GIS Components Research Articles

Evaluation, Spatial Analysis and Prediction of Ecological Vulnerability in Chongqing Municipality Based on GIS and Principal Component Analysis (PCA)

Evaluation, Spatial Analysis and Prediction of Ecological Vulnerability in Chongqing Municipality Based on GIS and Principal Component Analysis (PCA)

Assessment of groundwater quality in arid regions utilizing principal component analysis, GIS, and machine learning techniques

Assessing water quality in arid regions is vital due to scarce resources, impacting health and sustainable management.This study examines groundwater quality in Assuit Governorate, Egypt, using Principal Component Analysis, GIS, and Machine Learning Techniques. Data from 217 wells across 12 parameters were analyzed, including TDS, EC, Cl−, Fe++, Ca++, Mg++, Na+, SO4−−, Mn++, HCO3−, K+, and pH. The Water Quality Index (WQI) was calculated, and ArcGIS mapped its spatial distribution. Machine learning algorithms, including Ridge Regression, XGBoost, Decision Tree, Random Forest, and K-Nearest Neighbors, were used for predictive analysis. Higher concentrations of Na, K, Ca, Mg, Mn, and Fe were correlated with industrial and densely populated areas. Most samples exhibited excellent or good quality, with a small percentage unsuitable for consumption. Ridge Regression showed the lowest MAPE rates (0.22 % training, 0.26 % in testing). This research highlights the importance of advanced machine learning for sustainable groundwater management in arid regions. Thus, our results could provide valuable assistance to both national and local authorities involved in water management decisions, particularly for water resource managers and decision-makers. This information can aid in the development of regulations aimed at safeguarding and sustainably managing groundwater resources, which are essential for the overall prosperity of the country.

Web-Gis and Artificial Intelligence A Panacean for Agriculture Advancement in Developing Countries - A Review

Agricultural contributions to the economies of developing countries, including Nigeria, have been declining. This paper proposes leveraging Web-GIS and artificial intelligence (AI) to rejuvenate agriculture in these regions. It aims to dissect the components of Web GIS, examine spatial data capture methods, delve into AI for image processing, and develop revenue-generating strategies to boost Nigeria’s agricultural sector. Despite substantial capital investment, agricultural outputs remain below expectations, primarily due to informational gaps regarding land use, planting techniques, and storage. The bureaucratic management of public agricultural sectors further delays disseminating research findings to farmers. Adopting Web-GIS and AI offers a modern solution to these challenges, facilitating real-time analysis and communication of vital information related to soil conditions, farmer challenges, and broader socio-economic and environmental concerns. This approach promises to elevate agricultural practices by integrating technologies like the Internet of Things, GIS, and extensive data-sharing capabilities across the web. The paper emphasizes the potential of ubiquitous smartphone use and widespread internet connectivity to transform agricultural information dissemination. It advocates for leveraging the digital infrastructure of smart cities to enhance access to agricultural data, ultimately improving farming practices across Nigeria. The fusion of Web-GIS and AI with existing telecommunications networks presents a viable path forward, enabling farmers to make informed decisions, improve productivity, and contribute more significantly to the national economy.

GEODESY IN SOLVING PROBLEMS OF SUSTAINABLE CITY DEVELOPMENT: ANALYSIS OF URBAN TERRITORY PLANNING

The article focuses on the creation and implementation of a geodetic planning system aimed at ensuring the sustainable development of urban areas. The main emphasis is on the use of open and accessible software, integration of full-fledged GIS and DBMS components. The process of determining the most suitable GIS platform for GSP included a detailed analysis of various GIS systems taking into account their functionality, which allowed to choose QGIS as the optimal solution due to its high functionality in the field of geodata management, support for various data formats, user interface and dynamic development. The structure of the GSP is based on two main platforms: the QGIS GIS system for cartographic data presentation and analysis, and the Microsoft Access DBMS for performing all data operations. Interaction between these platforms is provided through the eVis module built into QGIS, which allows for efficient planning of urban area surveys and land use management. The main tasks solved with the help of GSP are the planning of primary surveys to identify target plantations, detailed taxation of plantations with the creation of samples according to specified parameters, obtaining point maps of the distribution of various types of vegetation, planning sample surveys with specific geospatial conditions, as well as geo-information and tax analysis in in solving the problems of sustainable development of cities. The implementation of GSP provides significant advantages for planning the sustainable development of urban areas, allowing more efficient use of land resources, optimizing urban management processes and intensifying scientific research in this field. The development and integration of modern digital technologies in the field of urban planning play a key role in achieving the goals of sustainable development.

An open GIS based 3D simulation software to predict cooling tower drift diffusion

This paper developed XJCT-3D, a simulation software for cooling tower wet plume dispersion. By coupling it with the Open GIS component Dotspatial, we have achieved geospatial visual representation of the calculation results, which has solved the problems of low calculation efficiency and insufficient visual representation of the traditional CFD software in the calculation of cooling tower wet plume dispersion. In order to verify the validity of the XJCT-3D software simulation results, we have conducted tracer experimental data from the ChalkPoint power plant. XJCT-3D accurately models wet plume deposition during cooling tower operation. From the XJCT-3D calculation results, we have observed that the maximum value of the cooling tower thermal plume wet deposition occurs near 610 m with a maximum value of 6.9E−07 kg/m2 s. This finding suggests that the cooling tower emissions carry a significant load of particles or droplets that have settled on surfaces at this particular altitude. It provides insights into potential environmental and human health impacts and helps in identifying and assessing areas at relatively higher risk of deposition, such as nearby ecosystems, farmland, or urban areas. This information can contribute to the development of effective mitigation strategies and the implementation of appropriate measures to minimize the impact of cooling tower emissions.

A cross-scale modeling framework for simulating typhoon-induced compound floods and assessing the emergency response in urban regions

The cities within the Pearl River Delta (PRD), known as the Greater Bay Area (GBA), are flood-prone areas that faced frequent threats from typhoon-induced storm surges and rainfall in recent decades. To assess the potential consequences of a typical typhoon event, fine-scale simulations of inundation induced by both pluvial and fluvial flooding (known as compound flooding) are crucial for policymakers and concerned stakeholders to develop proper measures. In this study, we develop a cross-scale framework for modeling city inundation arising from typhoon-induced surges and torrential rainfall–runoff by coupling a three-dimensional ocean model (FVCOM) and a one-dimensional and two-dimensional coupling river analysis system (HEC-RAS). The ocean model is applied to capture the mesoscale behavior of surge and tide propagation from the deep sea to shallow offshore areas near the PRD. The river analysis system can simulate storm wave propagation along rivers, overtopping banks on floodplains and rainfall–runoff-induced flooding in urban regions at gradually refined scales. Second, the framework integrates GIS components (network analysis) with these hydraulic models. Through this framework, the urban emergency service coverage can be assessed under various potential flood scenarios during typhoon landfalls. Super Typhoon Mangkhut in 2018, the most devastating typhoon in the last decade in the PRD, is used as a case study in simulating and studying the flooding process. The simulated results show that in this typhoon event, pluvial and fluvial flooding play different roles in hindering the accessibility of emergency resources. Moreover, the interaction between pluvial and fluvial flooding significantly affects some urban regions.

BRIDGING THE GAP: AN OPEN-SOURCE GIS+BIM SYSTEM FOR ARCHAEOLOGICAL DATA. THE CASE STUDY OF ALTINUM, ITALY

Abstract. GIS tools in archaeology have become fundamental for professionals and researchers around the world. In recent years, the “3D revolution” has drastically changed archaeological practices and methods, including the use of GIS. This paper presents the results of a project aimed at building an integrated system for the management and study of archaeological data in 2D and 3D, connecting GIS to BIM. Building Information Modeling (BIM) is an innovative technology developed for architectural design and planning, and later adapted for cultural heritage and historic buildings (HBIM). More recently, it has been tested on archaeological sites. Some attempts have been made to connect landscape scale GIS projects with BIM models of individual buildings. However, these have not been applied specifically to archaeology thus far. Through the use of open-source software, a seamless workflow from data collection in GIS to 3D visualization and modeling in a native OpenBIM environment has been created. The paper illustrates this methodology with a case study about Altinum, a Roman city in northeastern Italy near Venice. The GIS component of the project manages the archaeological map of the city, while the OpenBIM application allows the virtual reconstruction of the sites. For the latter, the case of the ancient city-gate of Altinum is considered and explained.

Pelatihan dan Pendampingan Informasi Geospasial Berbasis Sistem Informasi Geografi (SIG) dalam Meningkatkan Kompetensi Guru Geografi

The rapid development of GIS geospatial information technology demands proficiency from geography teachers, but teachers tend to have minimal knowledge of the technology, and even the impression that teaching and learning activities are not being used optimally. I have. GIS geospatial information technology is very useful in describing environmental problems, metropolitan areas, floods, and various activities to establish trends, determine best locations, and identify patterns and models. The purpose of this activity is the enhancement and application of geographic information technology based on geographic information systems for geography teachers in the form of training and instruction, including data entry, management, analysis and presentation. In carrying out this activity, the participants, while receiving a strengthening of GIS concepts and GIS components, created maps of slopes, elevations and landslides in operational demonstration activities, then classified and scored them, and evaluated them. Click Overlay (Overlay) to create a new map with the new classification. The results of this activity show better control as well as higher enthusiasm and responsiveness during the training activity. The whole operation went smoothly. In addition, GIS education can enhance the effectiveness of geography education through application in schools.

LISROP: A New Platform for the Spatial Analysis of Massive Archaeological and Historical Information (a Work in Progress)

LISROP (Land of Israel Study and Research Online Platform) is an online, bilingual, English-Hebrew, integrative platform (under construction) aiming to allow scholars and interested non-academics to review a vast amount of archaeological and historical data from the land of Israel, explore it and dissect in various ways, and then analyze it using sophisticated GIS tools, some of which were specifically developed for the platform. The platform can be used for various types of studies and can be expanded thematically and spatially beyond its current limits by incorporating additional databases and applications and providing information on nature, culture, and heritage, furthering study and research into these areas. The paper briefly presents the project’s background, history, development, and current aims. It then describes the platform and its components, including the geographical foundations on which the data is studied, the archaeological and historical data it incorporates, and the various GIS components it includes. The paper then outlines the platform’s potential, capacity to advance research on several levels, and expected relevance for non-academics. Toward the end, the paper briefly describes some of the major challenges we encountered in our work and potential avenues for expanding the platform.

Social vulnerability assessment to flood hazard in Souss basin, Morocco

The concept of vulnerability is now embedded into multiple disaster-related disciplinary theories and is currently incorporated in various ways concerning disaster risk management. The Souss basin has witnessed many intensive extreme flooding events during the last two decades because it is located in a semi-arid to arid climate. The area is quite vulnerable, and negative impacts have gotten increasingly alarming. The susceptibility to this risk is accentuated not only by the shorter intense rainfall but also by the rapid population growth, uncontrolled land use, anthropogenic actions, and recent environmental fluctuations. These factors affect both the potential hazards and general vulnerability. In this study and given the current context, we developed a methodology to assess social vulnerability towards flooding in the area based on 26 socioeconomic indicators from the database of the Last general census of population and housing (GPHC) in 2014. These indicators aim to describe the spatial variability of the Social Vulnerability Index (SoVI) using principal component analysis (CPA) and GIS. Thus, the SoVI, with values ranging from 0 to 4, is a powerful tool for better quantifying social vulnerability and highlighting the potential impact on urban and rural communities. The result shows that few urban areas have a low average vulnerability index (1.76–2.33) while most rural areas have a higher average of SoVI ranging from 3.56 to 4 in this area. Therefore, it will serve as a basis for evacuation plans to anticipate and prevent flooding risks. This statement indicates that social-economic indicators enhancement is advisable in this area.

Analysis of ecological security evaluation and zoning threshold determination of typical energy industrial area based on PSR model

Ecological security is the main way to measure the effects of urbanization and industrialization in developing regions on the ecological environment. As an important coal chemical industry base in China, Taiyuan is located in the Loess Plateau, resulting in great pressure on the ecological environment. Based on the pressure-state-response (PSR) model, this study selected 15 evaluation indicators to evaluate the ecological security of 10 counties(districts/city) in Taiyuan City. Using the spatial principal component analysis (SPCA) method and GIS technology to simulate and analyze the ecological security distribution of this area, for the first time, the typical method and average method were used to divide the threshold of the ecological security level to achieve a more reasonable ecological security classification method. The results showed that from 2000 to 2016, the ecological security level of Taiyuan city and its surrounding areas was relatively low, mainly concentrated in Xinghualing, Yingze and Jiancaoping districts, while the ecological security level of Loufan county, Qingxu County, Gujiao city and Yangqu County was relatively high. Among these, energy mining and urbanization have had a relatively obvious impact on ecological security. It could provide more detailed ideas for the protection of ecological security in different areas and enrich the research on the ecological security of important coal chemical bases.

Assessment of Surface Water Quality in the Hau Giang Province Using Geographical Information System and Statistical Approaches

The study aims to assess the surface water quality in Hau Giang province in 2021 at 44 locations (with a frequency of 5 times per year) using Principal Component Analysis (PCA), Cluster Analysis (CA) and GIS. Surface water quality is compared with the national technical regulation on surface water quality (QCVN 08-MT:2015/BTNMT, column A1) and Water Quality Index (WQI). The results showed that the surface water quality parameters of total suspended solids, organic matters, nutrients, coliform, and Fe far exceeded the allowable limits while the parameter of Cl-, color, and CN- were within the allowable limits of QCVN 08-MT:2015/BTNMT, column A1. The values of WQI showed that the water quality of Hau Giang province ranged from poor to excellent. The water quality at Vam Cai Dau and Hau River areas could only be exploited for water supply, but appropriate treatment is needed. CA divided the monitoring months into three distinct clusters and reduced the sampling sites from 44 to 33 locations, reducing 25% of monitoring cost per year. PCA revealed three main factors which could explain 69.0% of the variation in water quality. The water pollution sources were mainly industrial and agricultural discharges, domestic and urban activities, transportation activities, salinity, hydrological conditions and water runoff. The current findings provide useful information which support local environmental managers and water supply companies for safe and sustainable.

Mapping the spatial dimension of food insecurity using GIS-based indicators: A case of Western Kenya

Food insecurity elimination is a major focus of the Sustainable Development Goals and addresses one of the most pressing needs in developing countries. With the increasing incidence of food insecurity, poverty, and inequalities, there is a need for realignment of agriculture that aims to empower especially the rural poor smallholders by increasing productivity to improving food security conditions. Repositioning the agricultural sector should avoid general statements about production improvement, instead, it should tailor to location-specific recommendations that fully acknowledge the local spatial diversity of the natural resource base that largely determines production potentials under current low input agriculture. This paper aims to deconstruct the complex and multidimensional aspect of food insecurity and provides policymakers with an approach for mapping the spatial dimension of food insecurity. Using a set of GIS-based indicators, and a small-area approach, we combine Principal Component Analysis and GIS spatial analysis to construct one composite index and four individual indices based on the four dimensions of food security (access, availability, stability, and utilization) to map the spatial dimension of food insecurity in Vihiga County, Kenya. Data were collected by the use of a geocoded household survey questionnaire. The results reveal the existence of a clear and profound spatial disparity of food insecurity. Mapping food insecurity using individual dimension indices provides a more detailed picture of food insecurity as compared to the single composite index. Spatially disaggregated data, a small area approach, and GIS-based indicators prove valuable for mapping local-level causative factors of household food insecurity. Effective policy approaches to combat food insecurity inequalities should integrate spatially targeted interventions for each dimension of food insecurity.

Green City Landscape Design Based on GIS System

In order to solve the problem of green urban garden landscape design, a green urban garden landscape design method based on GIS system is proposed. This method introduces component GIS and compares three AO-based GIS secondary development modes. Based on the developed solution, comprehensively considering the index evaluation of urban landscaping and its influencing factors, the functional structure of the GIS-based landscaping management information system is designed, and the system requirements are analyzed in detail. It solves the problem that the cost of traditional green urban garden landscape design is too high, and the experiment proves that improving the landscape environment of urban public space has practical value for the research of urban landscape system. Combined with practical application, it is found that the intervention of rainwater landscape can effectively improve the aesthetics and artistry of urban rainwater public facilities. At the same time, the development of GIS and its related technologies (such as database technology and network technology) has also promoted the construction and development of urban landscaping system, which has a great effect on the beautification of the environment, which verifies the validity of the experiment.

Application of 3D Virtual Reality Sensor in Tourist Scenic Navigation System

In order to solve the application of 3D virtual reality technology in the practical development of scenic spot navigation system, this paper proposes a scenic spot navigation system based on multimedia technology and component geographic information system (ComGIS). The system takes Visual Basic 6.0, MapObjectst2.4, and GeoLOD 4.0 as the development platform and develops the tourism navigation system based on component GIS and multimedia technology, which manages nonspatial data with access 2003. The results are as follows: the path length designed by the system in this paper and the three compared navigation apps is 1.56 km, respectively. Although the path designed in this paper is not the shortest, the number of scenic spots passed by this path is up to 6; The accuracy of the navigation system designed in this paper is 97.2%, about 5% higher than that of similar navigation APPs; 55.2% of users felt that the navigation system designed in this paper was excellent. The results show that the system can basically meet the needs of tourists, hoping to promote the information construction of tourism.

Wind Energy Development Site Selection Using an Integrated Fuzzy ANP-TOPSIS Decision Model

The identification of appropriate locations for wind energy development is a complex problem that involves several factors, ranging from technical to socio-economic and environmental aspects. Wind energy site selection is generally associated with high degrees of uncertainty due to the long planning, design, construction, and operational timescales. Thus, there is a crucial need to develop efficient methods that are capable of capturing uncertainties in subjective assessments provided by different stakeholders with diverse views. This paper proposes a novel multi-criteria decision model integrating the fuzzy analytic network process (FANP) and the fuzzy technique for order performance by similarity to ideal solution (TOPSIS) to evaluate and prioritize the potential sites for wind power development. Four major criteria, namely economic, social, technical, and geographical, with nine sub-criteria are identified based on consultation with wind farm investors, regulatory bodies, landowners and residents, developers and operators, component suppliers, ecologists, and GIS analysts. The stakeholders’ preferences regarding the relative importance of criteria are measured using a logarithmic least squares method, and then the alternative sites are prioritized based on their relative closeness to the positive ideal solution. The proposed model is applied to determine the most appropriate site for constructing an onshore wind power plant consisting of 10 wind turbines of 2.5 MW. Finally, the results are discussed and compared with those obtained using the traditional AHP, ANP and ANP-TOPSIS decision-making approaches.

COVID-19 susceptibility mapping: a case study for Marinduque Island, Philippines

Small islands are highly susceptible to infectious disease outbreak and other health emergencies because of their remoteness, small physical size, and poorly developed infrastructure. These are true in the case of Marinduque, an island province around 200 km south of the National Capital Region (NCR), which is the “epidemiological epicenter” of the COVID-19 pandemic in the Philippines. This study utilized GIS and Principal Component Analysis (PCA) using demographic, socio-economic, and geographic indicators to map susceptibility of different villages in the island province of Marinduque, the Philippines. Based on the results, the northwestern and northeastern portion of Marinduque has a higher susceptibility score. Also, villages in the town centers have relatively high susceptibility scores compared to other villages in each municipality.

A GIS-Based Spatiotemporal Impact Assessment of Droughts in the Hyper-Saline Urmia Lake Basin on the Hydro-Geochemical Quality of Nearby Aquifers

Urmia Lake is a hyper-saline lake in northwestern Iran that has been drying up since 2005. The main objective of this study was to evaluate the water quality in aquifers that are the main source of fresh water for the eastern plains Urmia Lake, which has been drying up due to intensive land use/cover changes and climate change. We evaluated hydro-geochemical data and factors contributing to aquifer pollution and quality variation for nine aquifers in the vicinity of Urmia Lake during the dry and wet seasons from 2000–2020. Our methodology was based on the analysis of 10 years of data from 356 deep and semi-deep wells using GIS spatial analysis, multivariate statistical analysis, and agglomerative hierarchical clustering. We developed a Water Quality Index (WQI) for spatiotemporal assessment of the status of the aquifers. In doing so, we highlighted the value of combining Principal Component Analysis (PCA), WQI, and GIS to determine the hydro-geochemical attributes of the aquifers. We found that the groundwater in central parts of the study area was unsuitable for potable supplies. Anthropogenic sources of contamination, such as chemical fertilizers, industrial waste, and untreated sewage water, might be the key factors causing excessive concentrations of contaminants affecting the water quality. The PCA results showed that over 80% of the total variance could be attributed to two principal factors for most aquifers and three principal factors for two of the aquifers. We employed GIS-based spatial analysis to map groundwater quality in the study area. Based on the WQI values, approximately 48% of groundwater samples were identified as poor to unsuitable for drinking purposes. Results of this study provide a better hydro-geochemical understanding of the multiple aquifers that require preventive action against groundwater damage. We conclude that the combined approach of using a multivariate statistical technique and spatial analysis is effective for determining the factors controlling groundwater quality.

OpenHi.net: A Synergistically Built, National-Scale Infrastructure for Monitoring the Surface Waters of Greece

The large-scale surface-water monitoring infrastructure for Greece Open Hydrosystem Information Network (Openhi.net) is presented in this paper. Openhi.net provides free access to water data, incorporating existing networks that manage their own databases. In its pilot phase, Openhi.net operates three telemetric networks for monitoring the quantity and the quality of surface waters, as well as meteorological and soil variables. Aspiring members must also offer their data for public access. A web-platform was developed for on-line visualization, processing and managing telemetric data. A notification system was also designed and implemented for inspecting the current values of variables. The platform is built upon the web 2.0 technology that exploits the ever-increasing capabilities of browsers to handle dynamic data as a time series. A GIS component offers web-services relevant to geo-information for water bodies. Accessing, querying and downloading geographical data for watercourses (segment length, slope, name, stream order) and for water basins (area, mean elevation, mean slope, basin order, slope, mean CN-curve number) are provided by Web Map Services and Web Feature Services. A new method for estimating the streamflow from measurements of the surface velocity has been advanced as well to reduce hardware expenditures, a low-cost ‘prototype’ hydro-telemetry system (at about half the cost of a comparable commercial system) was designed, constructed and installed at six monitoring stations of Openhi.net.

Prediction of direct carbon emissions of Chinese provinces using artificial neural networks.

Closely connected to human carbon emissions, global climate change is affecting regional economic and social development, natural ecological environment, food security, water supply, and many other social aspects. In a word, climate change has become a vital issue of general concern in the current society. In this study, the carbon emission data of Chinese provinces in 1999–2019 are collected and analyzed, so as to identify the carbon emission of direct consumption per 10,000 residents in each province (including each municipal city and autonomous region) and the entire nation based on population data. The Arc Geographic Information Science Engine (ArcGIS Engine) and C#.NET platform are employed to call the MATLAB neural network toolbox. A model is selected and embedded in the prediction system to develop the entire system. This study demonstrates that the carbon emissions per resident in Northern China are significantly higher than those in Southern China, with the rate of carbon emissions continuing to increase over time. Compared with other models, the Elman neural network has a higher carbon emission prediction accuracy, but with more minor errors. For instance, its accuracy and prediction performance are improved by 55.93% and 19.48%, respectively, compared with the Backpropagation Neural Network (BPNN). The prediction results show that China is expected to reach its peak carbon emission in around 2025–2030. The above results are acquired based on the concept of carbon emissions and neural network model theories, supported by GIS component technology and intelligent methods. The feasibility of BPNN, Radial Basis Function (RBF) and Elman neural network models for predicting residential carbon emissions is analyzed. This study also designs a comprehensive, integrated and extensible visual intelligent platform, which is easy to implement and stable in operation. The trend and characteristics of carbon emission changes from 2027 to 2032 are explored and predicted based on the data about direct carbon emissions of Chinese provincial residents from 1999 to 2019, purposed to provide a scientific basis for the control and planning of carbon emissions.