Geospatial Data Analysis Research Articles

Encoding and operation scheme for the rhombic triacontahedron aperture 4 hexagonal discrete global grid system

ABSTRACT Discrete Global Grid System (DGGS) is a hierarchical structure with seamless global coverage, supporting processing and analysis of heterogeneous geospatial data. Compared with the most commonly used icosahedron, the rhombic triacontahedron (RT) approximates Earth better and can improve the accuracy of data modeling and expression. However, current RT DGGS studies have adopted a single-resolution integer coordinate scheme within a single surface that cannot achieve multilevel grid analysis. To this end, three adjacent surfaces are combined and a three-axis integer coordinate system is established to reduce the number of times crossing the surface. Then, the principle for partitioning aperture 4 hexagonal grids and a quadtree hierarchical encoding scheme are proposed. Further, this study establishes a fast transformation between the code and three-axis coordinates, implements hierarchical and neighbor code operations, and designs a transformation between the code and geographic coordinates. Experimental results indicate that, compared with the icosahedral hexagonal DGGS, the proposed scheme has a significant efficiency advantage in the transformation between the code and geographic coordinates, while the neighbor code operation efficiency can reach 40.97 times that of HLQT and 4.35 times that of HHUT. The proposed scheme provides a more suitable framework for organizing and analyzing global geospatial data.

A Review Geospatial Artificial Intelligence (GEO-AI): Implementation Of Machine Learning On Urban Planning

Geospatial Artificial Intelligence (Geo-AI) is an interesting topic in its development and application in our lives. One of them is spatial planning, which contributes to the economic and social development of a region or country. Spatial data is the main thing in this research, which maximizes the effectiveness of land use spatial data on the area as upstream data and develops GIS-based urban planning applications to display the results of analysis and predictions of urban objects automatically. On the other hand, to maximize tourism revenue, the government plans urban areas for both spatial and land use and makes a lot of spatial data based on geographical and environmental conditions. This study will analyze the benefits of Geo-AI in urban planning and the tourism sector. The method used in this study is the Systematic Literature Review (SLR), where the search for the required articles comes from electronic databases obtained using the NVivo software with article sources from Publish or Perish. This study discusses the main steps for the analysis of geospatial data that have been successful in the main areas, namely the development of applications and models, including visualization. By treating issues in geospatial artificial intelligence, the overall aim of the research is to improve the quality of life for Indonesia's growing urban population. The results of the study with the systematic literature review of Geo-AI found a gap in the research implementation of the machine learning model used where five models were compared and relevant to the geospatial dataset displayed in the form of a literature review matrix and visually included in the relevant keywords in the bibliometric analysis. Interdisciplinary results are developing and are urgently needed by both government and private stakeholders in the development of smart cities to prepare spatial planning in urban areas and strategies in optimizing technology in the field of spatial planning in implementing systems based on Geo-AI.

Geospatial data analytics for equitable forest resource management

Geospatial data analytics for equitable forest resource management

A Python package for the local multiscale analysis of spatial point processes (LomPy)

The consideration of multiscale characteristics has emerged as a popular component of geospatial data analysis and modeling. However, the practical implementation of such analysis tasks involves time-consuming and computationally intensive processes that require the integration of knowledge and methods from different disciplines (e.g., quantitative geography, signal processing, and natural sciences) and in which large amounts of data have to be processed. Yet, to date, there is few open-source software that enables an efficient and transparent computational workflow. This paper introduces a Python package for the local multiscale analysis of spatial point processes (LomPy). LomPy is specifically designed for processing and analyzing data that is either irregularly spaced or has large data holes over the spatial territory: a common and methodologically challenging property of geoprocessing operations. The first main function of the package computes the multiresolution quantities, while the second applies them to the extraction of fractal and multifractal features at arbitrarily “local” spatial scales. The third function extends the univariate analysis option to multivariate settings. Powerful tools for regression modeling, density estimation, and visualization are also provided. It should be emphasized that the package is efficient on both vector and raster data structures, ensuring a wide range of applicability in urban data science and beyond.

Three-dimensional stability analysis of two-layer slope based on GIS under rainfall conditions

ABSTRACT Geographical Information System (GIS), as a computer-based tool, allows easy data processing, and complex geospatial data management and analysis, making it a suitable environment for 3D slope stability analysis. The study proposed an integral scheme for 3D stability analysis of a two-layer slope under rainfall conditions based on GIS. To construct the 3D model of the slope and the sliding surface in GIS, a method was proposed in which a grid is divided into four sections to improve the accuracy of calculation, and the extension of the Hovland model and the Janbu model in unsaturated soil was discussed. The transformation equations between the global coordinates and the ellipsoid local coordinates were derived to locate the critical sliding surface, and how to apply constraints of parameters to improve search efficiency was put forward. An infiltration model for a two-layer slope was built so that the changes in water content in slope at the both stages of rainwater infiltration and redistribution can be determined, and the model was integrated into the GIS-based scheme to assess the impact of rainfall on slope stability. Finally, two case studies were implemented to demonstrate the validity and correctness of the proposed scheme.

Geographic information system and information visualization capacity building: Successful polio eradication and current and future challenges in the COVID-19 era for the World Health Organization's African region.

Despite a half-century-long global eradication effort, polio continues to have a devastating impact on individuals and communities worldwide, especially in low-income countries affected by conflict or geographic barriers to immunization programs. In response, the World Health Organization (WHO) Global Polio Eradication Initiative (GPEI) employs disease surveillance and vaccination campaigns coordinated through the WHO Regional Office for Africa (AFRO) Geographic Information System (GIS) Centre. Established in 2017, the AFRO GIS Centre played a key role in the eradication of wild-type polioviruses (WPVs) in 2020, but the COVID-19 pandemic, emergence of circulating vaccine-derived polioviruses, and transmission of WPV1 from Central Asia have led to a resurgence of polio in Sub-Saharan Africa. The AFRO GIS comprises a set of mobile device or cloud-based tools for geospatial data collection, analysis, and visualization. Using tools such as Auto-Visual Acute Flaccid Paralysis Detection and Reporting, electronic surveillance, and Integrated Supportive Supervision, GIS personnel collect polio case numbers and locations, track field worker activities, follow the movements of nomadic populations vulnerable to polio and other diseases, and determine needs for further healthcare deployments. The system is location specific and operates in real time, enabling the AFRO GIS to promptly target its responses to polio, COVID-19, Ebola virus disease, and other public health crises and natural disasters. The present review describes the components of the AFRO GIS and how the AFRO GIS Centre coordinated on-the-ground polio eradication efforts to help secure Africa's certification as WPV free. It also examines current and prospective challenges regarding other disease outbreaks in the COVID-19 era and how the AFRO GIS Centre is addressing these ongoing public health needs.

Building the Geodesic Network for the City of Gramsh through GNSS Surveys

This study presents the methodology and results of establishing a geodesic network for the urban area of Gramsh, Albania, utilizing Global Navigation Satellite System (GNSS) surveys. The development of an accurate geodetic framework is essential for various applications including urban planning, infrastructure development, and cadastral mapping. The research focuses on deploying GNSS receivers strategically across Gramsh to collect precise positioning data, which is subsequently processed using differential correction techniques to achieve centimeter-level accuracy. Ground control points are established throughout the city to facilitate georeferencing and validation of the network. Additionally, quality control measures such as redundancy checks and error analysis are implemented to ensure the reliability of the geodesic network. The resulting network provides a reliable spatial reference system for the city of Gramsh, enabling efficient and accurate geospatial data collection, analysis, and decision-making processes for urban development projects. This study demonstrates the effectiveness of GNSS surveys in establishing geodetic infrastructure in urban environments and highlights their significance in supporting sustainable urban growth and management.

Machine Vision for Efficient Electric Vehicle Charging Station Deployment

This research examines the optimization of the deployment of electric vehicle (EV) charging stations by using machine vision technology, which involves analyzing real-time data and geographical information. Geospatial data analysis reveals prospective sites for charging stations by considering population density and accessibility to roads, hence identifying regions with increased demand for electric vehicle charging. The assessment of electric vehicle (EV) traffic patterns highlights the ever-changing charging requirements at various times and places, underscoring the need of flexible deployment techniques. Furthermore, evaluating the costs of implementing the deployment and the capabilities of charging, it becomes apparent that there are compromises to be made between the initial expenditures of installation, the amount of power generated, and the quantity of charging stations. These trade-offs are essential for optimizing resources. The usage study of charging stations using machine vision reveals variations in the number of available charging points at different stations and the need for adaptive resource distribution timestamps, techniques. The examination of percentage change reveals notable fluctuations in population density, installation costs, and the availability of charging points. This information is crucial for making well-informed decisions about the deployment of charging infrastructure. Combining machine vision insights with geographical and traffic analyses presents a promising method to create data-driven strategies for the placement of EV charging stations. This approach addresses the changing needs of electric mobility and provides guidance to stakeholders for efficient and flexible charging solutions.

Geospatial Data Analysis of School Distribution in Baghdad City

Education and lifelong learning are necessary components of daily city life for urban communities to encourage sustainable and positive communities. The study attempts to analyze the actual school distribution patterns and densities in Baghdad, the Iraqi capital. The significance of this study is that it is associated with one of the essential aspects of humanity: the improvement and affluence of schooling; it impacts school attendance limitations and educational evolution. The education process has been inextricably tied to students' timely and orderly entrance to their schools. Hence the decision maker and planner are concerned by this. The statistics examined elementary and high schools, and the investigated data are related to the Ministry of Education that was available in 2004. Regarding spatial analysis and results assessment, this work employs Microsoft Excel techniques, ArcGIS, C# simulator, and the buffer methodology. It has depicted the school densities and assessed the distances between them using spatial analytic techniques. The study reveals that the school distribution is uneven, and there is a disparity among neighbors in the spatial distribution. Also, the in-between distances of observed schools formulate four significant patterns. At the same time, this study offers important information about the spatial distribution of schools as a significant influencing indicator. In the future, it will be beneficial to investigate the student's geographical accessibility to their residence and the transportation to/from these schools.

Slendr: a framework for spatio-temporal population genomic simulations on geographic landscapes.

One of the goals of population genetics is to understand how evolutionary forces shape patterns of genetic variation over time. However, because populations evolve across both time and space, most evolutionary processes also have an important spatial component, acting through phenomena such as isolation by distance, local mate choice, or uneven distribution of resources. This spatial dimension is often neglected, partly due to the lack of tools specifically designed for building and evaluating complex spatio-temporal population genetic models. To address this methodological gap, we present a new framework for simulating spatially-explicit genomic data, implemented in a new R package called slendr (www.slendr.net), which leverages a SLiM simulation back-end script bundled with the package. With this framework, the users can programmatically and visually encode spatial population ranges and their temporal dynamics (i.e., population displacements, expansions, and contractions) either on real Earth landscapes or on abstract custom maps, and schedule splits and gene-flow events between populations using a straightforward declarative language. Additionally, slendr can simulate data from traditional, non-spatial models, either with SLiM or using an alternative built-in coalescent msprime back end. Together with its R-idiomatic interface to the tskit library for tree-sequence processing and analysis, slendr opens up the possibility of performing efficient, reproducible simulations of spatio-temporal genomic data entirely within the R environment, leveraging its wealth of libraries for geospatial data analysis, statistics, and visualization. Here, we present the design of the slendr R package and demonstrate its features on several practical example workflows.

Statistical Modeling of Spatially Stratified Heterogeneous Data

Spatial statistics is an important methodology for geospatial data analysis. It has evolved to handle spatially autocorrelated data and spatially (locally) heterogeneous data, which aim to capture the first and second laws of geography, respectively. Examples of spatially stratified heterogeneity (SSH) include climatic zones and land-use types. Methods for such data are relatively underdeveloped compared to the first two properties. The presence of SSH is evidence that nature is lawful and structured rather than purely random. This induces another “layer” of causality underlying variations observed in geographical data. In this article, we go beyond traditional cluster-based approaches and propose a unified approach for SSH in which we provide an equation for SSH, display how SSH is a source of bias in spatial sampling and confounding in spatial modeling, detect nonlinear stochastic causality inherited in SSH distribution, quantify general interaction identified by overlaying two SSH distributions, perform spatial prediction based on SSH, develop a new measure for spatial goodness of fit, and enhance global modeling by integrating them with an SSH q statistic. The research advances statistical theory and methods for dealing with SSH data, thereby offering a new toolbox for spatial data analysis.

Advancing digital earth modeling: Hexagonal multi-structural elements in icosahedral DGGS for enhanced geospatial data processing

Discrete Global Grid Systems (DGGSs) employ uniform cells for hierarchical Earth surface representation. However, prevalent software systems stemming from DGGS only focus on grid centers and lack the modeling of vertices and edges, which hindering efficient geospatial data computation and analysis. Consequently, we propose a novel mathematical model for hexagonal centers, vertices and edges of an icosahedral DGGS and provide corresponding hierarchical indexing methods and conversion rules with respect to integer coordinates. The proposed method was incorporated into the open-source software DGGRID, thereby enhancing its capabilities by integrating data fusion into the computation framework and expanding its application scope. The results show that the introduction of multi-structural elements could lead to higher-precision modeling of raster and vector data.

An efficient visual exploration approach of geospatial vector big data on the web map

The visual exploration of geospatial vector data has become an increasingly important part of the management and analysis of geospatial vector big data (GVBD). With the rapid growth of data scale, it is difficult to realize efficient visual exploration of GVBD by current visualization technologies even if parallel distributed computing technology is adopted. To fill the gap, this paper proposes a visual exploration approach of GVBD on the web map. In this approach, we propose the display-driven computing model and combine the traditional data-driven computing method to design an adaptive real-time visualization algorithm. At the same time, we design a pixel-quad-R tree spatial index structure. Finally, we realize the multilevel real-time interactive visual exploration of GVBD in a single machine by constructing the index offline to support the online computation for visualization, and all the visualization results can be calculated in real-time without the external cache occupation. The experimental results show that the approach outperforms current mainstream visualization methods and obtains the visualization results at any zoom level within 0.5 s, which can be well applied to multilevel real-time interactive visual exploration of the billion-scale GVBD.

ODRA2D – TWO-DIMENSIONAL CASCADE FLOOD MODELLING FOR THE ODRA RIVER – DEVELOPMENT AND APPLICATIONS

Aim of the study: Development of new hydrodynamic numerical tools for simulations of the flow in the Odra River valley for the flood hazard assessment and management Material and methods: Use of the MIKE21 software for models building. Geospatial (ArcGis) topographical data processing and analysis for model inputs and outputs. Extensive use of remote sensing data (digital terrain model and orthophoto maps) combined with traditional data sources. Results and conclusions: A cascade of twenty five MIKE21 models was developed to cover a nearly 600 km long section of the Odra River. The models were used in different studies, including: the assessment of flood hazards with regards to various scenarios, the assessment of flooding due to ice jamming and breaks in embankments, the evaluation of the effectiveness of flood mitigating works within the complex Hydrosystem of the city of Wrocław, the verification of historic flood data and stage-discharge relations, and also the verification of design discharges.

From Geospatial to Spatial -Omics

When Los Angeles is mentioned, cycling is usually not the first thing that comes to mind. However, during my past 10 years in LA studying molecular biology and bioinformatics, my bike trips through the geographical space of LA have inspired many ideas in my research in spatial data analysis in bioinformatics. I have written software to bring decades of research in geospatial data analysis to spatial -omics, as my trips make me ponder on spatial phenomena in general.

Staging Molière in Communist Romania. A Quantitative Diorama

"This paper accompanies a dataset containing information regarding the stagings of Molière’s plays during the 42 years of the communist regime in Romania, from 1948 to 1989. It offers a quantitative overlook of 120 productions, through statistics and visualizations, in an attempt to make sense of the data and find trends and directions otherwise difficult to detect. The data was collected from a public repertory database, supplemented by information from the theaters’ archives and reviews from period magazines and newspapers. We have strived to let the data ‘speak,’ minimizing speculative commentary to where we felt it was needed to add colour or emphasize certain possible patterns. For network graphing and analysis, we used Gephi 0.9 software, and for the analysis and visualization of geospatial data, QGIS 3. Data processing and graph generation were carried out using Microsoft Excel. Keywords: Molière, communist Romania, data, quantitative survey, Romanian theatre system. "

Special issue: Geospatial data analysis through artificial intelligence: editorial column

Special issue: Geospatial data analysis through artificial intelligence: editorial column

Comparison of Three Machine Learning Algorithms Using Google Earth Engine for Land Use Land Cover Classification

Google Earth Engine (GEE) is presently the most innovative international open-source platform for the advanced-level analysis of geospatial big data. In this study, we used three machine learning algorithms to apply this cloud platform for Land Use Land Cover (LULC) research in the Mardan, Pakistan. The machine learning algorithm in GEE is the most advanced technique to generate reliable and informative LULC maps from various satellite data to present reliable results. The primary goal of the present study is to compare the performance of various machine learning models (i.e., classification and regression trees [CART], support vector machine [SVM], and random forest [RF]) in GEE for the reliable four classes LULC maps using the Sentinel-2 imageries of 2022. In the current study, three satellite indices like the Normalized Difference Vegetation Index, Modified Normalized Difference Water Index, and Normalized Difference Built Index were applied to detect the features (i.e., vegetation, built, barren land, and water bodies in the study area). The performance of all three models was evaluated by validation and accuracy assessments. The Kappa coefficients of CART, SVM, and RF for Sentinel-2 images were 94%, 95%, and 97%, while the average overall accuracy is 96.25%, 97%, and 98.68%, respectively. The present study illustates that in this classification and comparison, RF performed better than SVM and CART. The current research study revealed that GEE has speedily processed the satellite imageries to develop the four classes of reliable LULC maps of the study area with the best accuracy results and deliver excellent support for further analysis.

The impact of rice-crayfish field on socio-ecological system in traditional farming areas: Implications for sustainable agricultural landscape transformation

Agricultural landscape transformation has profound impacts on regional sustainable development. Understanding the relationship between agricultural landscape transformation and socio-ecological system is fundamental to both social prosperity and ecological environment. Rice-crayfish field (RCF) as a unique agricultural model is widely practiced in traditional farming areas and causes significant landscape transformation. However, the RCF remains less understood and its impacts on socio-ecological systems need to be comprehensively investigated. China has a developed crayfish industry with over 90% of the world's crayfish production, which provides an interesting lens to examine the RCF-led agricultural landscape transformation. This research proposes a novel research framework to analyse the spatial dynamic of RCF and its complex impact on socio-ecological system. Using remote sensing and geospatial data analysis, this study explores the spatiotemporal heterogeneity of RCF-led landscape transformation and socio-ecological system. In addition, the impacts of RCF on socio-ecological system are examined by the Pearson correlation analysis, root mean square error and regression model. The results reveal that RCF has dramatically expanded and transformed from paddy fields. The socio-ecological system presents obvious spatial agglomeration characteristics. RCF has the potential to increase farmers' income and alleviate rural poverty, but it is not conducive to grain production and water consumption. The RCF-led agricultural landscape transformation could be a double-edged sword to socio-ecological system. To balance social and ecological systems, it is necessary to manage and design the rational development of RCF to support sustainable agricultural landscape transformation.

Mapping Feasibility for Wood Supply: A High-Resolution Geospatial Approach to Enhance Sustainable Forest Management in Galicia (NW Spain)

The forest value chain is key to the European transition to a climate-neutral economy. Sustainable forest management is essential for this task. To plan sustainable forest management, it is essential to track forest resources in relation to their feasibility for wood supply. This means considering the constraints that may limit the incorporation of these resources into the forest value chain. Maps adapted to specific regional constraints and to the characteristics of specific forests are essential for performing sustainable forest management at a local scale. This study presents a methodology for the integrated analysis of geospatial data focused on classifying the land and the forest resources of a region according to their feasibility for wood supply. It produces maps of the feasibility for wood supply in an area and of the existing forest resources at a 10 m spatial resolution. This was done by integrating information about the legal and technical constraints present in the area according to decision rules. The land was classified into three classes: favorable, intermediate or unfavorable. Additionally, updated forest-oriented land cover maps were produced to analyze the feasibility for wood supply of the forest resources present in the region. It was found that 42% of the Eucalyptus spp., 48% of the conifers and 30% of the broadleaves in the study area were located in favorable areas. These maps would help in the quest for more sustainable forest management in the region and aid in boosting the competitiveness of the regional forest value chain.