Geospatial Data Analysis Research Articles

Monitoring of Traffic Flow Parameters Using Geospatial Technology for Effective Traffic Management around Maiduguri Central Business District

Abstract: The ever-increasing traffic flow has become one of the most critical challenges faced by municipalities and governments worldwide. This paper intends to ascertain the traffic flow parameters using geospatial technology for effective traffic management around the Central Business District (CBD) in Maiduguri to provide solutions to the menace of traffic congestion. Data were obtained from primary and secondary sources. The primary data were obtained from Unmanned Aerial Vehicles (UAV) and Global Positioning System (GPS). UAVs were utilized to record (videography) the traffic flow of the chosen traffic corridors during the peak hours (morning, 7:00 to 9:00 am and evening, 4:00 to 6:00 pm). The classification of the vehicles and conversion to a common standard format was done in Microsoft Excel before being carried forward for GIS Operations in ArcGIS 10.7 Environment to achieve geospatial spatial analysis and mapping of the field data. The results show that Tricycles dominated the traffic during the peak periods, followed by Cars. This indicates that the Tricycle has been the overriding and preferred means of public transport in Maiduguri since 2009 after the ban placed on motorcycles. The also reveals that no matter the direction of movement in all the approach arms, Tricycles override all other means of transportation. The results also show that all the Trio Intersections were observed to have high traffic volume on all their trajectory arms, with only the Tabacco intersection having medium traffic volume during the morning peak period. Given the above findings, the study recommended that the Government to consider the construction of a flyover, particularly at the post office intersection.

Advancing Seabed Bedform Mapping in the Kuźnica Deep: Leveraging Multibeam Echosounders and Machine Learning for Enhanced Underwater Landscape Analysis

The ocean, covering 71% of Earth’s surface, remains largely unexplored due to the challenges of the marine environment. This study focuses on the Kuźnica Deep in the Baltic Sea, aiming to develop an automatic seabed mapping methodology using multibeam echosounders (MBESs) and machine learning. The research integrates various scientific fields to enhance understanding of the Kuźnica Deep’s underwater landscape, addressing sediment composition, backscatter intensity, and geomorphometric features. Advances in remote sensing, particularly, object-based image analysis (OBIA) and machine learning, have significantly improved geospatial data analysis for underwater landscapes. The study highlights the importance of using a reduced set of relevant features for training models, as identified by the Boruta algorithm, to improve accuracy and robustness. Key geomorphometric features were crucial for seafloor composition mapping, while textural features were less significant. The study found that models with fewer, carefully selected features performed better, reducing overfitting and computational complexity. The findings support hydrographic, ecological, and geological research by providing reliable seabed composition maps and enhancing decision-making and hypothesis generation.

The integration of geospatial analyses based on AI in GIS in the context of the “15-minute city” concept

The “15-minute city” concept is an urban planning model that aims to ensure all essential services are accessible within a 15-minute walk or bike ride in order to reduce the reliance on cars and to offer a sustainable approach to urban living, contributing to the achievement of green and inclusive smart cities. AI (Artificial Intelligence) tools can provide the means to obtain a quick and efficient response in analysing urban geospatial data and can make data analysis a process available even for non-specialist users. Integrating AI-driven geospatial analysis in GIS can significantly enhance the planning and implementation of this concept, by providing precise, real-time data and predictive insights. The aim of this article is to analyse the possibilities of geospatial data management and analysis using AI tools, within the context of the “15-minute city”, to contribute to the process of obtaining green and inclusive smart cities.

Effectiveness of Hybrid Quantum-Classical and Quanvolutional Neural Networks for image classification

The article focuses on studying the effectiveness of two different Hybrid Neural Networks (HNNs) architectures for solving real-world image classification problems. The first approach investigated in the research is a hybridization technique that allows creation of HNN based on a classical neural network by replacing a number of hidden layers of the neural network with a variational quantum circuit, which allows to reduce the complexity of the classical part of the neural network and move part of computations to a quantum device. The second approach is a hybridization technique based on utilizing quanvolutional operations for image processing as the first quantum convolutional layer of the hybrid neural network, thus building a Quanvolutional Neural Network (QNN). QNN leverages quantum phenomena to facilitate feature extraction, enabling the model to achieve higher accuracy metrics than its classical counterpart. The effectiveness of both architectures was tested on several image classification problems. The first one is a classical image classification problem of CIFAR10 images classification, widely used as a benchmark for various imagery-related tasks. Another problem used for the effectiveness study is the problem of geospatial data analysis. The second problem represents a real-world use case where quantum computing utilization can be very fruitful in the future. For studying the effectiveness, several models were assembled: HNN with a quantum device that replaces one of the hidden layers of the neural network, QNN based on quanvolutional operation and utilizes VGG-16 architecture as a classical part of the model, and also an unmodified VGG-16 was used as a reference model. Experiments were conducted to measure the models' key efficiency metrics: maximal accuracy, complexity of a quantum part of the model and complexity of a classical part of the model. The results of the research indicated the feasibility of both approaches for solving both proposed image classification problems. Results were analyzed to outline the advantages and disadvantages of every approach in terms of selected key metrics. Experiments showed that QNN architectures proved to be a feasible and effective solution for critical practical tasks requiring higher levels of model prediction accuracy and, simultaneously, can tolerate higher processing time and significantly increased costs due to a high number of quantum operations required. Also, the results of the experiments indicated that HNN architectures proved to be a feasible solution for time-critical practical tasks that require higher processing speed and can tolerate slightly decreased accuracy of model predictions.

"Integrating AHP and geospatial data analysis for mapping groundwater potential in tropical coastal villages of Thiruvananthapuram, Southern India"

"Integrating AHP and geospatial data analysis for mapping groundwater potential in tropical coastal villages of Thiruvananthapuram, Southern India"

Geodl: An R package for geospatial deep learning semantic segmentation using torch and terra.

Convolutional neural network (CNN)-based deep learning (DL) methods have transformed the analysis of geospatial, Earth observation, and geophysical data due to their ability to model spatial context information at multiple scales. Such methods are especially applicable to pixel-level classification or semantic segmentation tasks. A variety of R packages have been developed for processing and analyzing geospatial data. However, there are currently no packages available for implementing geospatial DL in the R language and data science environment. This paper introduces the geodl R package, which supports pixel-level classification applied to a wide range of geospatial or Earth science data that can be represented as multidimensional arrays where each channel or band holds a predictor variable. geodl is built on the torch package, which supports the implementation of DL using the R and C++ languages without the need for installing a Python/PyTorch environment. This greatly simplifies the software environment needed to implement DL in R. Using geodl, geospatial raster-based data with varying numbers of bands, spatial resolutions, and coordinate reference systems are read and processed using the terra package, which makes use of C++ and allows for processing raster grids that are too large to fit into memory. Training loops are implemented with the luz package. The geodl package provides utility functions for creating raster masks or labels from vector-based geospatial data and image chips and associated masks from larger files and extents. It also defines a torch dataset subclass for geospatial data for use with torch dataloaders. UNet-based models are provided with a variety of optional ancillary modules or modifications. Common assessment metrics (i.e., overall accuracy, class-level recalls or producer's accuracies, class-level precisions or user's accuracies, and class-level F1-scores) are implemented along with a modified version of the unified focal loss framework, which allows for defining a variety of loss metrics using one consistent implementation and set of hyperparameters. Users can assess models using standard geospatial and remote sensing metrics and methods and use trained models to predict to large spatial extents. This paper introduces the geodl workflow, design philosophy, and goals for future development.

Advancements in Geohazard Investigations: Developing a Machine Learning Framework for the Prediction of Vents at Volcanic Fields Using Magnetic Data

This study investigates the application of machine learning techniques for predicting volcanic vent locations based on aeromagnetic geophysical data. Magnetic data, known to reflect subsurface geological structures, presents a valuable source of information for understanding volcanic activity. Leveraging this data, we aim to develop and validate predictive models capable of discerning the presence of volcanic vents. Through a comprehensive data analysis, feature engineering, and model training, we explore the intricate relationships between magnetic variations and volcanic vent locations. Various machine learning algorithms were evaluated for their efficacy in binary classification, with a focus on identifying areas with a high likelihood of volcanic vent presence. The Random Forest model (RFM) was adopted given its high performance metrics, achieving a prediction accuracy of 92%. Our results demonstrate the successful prediction of volcanic vent locations, with a significant correlation of 86% between the actual and predicted vent locations and a high Degree of Certainty (DC) at 97%. This research contributes to the advancement of geospatial data analysis within the field of geoscience, showcasing the potential of machine learning in interpreting and utilizing magnetic data for volcanic hazard assessment and early warning systems. The findings represent a significant step towards enhancing our understanding of volcanic dynamics and improving the predictive tools available for volcanic hazard assessment.

Construction of inherent geometric pattern for equal-area hexagonal discrete global grid systems

ABSTRACT Discrete Global Grid Systems (DGGSs) are hierarchical frameworks that provide seamless global coverage and enable the efficient processing and analysis of heterogeneous geospatial data. As cell resolution becomes finer, similarities in cell attributes, such as cell size and shape, increase. Understanding these shared characteristics enhances our knowledge of DGGSs structures, improving their reliability and accuracy across a range of applications. However, existing methods for quantifying these characteristics are limited, often emphasizing overall analysis and visualization rather than detailed assessment. This paper introduces a geometric pattern quantification method for equal-area hexagonal DGGSs by calculating inter-level attribute similarities to identify a reference level, segmenting the spatial distribution image at this reference level, and fitting the boundaries of these sub-images to create iso-feature lines. The results show that this method improves the accuracy and efficiency of cell attribute calculations, with enhancements of at least 5.6 times for study regions and 13.8 times for sample points at lower levels. This pattern provides a robust basis for grid selection and optimization in regional applications, promoting the broader adoption of DGGSs across various fields.

Geolocation of Fixed Emission Sources Produced by Artisan Brick Kilns in the Atmospheric Basin of the City of Juliaca, Peru

Objective: The study objective is to investigate the application of georeferencing systems to identify the geospatial location of fixed sources of atmospheric emissions produced by artisanal brickyards in the air basin of Juliaca city-Peru. Theoretical Framework: Previous studies have shown that artisanal brickyards are a significant source of air pollution in developing urban and on the outskirts. Emissions of fine particles, nitrogen oxides and other pollutants from the burning of traditional fuels, such as firewood and coal contribute to the degradation of air quality and can have adverse effects on human health and the environment. The georeferencing application and geospatial analysis techniques in the atmospheric pollution study have allowed a better understanding of the spatial distribution of emission sources and their impact on the environment. These tools are essential to identify and map the artisanal brickyard locations and to evaluate their contribution to air pollution in urban and on the outskirts. Method: The methodology adopted for this research includes data collection and compilation of existing information; georeferencing of brickyards through the use of geographic information systems (GIS); analysis of geospatial data for the identification of spatial patterns; preparation of thematic and spatial distribution maps; and the interpretation of results. Results and Discussion: The study results highlight the importance of locating the fixed sources of emissions produced by artisanal brick factories in the Juliaca air basin. This precise spatial identification provides a solid basis for the formulation of policies and mitigation strategies aimed at reducing air pollution in the region, in addition to representing fundamental data for the use and exploitation of GIS for environmental protection and modelling, among others. Research Implications: The results can be applied or influence practices in the environmental and forestry engineering field, the ICTs application, modelling and simulation, and territorial planning, among others. Originality/Value: This study contributes to the literature using georeferencing techniques for environmental modelling purposes. The relevance and value of this research are evident in obtaining georeferenced points for the estimation of emission factors for the predictive calculation of volumes, flows and dispersion of pollutants in an air basin.

Multi-parameter study of Aculeo lagoon (Chile)

Abstract. The Aculeo Lagoon, located in the Santiago Metropolitan Region, was one of the main water reservoirs of central Chile. It has experienced a decline in water levels since 2009 and completely dried up in 2018. It was unable to recover until 2023 due to sustained demand for water resources associated with population growth, changes in land use, and years of drought. The objective of this study is to estimate how anthropogenic factors and climate change have influenced the drying of the lagoon. Parameters considered include population growth, housing increases, changes in land use within the Aculeo basin, local temperature and precipitation variations, and global El Niño Southern Oscillation (ENSO) phenomena. Input data included multi-year Landsat images, census data, and a systematic review of the literature on the study area. Furthermore, applying geospatial data analysis techniques such as satellite image processing, supervised classification of land cover/use, and calculation of spectral indices, it is possible to conclude that the Aculeo Lagoon dried out due to a significant precipitation deficit over nearly a decade, exacerbated by the overexploitation of land for agricultural activities.

Unleashing the Dynamic Strategies in the Era of Change – A Spotlight on the ISPRS Student Consortium's Global Contributions

Abstract. ISPRS Student Consortium (ISPRS SC) has played a significant role in bringing students and young researchers closer to the field of remote sensing and photogrammetry in the evolving landscape of geospatial sciences. Innovative strategies and events have been employed by the ISPRS SC to address contemporary challenges and harness emerging opportunities in the field of photogrammetry and remote sensing. Through an analysis of the global contributions of ISPRS SC, ranging from research initiatives to the online webinar series, this paper underscores the consortium's commitment to fostering collaboration, innovation, and knowledge exchange among young scholars and professionals worldwide. The paper highlights key thematic areas addressed by ISPRS SC such as advancements in remote sensing technologies, geospatial data analysis, and applications in diverse domains including environmental monitoring, agriculture, urban planning, and disaster management. Moreover, in this paper, we emphasize the role of the consortium in nurturing future leaders in geospatial sciences through ISPRS SC mentorship programs, networking opportunities, and capacity-building initiatives. By highlighting the initiatives led by the ISPRS Student Consortium, this paper underscores the key role of youth-driven innovation for positive change and shaping the future of geospatial sciences on a global scale.

Exploiting Legal Reserve Compensation as a Mechanism for Unlawful Deforestation in the Brazilian Cerrado Biome, 2012–2022

This research investigates the illegal Vegetation Suppression Authorization (VSA) for Legal Reserve Compensation (LRC) in Tocantins, Brazil, between 2012 and 2014. The VSA is a license issued by state environmental agencies that authorizes the removal or clearing of native vegetation. This infraction resulted from the state environmental agency’s mistaken interpretation of Resolution 07/2005 of the State Environmental Council and the Native Vegetation Protection Law, which operates nationally and takes precedence over state resolutions and laws. The study area focuses on eight municipalities contained in the Formoso, Pium, and Javaés river basins. The methodology examines irregular VSA-LRC cases through geospatial data analysis, multitemporal analysis using remote sensing data, and economic assessments in both the origin and destination areas of the compensatory reserve. The results showed that among the 217 VSA-LRC applications during the period, 93 had corresponding geospatial data, indicating that 57.1% of the geospatial data were missing. This discrepancy between the authorized area and the available geospatial information restricts the oversight capability of the regulatory agency. A decade-long deforestation analysis of VSA-LRCs using remote sensing data considered three phases: 2012–2014 (application and issuance of VSA-LRCs), 2015–2018 (operational issuance), and 2019–2022 (post-expiration of VSA-LRCs). The results revealed a total VSA-LRC area of 343.291 km2 (34,329.10 ha), with 229.1169 km2 (22,911.69 ha) of deforestation. Notably, 54.877 km2 (5487.70 ha) of this deforestation occurred in 2019–2022, after the VSA-LRC had expired. The analysis of the LRC origin and destination areas demonstrates a unidirectional strategy, from economically more valuable areas to less valuable ones, according to land market dynamics. Therefore, this investigation concludes the need for greater administrative transparency, the development of a reliable geospatial data system for monitoring, and policy changes and actions to enhance environmental protection.

A place-based spatial analysis of racial inequities in overdose in St. Louis County Missouri, United States

ObjectiveThe objective of this study was to identify place features associated with increased risk of drug-involved fatalities and generate a composite score measuring risk based on the combined effects of features of the built environment. MethodsWe conducted a geospatial analysis of overdose data from 2022 to 2023 provided by the St. Louis County Medical Examiner's Office to test whether drug-involved deaths were more likely to occur near 54 different place features using Risk Terrain Modeling (RTM). RTM was used to identify features of the built environment that create settings of heightened overdose risk. Risk was estimated using Relative Risk Values (RRVs) and a composite score measuring Relative Risk Scores (RRS) across the county was produced for drugs, opioids, and stimulants, as well as by Black and White decedents. ResultsIn the model including all drugs, deaths were more likely to occur in close proximity to hotels/motels (RRV=39.65, SE=0.34, t-value=10.81 p<.001), foreclosures (RRV=4.42, SE=0.12, t-value = 12.80, p<.001), police departments (RRV=3.13, SE=0.24, t-score=4.86, p<.001), and restaurants (RRV=2.33, SE=0.12, t-value=7.16, p<.001). For Black decedents, deaths were more likely to occur near foreclosures (RRV=9.01, SE=0.18, t-value =11.92, p<.001), and places of worship (RRV= 2.51, SE=0.18, t-value = 11.92, p<.001). For White decedents, deaths were more likely to occur in close proximity to hotels/motels (RRV=38.97, SE=0.39, t-value=9.30, p<.001) foreclosures (RRV=2.57, SE=0.16, t-value =5.84, p<.001), restaurants (RRV=2.52, SE=0.17, t-value=5.33, p<.001) and, auto painting/repair shops (RRV=0.04, SE=0.18, t-value =3.39, p<.001). ConclusionThese findings suggest that places of worship, the hospitality industry, and housing authorities may be physical features of the environment that reflect social conditions that are conducive to overdose. The scaling up of harm reduction strategies could be enhanced by targeting places where features are co-located.

Региональная оценка трансформации землепользования на основе геопространственных данных (на примере Ярославской области)

The main directions of land use change in the Yaroslavl Region for 1992–2019 were identified based on a conjugate analysis of multi-temporal geospatial data on land cover from the European Space Agency, with the involvement of environmental and economic-geographical materials. On this basis, the main geoecological processes characterizing the features of the current development of natural and economic systems are identified. For analysis, 22 transitions of land cover classes were selected (occupying more than 1 % of the class area). They form six transformation directions: 1) increase of tree cover area; 2) reduction of tree cover area; 3) expansion of built-up areas; 4) waterlogging; 5) increase of flooded areas; 6) reduction of flooded areas. For each direction, the main processes determined by a complex of various factors are characterized. Analysis of data in GIS showed that for 1992–2019 land cover changes occurred over an area of 2.44 thous.km2, or 6.8 % of the region’s territory. As a result of the assessment of two main (dominant in area) directions (increase and reduction of tree cover), a conclusion was drawn about a net increase in the forested area in the Yaroslavl Region over 27 years by 970 km2. At the same time, the expansion of forest lands took place mainly in the zone of coniferous-broadleaved (mixed) forests, and the reduction of forests took place in the southern taiga subzone of taiga zone. Land use transformation is an important factor that should be taken into account for the development of regionally oriented recommendations for managing natural-anthropogenic landscapes and solving environmental problems.

A triadic model of job retention and turnover dynamics in the hospitality industry

PurposeThis study aims to examine key factors influencing hospitality employee turnover in a post-pandemic context, challenging conventional assumptions about the role of demographics and work-related factors in retention decisions.Design/methodology/approachThe research adopted a comprehensive framework using the capability approach and geospatial data analysis, integrating social vulnerability indexes with survey responses from 797 hospitality employees. This study analyzed turnover intentions across demographics, job roles and regions, focusing on Florida’s I-4 corridor.FindingsIndividual conversion factors like age and marital status were the strongest predictors of turnover, with older and married employees less likely to leave their jobs. In contrast, traditional variables like income, education and job type did not significantly influence turnover intentions. The pandemic blurred distinctions between job roles, highlighting personal circumstances, health risks and economic pressures as critical factors. Contrary to expectations, financial assistance did not significantly reduce turnover intentions. In addition, employees’ life circumstances, such as social vulnerability, influence labor relations and turnover more than work-related conditions.Research limitations/implicationsThis study enriches turnover literature by confirming that personal life circumstances, like age and marital status, are pivotal in understanding employee retention. It challenges conventional demographic and work-related predictors and urges future research to explore the interaction between personal and professional factors in the hospitality industry. The study’s agent-based framework deepens the understanding of how various factors shape employee decisions to stay or leave.Practical implicationsThe findings suggest that hospitality employers must develop more localized, employee-centric retention strategies, especially for younger employees. Tailored approaches considering regional and demographic differences, such as providing career development opportunities and flexible work conditions, could foster long-term loyalty. In rural areas, retention efforts should focus on improving job satisfaction and community support, while in urban areas, strategies prioritize career progression and flexibility.Social implicationsThe study’s findings have significant social implications, particularly in reshaping how employee retention strategies are developed in the hospitality industry. By emphasizing life circumstances over traditional work-related factors, the research highlights the importance of supporting employees’ resilience, especially for those facing social vulnerability. Employers may need to create more flexible and inclusive policies that account for personal, economic and health-related challenges. In addition, the findings suggest that financial aid alone is insufficient in reducing turnover, calling for a more holistic approach that integrates emotional and social support to foster a more stable and loyal workforce.Originality/valueThis study challenges traditional turnover models by shifting focus from work-related factors to life circumstances, particularly the resilience of older and married employees. It integrates three dimensions – personal (sociodemographics), social (support) and environmental (job type, pandemic challenges) – to examine their influence on employee agency. This triadic framework offers insights into how individual, social and contextual factors shape turnover decisions.

Geospatial tools and data for health service delivery: opportunities and challenges across the disaster management cycle.

As extreme weather events increase in frequency and intensity, the health system faces significant challenges, not only from shifting patterns of climate-sensitive diseases but also from disruptions to healthcare infrastructure, supply chains and the physical systems essential for delivering care. This necessitates the strategic use of geospatial tools to guide the delivery of healthcare services and make evidence-informed priorities, especially in contexts with scarce human and financial resources. In this article, we highlight several published papers that have been used throughout the phases of the disaster management cycle in relation to health service delivery. We complement the findings from these publications with a rapid scoping review to present the body of knowledge for using spatial methods for health service delivery in the context of disasters. The main aim of this article is to demonstrate the benefits and discuss the challenges associated with the use of geospatial methods throughout the disaster management cycle. Our scoping review identified 48 articles employing geospatial techniques in the disaster management cycle. Most of them focused on geospatial tools employed for preparedness, anticipatory action and mitigation, particularly for targeted health service delivery. We note that while geospatial data analytics are effectively deployed throughout the different phases of disaster management, important challenges remain, such as ensuring timely availability of geospatial data during disasters, developing standardized and structured data formats, securing pre-disaster data for disaster preparedness, addressing gaps in health incidence data, reducing underreporting of cases and overcoming limitations in spatial and temporal coverage and granularity. Overall, existing and novel geospatial methods can bridge specific evidence gaps in all phases of the disaster management cycle. Improvement and 'operationalization' of these methods can provide opportunities for more evidence-informed decision making in responding to health crises during climate change.

Exploring Topological Information Beyond Persistent Homology to Detect Geospatial Objects

Accurate detection of geospatial objects, particularly landslides, is a critical challenge in geospatial data analysis due to the complex nature of the data and the significant consequences of these events. This paper introduces an innovative topological knowledge-based (Topological KB) method that leverages the integration of topological, geometrical, and contextual information to enhance the precision of landslide detection. Topology, a fundamental branch of mathematics, explores the properties of space that are preserved under continuous transformations and focuses on the qualitative aspects of space, studying features like connectivity and exitance of loops/holes. We employed persistent homology (PH) to derive candidate polygons and applied three distinct strategies for landslide detection: without any filters, with geometrical and contextual filters, and a combination of topological with geometrical and contextual filters. Our method was rigorously tested across five different study areas. The experimental results revealed that geometrical and contextual filters significantly improved detection accuracy, with the highest F1 scores achieved when employing these filters on candidate polygons derived from PH. Contrary to our initial hypothesis, the addition of topological information to the detection process did not yield a notable increase in accuracy, suggesting that the initial topological features extracted through PH suffices for accurate landslide characterization. This study advances the field of geospatial object detection by demonstrating the effectiveness of combining geometrical and contextual information and provides a robust framework for accurately mapping landslide susceptibility.

Rekonstrukcja miejsca zdarzenia drogowego w oparciu o dane geoprzestrzenne Geoportalu Krajowego

The purpose of the article is to analyze the potential use of free spatial data available in the National Geoportal to support traffic accident reconstruction. The research focuses on the capabilities and limitations of using LiDAR and orthophotomaps data in the context of reconstructing traffic accident scenes. A comparative methodology was employed, involving the analysis of geospatial data in relation to traditional surveying techniques, exemplified by a real traffic accident analysis using the PC-Crash soft-ware. The main findings of the research indicate high precision of orthophotos and LiDAR data in reproducing the actual topography and elements of road infrastructure. The analysis conducted on a 3D model created from LiDAR data showed that modern measurement techniques allow a more accurate reconstruction of the accident's trajectory and assessment of road visibility conditions. Integrating these data with traditional measurement methods can significantly enhance the standards of forensic experts' work, contributing to an increased accuracy in determining accident scenarios and, more broadly, to a better understanding of accident causes.

HierGP: Hierarchical Grid Partitioning for Scalable Geospatial Data Analytics

Application domains such as environmental health science, climate science, and geosciences—where the relationship between humans and the environment is studied—are constantly evolving and require innovative approaches in geospatial data analysis. Recent technological advancements have led to the proliferation of high-granularity geospatial data, enabling such domains but posing major challenges in managing vast datasets that have high spatiotemporal similarities. We introduce the Hierarchical Grid Partitioning (HierGP) framework to address this issue. Unlike conventional discrete global grid systems, HierGP dynamically adapts to the data’s inherent characteristics. At the core of our framework is the Map Point Reduction (MPR) algorithm, designed to aggregate and then collapse data points based on user-defined similarity criteria. This effectively reduces data volume while preserving essential information. The reduction process is particularly effective in handling environmental data from extensive geographical regions. We structure the data into a multilevel hierarchy from which a reduced representative dataset can be extracted. We compare the performance of HierGP against several state-of-the-art geospatial indexing algorithms and demonstrate that HierGP outperforms the existing approaches in terms of runtime, memory footprint, and scalability. We illustrate the benefits of the HierGP approach using two representative applications: analysis of over 289 million location samples from a registry of participants and efficient extraction of environmental data from large polygons. While the application demonstration in this work has focused on environmental health, the methodology of the HierGP framework can be extended to explore diverse geospatial analytics domains.

Performance of the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) Model in the Northeast Region of Brazil

Study is presented on the potential for using the tools contained in the Google Earth Engine (GEE) geospatial data processing and analysis platform in climatological studies in the Northeast Region of Brazil (NEB), in particular the validation of estimates of total annual rainfall obtained by the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) model in seven homogeneous regions of the NEB. This data is made up of various sources of meteorological information, including information from satellites, numerical models and observations at weather stations in operational and research centers across the globe. The performance of the CHIRPS model was assessed using climatological data obtained from the National Meteorological Institute's (INMET) meteorological database, metrics and statistical tests. The results showed that the CHIRPS model has potential for use in six of the seven regions studied, with low errors and strong statistical correlation, and limitations in one of these regions. The conclusion is that this technological tool, which has a low operating cost and is free of charge, can be used to assist in climatological studies, planning and the development of strategies for mitigation, adaptation and coexistence with the dry periods observed in the sub-regions of the NEB, especially in regions that are in a situation of socio-economic vulnerability, as is the case in the semi-arid region of the NEB.