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"Geospatial analysis"
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Identifying the Potential Dam Sites to Avert the Risk of Catastrophic Floods in the Jhelum Basin, Kashmir, NW Himalaya, India
In September 2014, Kashmir witnessed a catastrophic flood resulting in a significant loss of lives and property. Such massive losses could have been avoided if any structural support such as dams were constructed in the Jhelum basin, which has a history of devastating floods. The GIS-based multicriteria analysis (MCA) model provided three suitability zones for dam locations. The final suitable dam sites were identified within the highest suitability zone based on topography (cross-sections), stream order, high suitable zone, minimum dam site interval, distance from roads, and protected area distance to the dam site. It was discovered that 10.98% of the total 4347.74 km2 area evaluated falls in the high suitability zone, 28.88% of the area falls in the medium suitability zone, and 60.14% of the area falls in the low suitability zone. Within the study area, four viable reservoir sites with a holding capacity of 4,489,367.55 m3 were revealed.
Journal Article
Spatiotemporal Patterns of Agriculture Expansion Intensity and Land-Use/Cover Changes in the Mixed Urban-Rural Upper Kafue River Basin of Zambia (1989–2019)
Understanding land-use and land-cover (LULC) changes is essential for sustainable land management, particularly in regions experiencing rapid urbanization and agricultural expansion. This study analyzes the LULC dynamics in the Upper Kafue River Basin, Zambia, from 1989 to 2019, using remote-sensing data, Geographic Information Systems (GISs), and advanced analytical techniques such as intensity analysis and directional gradient analysis. The findings indicate a notable decline in forest cover, primarily driven by agricultural expansion, while built-up areas increased, reflecting urban growth. Forest-to-agriculture conversion emerged as the dominant driver of change, with significant transitions also occurring across multiple land categories. The results highlight a dynamic and complex landscape shaped by overlapping socio-economic and environmental pressures, emphasizing the need for targeted policy interventions to mitigate environmental degradation. These insights provide valuable guidance for policymakers and land managers seeking to balance development with conservation in Zambia and similar regions.
Journal Article
ANALYSIS OF SPATIAL DISPARITY OF PHARMACIES IN VIRGINIA, U.S.A
Many scholars have studied spatial equity issues of urban service delivery facilities in the past, including the pharmacy accessibility and pharmacy deserts. However, the analysis of spatial disparity of pharmacies in Virginia is lacking. To fill this research gap, we employed both statistical and geospatial methods to examine the pharmacy disparity and desert issues in Virginia. These methods include correlation, stepwise regression, average nearest neighbor analysis, network analysis, and geographically weighted regression (GWR). We examined five vulnerable populations and their accessibility to pharmacies. These subpopulations include racial minorities (defined as nonwhite population in this study), persons with income below the poverty level, older adults (age 65+), persons with disability, and households without vehicles. We found that spatial inequity of pharmacies exists in Virginia. At the statewide macro level, the spatial distribution of older adults is, largely, correlated with that of pharmacies. However, as revealed by GWR at local levels, the spatial pattern of pharmacy distribution is much more complicated, exhibiting both spatial inequity and social inequity (especially racial inequity, which is ubiquitous in Virginia). Pharmacies may be adequate for certain groups of people, but simultaneously inadequate for others.
Journal Article
Introduction to Reproducible Geospatial Analysis and Figures in R: A Tutorial Article
The present article is intended to serve an educational purpose for data scientists and students who already have experience with the R language and which to start using it for geospatial analysis and map creation. The basic concepts of raster data, vector data, CRS and datum are first presented along with a basic workflow to conduct reproducible geospatial research in R. Examples of important types of maps (scatter, bubble, choropleth, hexbin and faceted) created from open-source environmental data are illustrated and their practical implementation in R is discussed. Through these examples, essential manipulations on geospatial vector data are demonstrated (reading, transforming CRS, creating geometries from scratch, buffer zones around existing geometries and intersections between geometries).
Journal Article
Remote Sensing and Geospatial Analysis in the Big Data Era: A Survey
The present survey examines the role of big data analytics in advancing remote sensing and geospatial analysis. The increasing volume and complexity of geospatial data are driving the adoption of machine learning (ML) and artificial intelligence (AI) techniques, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, to extract meaningful insights from large, diverse datasets. These AI methods enhance the accuracy and efficiency of spatial and temporal data analysis, benefiting applications in environmental monitoring, urban planning, and disaster management. Despite these advancements, challenges related to computational efficiency, data integration, and model transparency remain. This paper also discusses emerging trends and highlights the potential of hybrid approaches, cloud computing, and edge processing in overcoming these challenges. The integration of AI with geospatial data is poised to significantly improve our ability to monitor and manage Earth systems, supporting more informed and sustainable decision-making.
Journal Article
Italian investments for soil defence: retrieving and visualizing data by the PublicWorksFinanceIT R Package
The PublicWorksFinanceIT R package enables users to retrieve and analyze financial data related to public works in Italy. Specifically, it focuses on soil defence investments. The data are sourced from three distinct platforms: the OpenCoesione website, which draws its information from the Cohesion Policy, the OpenBDAP website, the Ministry of Economy and Finance’s open data platform, and the ReNDiS database, provided by ISPRA, which exclusively gathers information about interventions in soil defence. This package offers a user-friendly tool that eliminates the need for direct access to the aforementioned institutional platforms and ensures real-time updates. Additionally, all measurements, metadata, and accompanying analytical tools are provided in English, enhancing accessibility for both international and domestic users. The data records from these three sources are linked using the unique project code (CUP), ensuring that there is no duplication. Moreover, the data is geographically referenced, meaning that each financial investment is associated with a specific municipality within a particular Italian region. This allows to provide information on the region, province, and municipality of each dataset entry. Users can select to geo-reference the data by either the coordinates of the municipality’s centroid or by the polygon representing the municipality’s administrative boundaries. In addition to functions for data retrieval, the package includes functions for visualizing the collected data on maps. After providing a detailed explanation of the purpose and operation of the main commands, the paper presents two case studies illustrating the software’s application. These examples serve as a step-by-step guide to using the PublicWorksFinanceIT package.
Journal Article
Assessment of Carcinogenic and non-carcinogenic risk indices of heavy metal exposure in different age groups using Monte Carlo Simulation Approach
Dermal contact, ingestion and inhalation of heavy metal poses significant health risk in human subjects. The exposure could be via potable water, soil or air. The current experiment design focuses on soil media and exposure. Advanced probabilistic and geospatial methods are used in this study which evaluates contamination levels and health risks associated with iron (Fe), arsenic (As), zinc (Zn), copper (Cu), nickel (Ni), chromium (Cr), lead (Pb), and cadmium (Cd) in soil samples. The samples were collected and analysed using ICP-OES after tri-acid digestion, and indices such as Geoaccumulation Index (Igeo), Contamination Factor (CF), Pollution Load Index (PLI), Hazard Quotient (HQ), Hazard Index (HI), and Carcinogenic Risk (CR) were used to assess environmental impacts and health risks across the age groups via oral ingestion, inhalation and dermal contact. The current study indicates heavy metal concentrations follow the order Ni > Zn > Pb > Cu > Cr > As > Cd, with more than 60% of samples demonstrating significant pollution levels. The computational method used in the study revealed substantial non carcinogenic risk (HQ > 1) and carcinogenic risk (33%) in the population related to As exposure. Geospatial analysis and Monte Carlo simulations helped in identifying the hotspots in the tropical coastal area emphasizing need for targeted remediation focusing on As and Pb.
Journal Article
ChatGeoAI: Enabling Geospatial Analysis for Public through Natural Language, with Large Language Models
Large Language Models (LLMs) such as GPT, BART, and Gemini stand at the forefront of Generative Artificial Intelligence, showcasing remarkable prowess in natural language comprehension and task execution. This paper proposes a novel framework developed on the foundation of Llama 2, aiming to bridge the gap between natural language queries and executable code for geospatial analyses within the PyQGIS environment. It empowers non-expert users to leverage GIS technology without requiring deep knowledge of geospatial programming or tools. Through cutting-edge Natural Language Processing (NLP) techniques, including tailored entity recognition and ontology mapping, the framework accurately interprets user intents and translates them into specific GIS operations. Integration of geospatial ontologies enriches semantic comprehension, ensuring precise alignment between user descriptions, geospatial datasets, and geospatial analysis tasks. A code generation module empowered by Llama 2 converts these interpretations into PyQGIS scripts, enabling the execution of geospatial analysis and results visualization. Rigorous testing across a spectrum of geospatial analysis tasks, with incremental complexity, evaluates the framework and the performance of such a system, with LLM at its core. The proposed system demonstrates proficiency in handling various geometries, spatial relationships, and attribute queries, enabling accurate and efficient analysis of spatial datasets. Moreover, it offers robust error-handling mechanisms and supports tasks related to map styling, visualization, and data manipulation. However, it has some limitations, such as occasional struggles with ambiguous attribute names and aliases, which leads to potential inaccuracies in the filtering and retrieval of features. Despite these limitations, the system presents a promising solution for applications integrating LLMs into GIS and offers a flexible and user-friendly approach to geospatial analysis.
Journal Article
Geodiversity assessment of Shkodra Municipality, Albania
The objective of this paper is to present a quantitative assessment of geodiversity for Shkodra Municipality, which is located in north-western Albania and comprises an area of 953.64 km2. It is one of the richest geosites in Albania, with Lake Shkodra being the largest lake in the Balkan Peninsula, situated along the Adriatic Sea shore and extending up to the Albanian Alps. The municipality’s favourable geographical positioning and climatic conditions offer numerous benefits for expanding diverse forms of tourism, particularly geotourism activities, which allow visitors to engage with the peculiar geological features of the municipality. The cultural heritage of the municipality, with the ancient Rozafa Castle at its heart, is a key factor in its appeal as a tourist destination. This assessment represents the first research that demonstrates the complex diversity of the geoscientific features of Shkodra Municipality. Geodiversity was calculated based on the geological, palaeontological, soil, mineral occurrences, and morphological diversity of the area using published maps and geodatabases. It was found that two-thirds of the municipality is classified as medium or high geodiversity, and 10% as very high geodiversity. Four main hotspots of very high geodiversity were identified, allowing a concentrated presentation of the geological heritage and enhancing visitors’ understanding of the area. Shkodra’s geodiversity is vital for promoting geotourism and for reflecting the cultural and historical background of the municipality. This study also contributes to the knowledge of the geological and geomorphological features and promotes geoconservation.
Journal Article