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High-speed railways are critical infrastructure in many countries, but their construction generates substantial spoil, particularly in mountainous regions dominated by tunnels and slopes, necessitating the establishment and monitoring of spoil disposal areas. Inadequate monitoring of spoil disposal areas can lead to significant environmental issues, including soil erosion and geological hazards such as landslides and debris flows, while also hindering the recycling and reuse of construction spoil, thereby impeding the achievement of circular economy and sustainable development goals for high-speed railways. Although the potential of geographic information systems, remote sensing, and global positioning systems in waste monitoring is increasingly recognized, there remains a critical research gap in their application to spoil disposal areas monitoring within high-speed railway projects. This study proposes an innovative framework integrating geographic information systems, remote sensing, and global positioning systems for monitoring spoil disposal areas during high-speed railway construction across three key scenarios: identification of disturbance boundaries (scenario 1), extraction of soil and water conservation measures (scenario 2), and estimation of spoil volume changes (scenario 3). In scenario 1, disturbance boundaries were identified using Gaofen-1 satellite data through processes such as imagery fusion, unsupervised classification, and spatial analysis. In scenario 2, unmanned aerial vehicle data were employed to extract soil and water conservation measures via visual interpretation and overlay analysis. In scenario 3, Sentinel-1 data were used to analyze elevation changes through the differential interferometric synthetic aperture radar method, followed by the estimation of spoil volume changes. The effectiveness of this integrated framework was validated through a case study. The results demonstrate that the framework can accurately delineate disturbance boundaries, efficiently extract soil and water conservation measures, and estimate dynamic changes in spoil volume with an acceptable error margin (15.5%). These findings highlight the framework's capability to enhance monitoring accuracy and efficiency. By integrating multi-source data, this framework provides robust support for sustainable resource management, reduces the environmental impact, and advances circular economy practices. This study contributes to the efficient utilization of construction spoil and the sustainable development of high-speed railway projects. [ABSTRACT FROM AUTHOR]

Land use refers to how the surface of a designated area is utilized, encompassing both developed and vacant lands. The intricate interplay between population growth and the availability of natural resources gives rise to diverse challenges that are exhibited in the recharge of groundwater. This research was conducted in the Koraiyar Watershed, Coimbatore District, Tamil Nadu, India. The change in the land use land cover (LULC) was detected by digitizing the linear imaging self scanning sensor (LISS) III and LISS IV satellite images for the years 2000 and 2011 using the maximum likelihood classification algorithm. LANDSAT-8 OLI data were processed for the year 2020. The study area has subsequently undergone an assessment of groundwater levels to determine the impact between groundwater and land use land cover. The result of the study indicates a continuous decrease in the wet crop area, which is the effect of the gradual depletion of groundwater resources. Along with this, the narrowing of the water bodies and increased areas of dry crops, industrial areas, and quarries are also responsible for lowering groundwater table depletion and contamination. Settlements increased from 39 to 49 km2 from 2000 to 2011 and then gradually increased by 50 km2 in 2020 to meet the demand of the population. From 2000 to 2019, the groundwater level has been declining with respect to the LULC pattern. The increase in concrete platforms in the study can affect the infiltration. Thus, the study can aid in developing water management strategies and decisions for governmental and non-governmental policymakers. [ABSTRACT FROM AUTHOR]

The integration of Building Information Modelling (BIM) and Geographic Information Systems (GIS) is becoming increasingly important in the construction industry and urban planning, helping to create smart cities and digital twins. The research, which involved expert interviews, and an online survey conducted among specialists from partner countries, shows that there is a significant skills gap, as many professionals lack expertise in combining BIM and GIS. It also indicates that while the integration of these technologies is advancing, it is still in the early stages. To tackle this issue, the BIRGIT project—featuring partners from Sweden, Italy, Spain, Belgium, and Croatia—aims to develop new educational programs that meet market demands. Ongoing initiatives like BIRGIT are vital for bridging the skills gap and ensuring that professionals are well-equipped to effectively implement these technologies, ultimately improving project management and decision-making in the industry. [ABSTRACT FROM AUTHOR]

The article aimed to identify areas vulnerable to soil loss and erosion in the Serra Dourada State Park and its two-kilometer surroundings, with the additional aim of verifying whether the delimitation of the Conservation Unit had a preservation effect. The years 1985, 2003 and 2022 were adopted as a time frame, therefore considering a time frame before and after the 2003 park creation decree. Three soil loss diagnoses were generated from the application of the Universal Loss Equation of Soil (USLE). It was observed that the main changes in soil loss classes were due to the conversion of areas with natural cover and appropriation for agricultural activities. Through visual inspection of satellite images, 294 erosion points were identified, of which 121 are within the park and 26 are critical erosions. The results show that the creation of the Conservation Unit was not enough to guarantee environmental preservation, however, it reduced the advancement of degradation over space. [ABSTRACT FROM AUTHOR]

Energy stands as an indispensable global need, yet the finite nature of rapidly depleting fossil fuel reserves poses environmental challenges. To address this, numerous nations are shifting toward sustainable energy sources to foster environmental well-being while satisfying their energy requirements. Among these alternatives, wind energy emerges as a particularly efficient option. This study focuses on identifying optimal sites for installing wind power plants in Adana province through the integration of the analytic hierarchy process (AHP) and geographic information system (GIS). Within this framework, a comprehensive set of 15 criteria was delineated along with their respective sub-criteria. It is thought that the criteria used in the study will be useful not only for Adana province but also for other provinces that are suitable for wind turbine placement and planned to be built. The outcome of this investigation delineates potential areas in Adana province deemed suitable for establishing wind power plants. As a result, analysis reveals that 9.94% of the study area is conducive to establishing a wind farm (WF), while 51.66% is deemed moderately suitable. Notably, the districts of Feke, Tufanbeyli, Saimbeyli, and Pozantı emerge as particularly well-suited for this purpose. This study aims to contribute to the existing literature by identifying precise and suitable areas by combining GIS and AHP in the WF site selection process. In the study, a new research perspective is presented by taking into account the uncertainty in the site selection process and the concept of sustainability in three different dimensions: technical, economic, and environmental, thus aiming to guide decision-makers for future WF projects. In addition, it is thought that the methods and criteria used in the study will also be guiding for future WF site selection studies. [ABSTRACT FROM AUTHOR]

Sustainable transportation is vital to climate justice and social equity. Despite the efforts to achieve sustainability, there is still a lack of adequate measurement that integrates land use and transportation systems, which can be barriers to planning implementation. With methodological improvements in fuzzy theory application, this study develops an integrated index to measure the sustainability of multimodal accessibility. We do so by defining a fuzziness degree based on the different trip purposes and modes of transportation with a case study in Isfahan, Iran. Sustainable accessibility indicators were developed for walking, biking, and public transportation to represent the performance of each transportation system, considering the integration with land-use patterns. We analyze transportation modes and the accessibility to five main urban activities, including employment opportunities, education, healthcare, shopping, and recreation services, based on the travel distances, followed by a statistical integration method with Principal Components Analysis (PCA) for each travel mode. The outcome provides insights for urban planners and transportation planners to effectively evaluate the degree of integration between transportation and land-use systems and contribute to enhancing sustainable accessibility. [ABSTRACT FROM AUTHOR]

Air pollution, particularly fine particulate matter (PM), poses significant health risks to vulnerable populations such as children, older adults, and individuals with chronic illnesses. Understanding the spatial distribution of these populations and their access to air quality information is crucial for effective interventions. In urban areas like Gangseo District, the distribution of essential facilities and accessibility varies greatly. While studies have highlighted the health impacts of PM, research on optimizing air quality monitoring for at-risk groups remains limited. This study aims to identify optimal locations for air quality monitoring by analyzing the spatial distribution of vulnerable populations and facility accessibility. Using Geographic Information Systems (GIS) and isochrone maps, we identified areas with high concentrations of vulnerable groups and poor access to healthcare facilities. Our findings revealed significant disparities in access to air quality information, with some high-risk areas underserved by current monitoring systems. This study integrated demographic data and spatial analysis to propose strategic monitoring placements. The methodology can be applied to other urban settings and offers a framework for improving air quality management. This study underscores the importance of targeted air quality monitoring to protect vulnerable populations and suggests practical steps for policymakers to enhance public health. [ABSTRACT FROM AUTHOR]

Siting an offshore wind project is considered a complex planning problem with multiple interrelated objectives and constraints. Hence, compactness and contiguity are indispensable properties in spatial modeling for Renewable Energy Sources (RES) planning processes. The proposed methodology demonstrates the development of a raster-based spatial optimization model for future Offshore Wind Farm (OWF) multi-objective site-prospecting in terms of the simulated Annual Energy Production (AEP), Wind Power Variability (WPV) and the Depth Profile (DP) towards an integer mathematical programming approach. Geographic Information Systems (GIS), statistical modeling, and spatial optimization techniques are fused as a unified framework that allows exploring rigorously and systematically multiple alternatives for OWF planning. The stochastic generation scheme uses a Generalized Hurst-Kolmogorov (GHK) process embedded in a Symmetric-Moving-Average (SMA) model, which is used for the simulation of a wind process, as extracted from the UERRA (MESCAN-SURFEX) reanalysis data. The generated AEP and WPV, along with the bathymetry raster surfaces, are then transferred into the multi-objective spatial optimization algorithm via the Gurobi optimizer. Using a weighted spatial optimization approach, considering and guaranteeing compactness and continuity of the optimal solutions, the final optimal areas (clusters) are extracted for the North and Central Aegean Sea. The optimal OWF clusters, show increased AEP and minimum WPV, particularly across offshore areas from the North-East Aegean (around Lemnos Island) to the Central Aegean Sea (Cyclades Islands). All areas have a Hurst parameter in the range of 0.55–0.63, indicating greater long-term positive autocorrelation in specific areas of the North Aegean Sea. [ABSTRACT FROM AUTHOR]

This study conducted a project to assess carbon sequestration in the forest area of Chiang Mai Rajabhat University, Mae Rim Campus, covering a total area of approximately 5,600 rai, with about 75% consisting of dry dipterocarp forest. The Sentinel-2 satellite data from 2019 to 2023 were used to analyze and classify forest density using the Normalized Difference Vegetation Index (NDVI). It was classified into four NDVI levels: highest, high, moderate, and low. Then, eight sample plots were distributed across all density levels to collect field data on tree species, number of trees, height, and diameter. The biomass and carbon sequestration in the sample plots showed a strong correlation with vegetation density, with the highest average correlation in February, particularly on February 13, 2023, showing the highest correlation coefficient of 0.817. This relationship is described by the equation y=78.601x‒25.726, indicating that this model is effective for estimating carbon sequestration. The analysis revealed that the area with the highest NDVI level of dry dipterocarp forest had the highest above-ground carbon sequestration rate of 16.25 tons per rai, whereas the forest with the lowest NDVI level had an above-ground carbon sequestration rate of 0.21 tons per rai. In total, the above-ground carbon sequestration for the trees amounted to 50,907.35 tons. This preliminary assessment serves as a promising foundation for future efforts the conservation and restoration of the university’s forest area, contributing to sustainable strategies for mitigating global warming. [ABSTRACT FROM AUTHOR]

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Abstract Site selection for agricultural products is critical for agricultural planning, productivity, and farmers. Site selection is also critical for agricultural sustainability, as it helps ensure the efficient use of natural resources and avoids environmental degradation. This research proposes an evaluation model for walnut cultivation in the Savur (Mardin, Turkey) district in the Southeastern Anatolia region. Based on literature data, expert opinion, and local walnut producers’ perspectives, a spatial decision support system was developed using Geographic Information System (GIS)-based Multi-Criteria Decision Analysis (MCDA). This model provides farmers and stakeholders with a comprehensive evaluation of the different walnut-producing sites in the Savur district. It allows them to make informed decisions about the most suitable areas for walnut production. The land suitability assessment for walnut orchards included four primary evaluation aspects, ten main criteria, and forty-seven sub-criteria. Evaluation criteria were weighted using the Analytic Hierarchy Process (AHP). A land suitability map was created using the weighted overlay method in overlap with the evaluation criteria. The results showed 442.56 ha of very highly suitable land, 9789.43 ha of highly suitable land, and 1140.76 ha of moderately suitable land in the Savur district. In order to determine the accuracy of the study model, ROC (Receiver Operating Characteristic) analysis was conducted. As a result of the validation analysis, the constructed model had an Area Under the Curve (AUC) of 0.911, which indicates an excellent fit between the model and reality; therefore, the model is acceptable. This study demonstrates that integrating AHP and GIS can aid agricultural land use planning and decision-making.

Abstract Sustainable transportation is vital to climate justice and social equity. Despite the efforts to achieve sustainability, there is still a lack of adequate measurement that integrates land use and transportation systems, which can be barriers to planning implementation. With methodological improvements in fuzzy theory application, this study develops an integrated index to measure the sustainability of multimodal accessibility. We do so by defining a fuzziness degree based on the different trip purposes and modes of transportation with a case study in Isfahan, Iran. Sustainable accessibility indicators were developed for walking, biking, and public transportation to represent the performance of each transportation system, considering the integration with land-use patterns. We analyze transportation modes and the accessibility to five main urban activities, including employment opportunities, education, healthcare, shopping, and recreation services, based on the travel distances, followed by a statistical integration method with Principal Components Analysis (PCA) for each travel mode. The outcome provides insights for urban planners and transportation planners to effectively evaluate the degree of integration between transportation and land-use systems and contribute to enhancing sustainable accessibility.

Pesticide exposure poses significant environmental and human health concerns, particularly given its extensive use in agricultural activities. The assessment of pesticide risks is a multifaceted and resource-intensive process, often requiring time-consuming toxicity studies. In response to this challenge, advanced computational models, remote sensing, and GIS (geographic information systems) have emerged as efficient and precise tools for evaluating pesticide exposure risks. This comprehensive review aims to provide an in-depth examination of the latest research methodologies for assessing the risks associated with pesticide exposure and their practical applications. These methodologies encompass the assessment of pesticide exposure in air, soil, and water, offering a comprehensive understanding of potential environmental pathways. The paper also delves into the effective utilization of these tools for pesticide risk assessment and examines the potential implications of their findings. The approaches outlined in this review hold promise for a thorough and insightful assessment of pesticide risks and are positioned to equip researchers and policymakers with valuable knowledge to mitigate the impacts of pesticide exposure on human health and the environment. [ABSTRACT FROM AUTHOR]

The numerical weather prediction model HARMONIE-AROME and a multiple linear regression model (referred to in this article as the TURCLIM model after the local climate observation network) were used to model surface air temperature for 25–31 July 2018 in the City of Turku, Finland, to study their performance in urban areas and surrounding rural areas. The 0200 LT (local standard time) temperatures modeled by the HARMONIE-AROME and TURCLIM models were compared to each other and against the observed temperatures to find the model best suited for modeling the urban heat island effect and other spatial temperature variabilities during heatwaves. Observed temperatures were collected from 74 sites, representing both rural and urban environments. Both models were able to reproduce the spatial nighttime temperature variation. However, HARMONIE-AROME modeled temperatures were systematically warmer than the observed temperatures in stable conditions. Spatial differences between the models were mostly related to the physiographic characteristics: for the urban areas, HARMONIE-AROME modeled on average 1.4°C higher temperatures than the TURCLIM model, while for other land-cover types, the average difference was 0.51°C at maximum. The TURCLIM model performed well when the explanatory variables were able to incorporate enough information on the surrounding physiography. Respectively, systematic cold or warm bias occurred in the areas in which the thermophysically relevant physiography was lacking or was only partly captured by the model. Significance Statement: As more and more people are living in an urban environment, the demand for accurate urban climate modeling is growing. This study aims to understand the differences between the numerical weather prediction and multiple linear regression modeling and their limitations in modeling surface air temperature in subkilometer scale. The case study shows that models are capable of predicting the spatial variation of 0200 LT nighttime temperature during a heatwave in a high-latitude coastal city. Both models are therefore valuable assets for city planners who need accurate information about the impacts of the physiography on the urban climate. The results indicate that to improve the performance of the models, more accurate physiographic description and higher spatial resolution of the models are needed. [ABSTRACT FROM AUTHOR]

This paper describes the integration of geographic information system (GIS) technology in a high school environmental science classroom, specifically examining the impact of GIS technology on student engagement, critical thinking, and interdisciplinary learning for Education for Sustainability (EfS). This environmental science classroom utilized ArcGIS Online and StoryMaps to facilitate hands-on learning, enabling students to explore spatial relationships, analyze data, and communicate insights to foster engagement and critical thinking. This GIS technology promotes interdisciplinary learning by connecting essential environmental issues with location and other social factors. Students deepen their understanding of human-environment interactions by interpreting spatial data, empowering them to propose sustainable solutions to environmental challenges. Using ArcGIS Online and StoryMaps, students engaged in guided lessons, explored sustainability topics, and created their own StoryMaps based on the United Nations' (UN) Sustainable Development Goals (SDGs). The findings highlight positive engagement, critical thinking, and interdisciplinary learning impacts. Students demonstrated increased interest, understanding of complex issues, and developed analytical skills through spatial data analysis. The approach encouraged interdisciplinary thinking and expanded awareness of ongoing environmental challenges, supporting EfS goals. [ABSTRACT FROM AUTHOR]

Soil erosion forms a serious ecological challenge to future production and sustainability, with implications of the environmental crisis and the food supply. It is one of the more critical geomorphological problems that threaten environmental sustainability and cause soil degradation along the Syrian coast. The object of this research is to evaluate the rate of probable annual water-induced soil erosion in the AL-Sanaoubar River Basin. By Integrating geographic information system (GIS), and remote sensing techniques with the Universal Soil Loss Equation (RUSLE). The results showed that the soil loss values in the AL-Sanaoubar River Basin ranged between 0–138.15 tons/ha/year, an average of 52.4 tons/ha/year. The total soil loss reached 12,606.11 tons/year for the entire studied area. The classification of possible spatial risk included five grades: very low (91.91%), low (7.62%), medium (0.35%), high (0.10%), and very high (0.01%). It has been noticed high soil erosion values in few portions of the study area, indicating that they had reached a risk stage; these sites were concentrated in the center and east of the studied basin, especially in the plateau and mountainous parts, Slope was a major factor that caused a direct impact on soil erosion. Soil erosion gradually increased with increasing slope in all types of land uses. To test the effectiveness of some soil conservation practices against water erosion, an experiment was conducted at the Mazar Qatrya site in Lattakia governorate during two consecutive seasons. Contour cultivation of the pea crop was applied at different distances, the agricultural method showed a high efficiency in reducing water erosion. Land use managers and Planners can benefit from this research to make suitable provisions and activities to conserve the soil. [ABSTRACT FROM AUTHOR]

The application of computational techniques to the study of zoology has greatly improved our knowledge of animal behaviour and species conservation. This study offers a thorough analysis of the ways that computational methods, such as data mining, simulation modelling, machine learning and geographic information systems (GIS) are revolutionising research in both applied and pure zoology. This review illustrates how these techniques can improve species distribution modelling, habitat suitability analysis, wildlife monitoring and behavioural investigations by looking at current case studies. More accurate forecasts have been produced by machine learning approaches, especially in the areas of species distribution and habitat suitability, which has improved the knowledge base for conservation initiatives. Through the extraction of significant patterns from large, complicated datasets, data mining has made it possible to get new insights into the vocalisations, social relationships and movement of animals. Because GIS provides precise habitat maps, landscape connectivity assessments, and spatial models of species distributions, it has proven to be an invaluable tool in ecological research. Important projections regarding the long-term effects of conservation initiatives have been made possible via simulation modelling, including individualbased models and population viability studies. The study does, however, also address the drawbacks of these computational methods, including data quality, model complexity, resource requirements and integrating disparate data sources. Notwithstanding these obstacles, zoological research seems to have a bright future because computational techniques provide effective instruments for addressing urgent ecological problems. In order to fully realise the potential of computational approaches in promoting species conservation and behavioural studies, this review emphasises the significance of ongoing innovation and multidisciplinary collaboration. [ABSTRACT FROM AUTHOR]

The study area occupies part of the southwestern part of the sedimentary plain from the city of Al-Kifl and Najaf to the city of Al-Mashkhab, which is predominantly characterized by extraversion, which made the study area a low topographical variation. However, this variation contributed significantly to determining the geomorphological forms of the Euphrates River course as well as the direction of the course, which was consistent with the direction of the surface slope from the northwest towards the southeast. Thus, the natural characteristics of the study area vary from geological structure, surface, climate, soil and natural vegetation. When taking into account the true longitudinal extension of the Euphrates River course of (59) km, we find that these characteristics have a prominent impact in causing geomorphological changes in the study area. [ABSTRACT FROM AUTHOR]

Relief, climate and soils in the lands of the villages of Edrevo and Panicherevo, Stara Zagora region, Bulgaria were examined. A geographic information system (GIS), including the collected information, and spatial visualization of soil data and main soil characteristics was carried out in the form of a soil map and thematic maps of soil texture (physical clay content in %), humus content (%) and soil acidity (pH). A greater part of the agricultural land is occupied by Cinnamon forest soils, a total of 1720.56 ha, of which 1012.43 ha are strongly leached to slightly podzolized with varying degrees of erosion and 708.12 ha are undeveloped and shallow soils. The rest of the agricultural lands are from Alluvial (Deluvial) - meadow soils - 930.29 ha. The detailed description of the soil-climatic conditions was applied in determining the credit rating and category of the lands in the investigated lands (part II). [ABSTRACT FROM AUTHOR]

The Common Agricultural Policy (CAP) is a European Union (EU) policy aimed at supporting farmers, guaranteeing food security, and promoting sustainable rural development, which has undergone numerous reforms to adapt to the needs of the agricultural sector and society in general. The main objective of this work is to statistically analyse CAP aid in southwest Spain, specifically in Extremadura, a region characterised by areas at risk of depopulation and deep rural areas, during the last two periods of European aid (2007–2013 and 2014–2020). In this study, by means of principal component analysis (PCA), agricultural, economic, and demographic variables were interrelated, together with geographic information systems (GIS), to evaluate their impact on the development of the regional agricultural sector. This methodology will help to identify areas with improvements in their territorial structures and can therefore serve as a basis for their application in other European territories. Through the analysis, we identified areas where the interrelation of the variables showed improvements in their demographic, social, and economic structures, with the municipalities of populations over 10,000 inhabitants in Extremadura being the ones that compose the territorial substructure A. Therefore, this work shows how European agricultural aid can disproportionately favour the most dynamic territories and leave the less developed regions at a disadvantage. [ABSTRACT FROM AUTHOR]

In an era of intense urban development and climate extremes, green infrastructure (GI) has become crucial for creating sustainable, livable, and resilient cities. However, the efficacy of GI is frequently undermined by haphazard implementation and resource misallocation that disregards appropriate spatial scales. This study develops a geographic information system (GIS)-based critical area detection model (CADM) to identify priority areas for the strategic placement of GI, incorporating four main indices—spatial form, green cover, gray cover, and land use change—and utilizing the digital elevation model (DEM), normalized difference vegetation index (NDVI), urban density index (UDI), and up-to-date land use data. By employing the developed method, the study successfully locates priority zones for GI implementation in Saitama City, Japan, effectively pinpointing areas that require immediate attention. This approach not only guarantees efficient resource allocation and maximizes the multifunctional benefits of GI but also highlights the importance of a flexible, all-encompassing GI network to address urbanization and environmental challenges. The findings offer policymakers a powerful tool with which to optimize GI placement, enhancing urban resilience and supporting sustainable development. [ABSTRACT FROM AUTHOR]

This paper is intended to elucidate the efficacy of employing a Geographic Information Systems (GIS) methodology to investigate the spatial interrelations among migration flows, housing prices, age demographics, and geographical distances. Utilizing data on migration flows and housing prices from Greater Manchester, UK, in 2011, in this study, we performed statistical analyses and generated a suite of visual mappings to illustrate the spatial dynamics between migration patterns, distances, age brackets, and the segmentation of migration destinations by housing price tiers. The findings reveal pronounced spatial associations among migration destinations, underscored by the interplay of housing price levels with age categories, distance intervals, or the origination points tied to specific housing price ranges. This GIS-based analytical framework offers policy-makers and urban planners a powerful tool for discerning distinct migration trends and locales, thereby facilitating more informed decision-making processes. [ABSTRACT FROM AUTHOR]

Soil moisture plays a critical role in agricultural productivity and water resource management, especially in a diverse and populous country like India. Understanding variations in soil moisture across different regions and seasons is essential for adapting agricultural practices and water management strategies to local conditions. This study examines changes in soil moisture levels across India, comparing contemporary data from 2023 with historical averages from 2000 to 2005 using advanced remote sensing and GIS technologies. The primary objective of this study is to identify Soil Moisture Anomalies (SMA) across India, quantify their impacts on agriculture and water resources, and provide recommendations for targeted management strategies. By comparing recent soil moisture data against historical averages, the study aims to highlight trends and changes that could influence future water resource planning and agricultural practices. The research utilizes data from the Famine Early Warning Systems Network's (FEWS NET) i.e. Land Data Assimilation System (FLDAS), obtained from NASA's data archives. The study employs a systematic approach to analyze seasonal variations in soil moisture across different Indian states. Soil moisture levels were analyzed using zonal statistics in GIS to classify regions into categories based on the degree of anomaly observed. This classification helped in understanding the spatial distribution of soil moisture during the pre-monsoon, monsoon, and post-monsoon seasons. The study found significant regional and seasonal variations in soil moisture across India. During the monsoon period, areas such as Bihar and Jharkhand consistently showed significant moisture deficits, indicative of drought conditions, affecting agricultural output and necessitating urgent water conservation measures. Conversely, regions like Punjab benefited from positive soil moisture anomalies, enhancing agricultural productivity. The pre-monsoon and post-monsoon seasons also showed variations, with some areas experiencing deficits requiring careful water management while others had surpluses that increased the risk of flooding. The analysis of SMA in India underscores the need for region-specific agricultural and water management strategies that consider significant variability in soil moisture conditions. The study highlights the importance of integrating soil moisture monitoring into national policy frameworks to enhance climate resilience and sustainable agricultural practices. Future research should focus on updating soil moisture assessments with more recent data and refining predictive models to improve the accuracy and effectiveness of water management and agricultural interventions. [ABSTRACT FROM AUTHOR]

NZCCs aim to minimise urban carbon emissions for healthier cities in line with national and international low-carbon targets and Sustainable Development Goals (SDGs). Many countries have recently adopted Net-Zero Carbon City (NZCC) policies and strategies. While there are many studies available on NZCC cities' definitions and policymaking, currently, research is rare on understanding the role of urban data-driven technologies such as Building Information Modelling (BIM) and Geographic Information Systems (GIS), as well as AI, for achieving the goals of NZCCs in relation to sustainable development goals (SDGs), e.g., SDGs 3, 7,11, 13, and 17. This paper aims to fill this gap by establishing a systematic review and ascertaining the opportunities and barriers of data-driven approaches, analytics, digital technologies, and AI for supporting decision-making and monitoring progress toward achieving NZCC development and policy/strategy development. Two scholarly databases, i.e., Web of Science and Scopus databases, were used to find papers based on our selected relevant keywords. We also conducted a desktop review to explore policies, strategies, and visualisation technologies that are already being used. Our inclusion/exclusion criteria refined our selection to 55 papers, focusing on conceptual and theoretical research. While digital technologies and data analytics are improving and can help in the move from net-zero carbon concepts and theories to practical analysis and the evaluation of cities' emission levels and in monitoring progress toward reducing carbon, our research shows that these capabilities of digital technologies are not used thoroughly yet to bridge theory and practice. These studies ignore advanced tools like city digital twins and GIS-based spatial analyses. No data, technologies, or platforms are available to track progress towards a NZCC. Artificial Intelligence, big data collection, and analytics are required to predict and monitor the time it takes for each city to achieve net-zero carbon emissions. GIS and BIM can be used to estimate embodied carbon and predict urban development emissions. We found that smart city initiatives and data-driven decision-making approaches are crucial for achieving NZCCs. [ABSTRACT FROM AUTHOR]

This paper proposes a lens of analysis for studying how public urban park creation, often presented as inherently beneficial for the public by planners, government officials, and stakeholders, served to enforce prevailing social and political norms by excluding unwanted visitors and fostering specific codes of conduct. The two case studies of post-war park development in Richmond, Virginia, U.S., and Hamburg, DE, exemplify the social and political effects of restoration ideas and ideals on beneficiaries and non-beneficiaries. In Richmond, the construction of Chimborazo Park displaced and excluded an African American (Black) community during the aftermath of the Civil War, while Hamburg's Alsterpark was created within the city’s general post-World War II greening initiatives that included forced expropriation and behavior regulation. The joint spatial humanities based approach, which includes GIS modeling and archival source analysis, furthers historical investigation to more deeply understand patterns of displacement, exclusion, and social control. The paper offers an approach for critically examining the planning histories of parks that were created under the justification of restoration but operated as sites of contestation and ideology during moments of societal, political, and social reform. Highlights: • Historical inquiry into the study of public urban park planning and development. • Use of GIS tools to analyze historical sources, especially atlases and maps, to reveal people’s perceptions, uses, and regulations of space as well as highlights experiences of place. • Challenges the assumption that parks are automatically beneficial to the public.

Working from the GLOBALISE project, we have enhanced access to over 5,000 Dutch maps from the seventeenth and eighteenth centuries, renowned for their geographic and historical significance. We have laid the groundwork for enriching these maps with annotations and metadata following the International Image Interoperability Framework (IIIF) standards, directly linked to their digital representations from the collection-holding institutions. This approach ensures a FAIR framework for sharing research results and fosters collaborative annotation. We enriched the maps with georeferencing annotations and employed automated tools to extract toponyms and geographic features, enabling large-scale analysis. These annotations open new pathways for exploring understudied historical regions, such as early modern Kerala, and assessing Dutch colonial influence globally. By publishing our data early, we aim to encourage further collaborative refinements, allowing a wider scholarly community to improve and build upon this work. This approach highlights the potential of combining historical cartography with computational tools to empower large-scale geographic and historical research. Highlights: • Dataset of over 5,000 early modern Dutch maps, accessible for collaborative annotation. • Automated tools used to extract geographic features, toponyms, and cartographic symbols from maps. • New opportunities for large-scale analysis of early modern topographies and Dutch colonial influence.

Effects of climate change in semi-arid areas occur in drought events, which affect aquifers whose recharge depends essentially on precipitation. The objective of this study is to evaluate the relationship between depth to groundwater (DTW), precipitation, Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST), in the alluvial aquifer of Mostaganem Plateau, Algeria over 2000, 2005, 2010-2011 and 2014-2015. This is caried out through an adaptive methodology, using remote sensing, Geographic Information Systems (GIS), and statistical analysis: correlation analysis and Multiple Linear Regression (MLR). The results indicate a 62 mm decline in precipitation from 2000 to 2015, inducing shifts in spatial patterns. This resulted in an increase of DTW (4 m to 10 m). The strong negative correlation between decreased precipitation and increased DTW, supported by an R2 value of -0.80, is evident. Moreover, NDVI and LST values increased notably by 0.034 and 3.38°C, respectively. The relationship between DTW, NDVI, and LST showed a diminishing negative correlation. The MLR reaffirmed the influence of precipitation and highlighted the impact of human activity on DTW and drought indicators effectiveness. High NDVI values indicated intensive groundwater pumping, while elevated LST contributed to DTW decrease due to increased evaporation rates caused by changes in crop types resulting from human actions. This study contributes to the understanding of the dynamic interactions between DTW, precipitation, and anthropogenic activities and gives insight to decision makers regarding irrigation strategies.

This study used data from the second Nigeria Malaria Indicator Survey (NMIS) conducted in 2015 to investigate the spatial distribution of malaria prevalence in the country and identify its associated factors. Nigeria is divided into 36 states with 109 senatorial districts, most of which are affected by malaria, a major cause of morbidity and mortality in children under five years of age. We carried out an ecological study with analysis at the senatorial district level. A malaria prevalence map was produced combining geographic information systems data from the Nigeria Malaria Indicator Survey (NMIS) of 2015 with shape files from an open data-sharing platform. Spatial autoregressive models were fitted using a set of key covariates. Malaria prevalence in children under-five was highest in Kebbi South senatorial district (70.6%). It was found that poorest wealth index (β = 0.10 (95% CI: 0.01, 0.20), p = 0.04), mothers having only secondary level of education (β = 0.78 (95% CI: 0.05, 1.51), p = 0.04) and households without mosquito bed nets (β = 0.21 (95% CI: 0.02, 0.39), p = 0.03) were all significantly associated with higher malaria prevalence. Moran’s I (54.81, p

Land use refers to how the surface of a designated area is utilized, encompassing both developed and vacant lands. The intricate interplay between population growth and the availability of natural resources gives rise to diverse challenges that are exhibited in the recharge of groundwater. This research was conducted in the Koraiyar Watershed, Coimbatore District, Tamil Nadu, India. The change in the land use land cover (LULC) was detected by digitizing the linear imaging self scanning sensor (LISS) III and LISS IV satellite images for the years 2000 and 2011 using the maximum likelihood classification algorithm. LANDSAT-8 OLI data were processed for the year 2020. The study area has subsequently undergone an assessment of groundwater levels to determine the impact between groundwater and land use land cover. The result of the study indicates a continuous decrease in the wet crop area, which is the effect of the gradual depletion of groundwater resources. Along with this, the narrowing of the water bodies and increased areas of dry crops, industrial areas, and quarries are also responsible for lowering groundwater table depletion and contamination. Settlements increased from 39 to 49 km2 from 2000 to 2011 and then gradually increased by 50 km2 in 2020 to meet the demand of the population. From 2000 to 2019, the groundwater level has been declining with respect to the LULC pattern. The increase in concrete platforms in the study can affect the infiltration. Thus, the study can aid in developing water management strategies and decisions for governmental and non-governmental policymakers.

Abstract Within the global architecture, engineering, and construction industry, the use of Building Information Modeling (BIM) technology has significantly expanded. However, given the unique characteristics of road infrastructure, the application of BIM technology is still being explored. This article focuses on the Yuanchen Expressway, exploring innovative applications of BIM technology in comprehensive construction management. The project employs advanced technologies, including BIM, Geographic Information Systems (GIS), and the Internet of Things (IoT), to precisely identify critical nodes and breakthroughs. Supported by a detailed BIM model and a multi-level, diversified digital management platform, the project effectively addresses construction challenges in multiple tunnels, bridges, and complex interchanges, achieving intelligent construction innovation throughout the Yuanchen Expressway with BIM technology. By guiding construction through BIM models, utilizing a BIM+GIS-based management cloud platform system, and employing VR safety briefings, the project effectively reduces the difficulty of communication and coordination in project management, shortens the project measurement cycle, improves on-site work efficiency, and ensures comprehensive control and safety management. This article provides an exemplary case for the application of full-line construction management using BIM technology in the highway sector both in China and globally, offering new perspectives and strategies for highway construction management.

Abstract Electric grids with buses that are mapped to geographic latitude and longitude are useful for a growing number of applications, such as data visualization, geomagnetically induced current calculations, and multi-energy coupled infrastructure simulations. This paper presents a methodology for validating the quality of geographic coordinates for a power system model, and to assign coordinates to buses with missing or low-quality coordinates. This method takes advantage of geographic indicators already intrinsic to a grid model, such as branch length as implied by impedance and susceptance parameters. The coordinate assignment process uses an approach inspired by graph drawing, that lays out the vertices (buses) and edges (transmission lines), formulated as a nonlinear programming problem with soft edge length constraints. The layout method is very computationally fast and scalable to large power system cases. The method is demonstrated in this paper using a 37-bus test case and a 6717-bus test case, both publicly available, along with a large actual grid model. The results show that, for cases with only a few errors in the coordinates, cases with no coordinates known beforehand, and others in between, this method is able to assign reasonable geographic coordinates to best match known data about the grid.

Abstract In this paper, we analysed the temporal and spatial distributions of ancient archaeological sites in China as well as their influencing factors. Our aim was to reveal the developmental trajectory of Chinese civilisation and to explore the natural and cultural factors affecting human distribution, with the goal of providing insights for the conservation and development of ancient relics. We employed spatial analysis methods using ArcGIS 10.8 software, such as kernel density analysis and trend surface analysis, to analyse 1194 historical ancient sites listed in the National Cultural Relics Protection Units of China. The research findings are as follows: (1) the distribution of ancient sites demonstrates an agglomerative spatial pattern. The nearest neighbour index (R

Abstract Water resource management relies heavily on the utilization of decision-making systems to guide the strategic rehabilitation and renovation of infrastructure within water distribution networks (WDNs). This study aims to develop and apply a scripting tool in ArcGIS for decision-making in WDN. It combines a collection of decision support systems, including Geographic Information Systems (GIS) and multi-criteria analysis, for infrastructure renovation and rehabilitation. The research is divided into two main sections. The first focuses on creating and implementing the analytic hierarchy process tool in ArcGIS, while the second discusses its application in the study area, which is the drinking water distribution network of an urban area in Tlemcen, located in the Northwest of Algeria. The study’s results reveal that 25% of the network is in critical condition and require immediate rehabilitation. Sixty-two percentage of the network is classified as moderately urgent, indicating a pressing but less immediate need for intervention. Finally, the remaining 13% of the network requires attention over a longer timeframe for rehabilitation. The main criteria influencing pipe prioritization are material (26% of total weight), laying date (26% of total weight), and pressure (20% of total weight). This developed tool can be easily adapted and applied by engineers and water management officers. It serves as a guide for decision-makers and planners in urban water management in Tlemcen and can be replicated and applied to other areas worldwide.

Flood risk assessment remains a crucial element, particularly within locations highly susceptible to repeated flood occurrences. This study seeks to conduct an elaborate flood risk analysis for Mumbai, India based on an integrated method of geographic information systems and analytic hierarchy process (AHP). In this study, land use/land cover, average annual rainfall, elevation, drainage density, normalized difference vegetation index, distance from rivers as well as distance from roads are identified and considered. For this reason, the expert survey utilizes the AHP weights so as to find out the significance of these factors towards flooding danger. Investigations show a flood risk index and a corresponding map for Mumbai, where all areas are divided into risk zones very low to very high. Very high risks are particularly situated along rivers. Such details offer critical knowledge to policymakers who will undertake informed emergency preparedness measures designed to shield Mumbai's citizens and assets. Therefore, this research is considered as one of the modern techniques for flood risk assessment, which can be applied in other flood-affected areas worldwide. HIGHLIGHTS Merges the geographic information system and the analytic hierarchy process (AHP) for Mumbai flood risk, giving a holistic view.; Evaluate land use, rainfall, elevation, and river proximity, enhancing risk assessments.; Introduces the flood risk index from the AHP, aiding targeted emergency plans.; Detailed flood risk map divides Mumbai into ‘Very Low’ to ‘Very High’ risk zones for precise mitigation strategies.; Offers a modern flood risk model, valuable worldwide amid climate change impacts.;

This study focuses on a multicriteria evaluation to determine optimal urban expansion areas in the canton of Naranjito using Geographic Information Systems (GIS). Restriction zones, such as consolidated areas and those near wastewater treatment plants where urban expansion is not feasible, were identified. A detailed geospatial model was developed based on layers representing key factors such as hospitals, educational centers, police stations, rivers, and roads. The Saaty methodology (MCE) was employed to weigh these variables, highlighting the relative importance of each in decision-making. Higher relevance was given to factors such as proximity to hospitals and police stations, while others, such as vegetation cover and soil slope, were considered to have less impact due to their minimal influence on urban expansion in this area. The consistency analysis confirmed the coherence in the weighting of the criteria. The GIS modeling results indicated that the most suitable areas for urban growth are close to already urbanized zones, which supports the utilization of existing infrastructure. This comprehensive approach allows for more efficient and sustainable urban planning, tailored to the specific characteristics of the Naranjito territory. [ABSTRACT FROM AUTHOR]