Statistical measures demonstrate an 82% agreement between the two maps, indicating a high level of concurrence. Accordingly, three groundwater zones of good, average and bad potential have been identified in the study area. In the current study, a process that combines weighted ranking with spatial data transformation and harmonization has been developed to obtain information for accurate decision-making. The results accruing from this research have significant ramifications for creating regional sustainable groundwater management plans.

A dataset on the geographical distribution, bounds, and reed cover of Hungarian fishponds.

This paper presents geospatial datasets, figures, and tables illustrating i) the location and total area of fish farms under cultivation; and ii) the spatiotemporal dynamics of reed cover in Hungarian fishponds generated from the published study of Sharma et al., [1]. Preliminary data for fish farm locations were obtained from the Institute of Agricultural Economics (AKI), followed by significant refinement based on high-resolution Google Earth Pro-imagery. The fishpond area dataset was validated against the values reported in annual statistical reports on aquaculture. In order to map reed vegetation freely available Sentinel-2 imagery (between 2017 and 2021) was accessed from the Copernicus Open Access Hub [2] and emergent macrophyte cover was classified using the NDVI-based threshold values [1]. Scientists, policymakers, and fish farmers can all benefit from such geospatial datasets. It could be used to monitor the extent of fishponds in Hungary and to design farm-level reed management plans to optimize the provision of ecological and production services.

Temporal modelling of Lymnaea natalensis (Krauss, 1848) in tropical aquatic habitats.

Lymnaea natalensis is the only snail intermediate host of Fasciola gigantica, the causative agent of fascioliasis, in Nigeria. The species also serves as intermediate host for many other African trematode species of medical and veterinary importance, and it is found throughout the country. However, there is no detailed information on the factors that influence its distribution and seasonal abundance in the tropical aquatic habitats in Nigeria. This study used the geographic information system and remotely sensed data to develop models for predicting the distribution of L. natalensis in South-Western Nigeria. Both land surface temperature (LST) and normalised difference vegetation index (NDVI) were extracted from Landsat satellite imagery; other variables (slope and elevation) were extracted from a digital elevation model (DEM) while rainfall data were retrieved from the European Meteorology Research Programme (EMRP). These environmental variables were integrated into a geographic information system (GIS) to predict suitable habitats of L. natalensis using exploratory regression. A total of 1410 L. natalensis snails were collected vis-à-vis 22 sampling sites. Built-up areas recorded more L. natalensis compared with farmlands. There was no significant difference in the abundance of snails with season (p  0.05). The regression models showed that rainfall, NDVI, and slope were predictors of L. natalensis distribution. The habitats suitable for L. natalensis were central areas, while areas to the north and south were not suitable for L. natalensis.Contribution: The predictive risk models of L. natalensis in the study will be useful in mapping other areas where the snail sampling could not be conducted.

Estimation of the general population and children under five years of age in France exposed to magnetic field from high or very high voltage power line using geographic information system and extrapolated field data.

BACKGROUND: The effects of extremely low-frequency magnetic fields, especially their long-term health effects, including childhood leukaemia, remain elusive. The International Agency for Research on Cancer has classified the exposure to magnetic fields >0.4 µT as 'possibly carcinogenic to humans (group 2 B)' for childhood leukaemia. However, the number of exposed individuals, particularly children, remains poorly documented in international literature. The objective of this study was to estimate the number of individuals living near a high or very high voltage line in France (≥63 kV), among the general population and children under the age of five years. METHODS: The estimate considered different exposure scenarios depending on the line voltage and the distance of the housing from it, and whether the line is overhead or underground. The exposure scenarios were obtained using a multilevel linear model created from a measurement database published by "Réseau de transport d'électricité", the operator of the French electricity transmission network. RESULTS: Between 0.11% (n = 67,893) and 1.01% (n = 647,569) of the French population and between 0.10% (n = 4712) and 1.03% (n = 46,950) of children under five years of age were estimated to be living in an area potentially exposed to a magnetic field, depending on the exposure scenario (>0.4 µT and >0.1 µT, respectively). CONCLUSIONS: By making it possible to estimate the total number of residents, schools, and health institutions near high-voltage power lines, the proposed methodology can help identify potential co-exposures near high-voltage power lines, which are regularly cited as a possible explanation for contradictory results from epidemiological studies.

Estimate of population density and diagnosis of main factors of spatial heterogeneity in the metropolitan scale, western China.

We estimated the population density and quantified its characteristics using remote sensing, census data, and Geographic Information System (GIS). The interactive influence of these factors on population density was quantified based on geographic detectors to identify the differentiation mechanisms in the Chengdu metropolitan area of China. We identified the key factors that contribute to population density growth. The models used to simulate population density had the highest R2 values (>0.899). Population density tended to increase with time, with a multicentre spatial agglomeration pattern; the centre of gravity of the spatial distribution tended to move from the southeast to the northwest. Industry proportions, Normalised Difference Vegetation Index (NDVI), land use, distance to urban centers or construction land, and GDP per capita can satisfactorily explain population density changes. The combined impact of these elements on population density variation exhibited mutual and non-linear strengthening, with the mutual effect of the two elements intensifying the impact of each individual element. Our study identified the key driving forces that contribute to the differentiation of population density, which can provide valuable support for the development of effective regional and targeted population planning guidelines.