Geographic Information System Data Research Articles

Assessment of Groundwater Vulnerability to Contamination using GIS-based DRASTIC model in Lucknow, India

Abstract: Groundwater intrinsic vulnerability maps are considered a promising means for forestalling groundwater resources from contamination in recent years. With the use of a Geographic Information System (GIS) and hydro-geochemical data, the DRASTIC model is used to forecast groundwater vulnerability. For which ArcGIS software is used. Lucknow, capital of Uttar Pradesh, is the study area for this study. This model is based on the seven data layers that provide the input to the modelling. It corresponds to the initials of seven layers i.e. Depth to water, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose zone ,and Hydraulic Conductivity. Mal and Sarojani Nagar Block showed higher vulnerability (139–143). The results also suggested that the low, moderate, and high groundwater vulnerability zones cover around 30.35%, 45.02%, and 24.63 % of the study area, respectively. The results is validated using nitrate concentration in groundwater. The percentage of study area for nitrate concentrations <45 mg/l, 45-60 mg/l, 60-80 mg/l, and 80-110 mg/l are 55.5%, 34.5%, 9%, and ~1% respectively. The GIS technique has provided an efficient tool for assessing and analysing the vulnerability to groundwater pollution.

Identifying Urban Heat Effect through Satellite Image Analysis: Focusing on Narayanganj Upazila, Bangladesh

The rapid growth of population and land use cover change are closely connected. Narayanganj Sadar Upazila is the first Growing City in Bangladesh. Land use and land cover change are very first. Global warming, increased greenhouse gas emissions, and other environmental issues have become critical issues to address in recent times. Because of the Unplanned Expansion of urban areas, the LULC pattern is changing, and this kind of adverse (LST increasing, Heat Island Growth) impact is increasing. In this area, the amount of vegetation is decreasing day by day. The objectives of this study are to identify land use land cover (LULC) dynamics for the year 2001 to 2021, identify Urban Heat Islands from the value of land surface temperature (LST) and identify hotspots based on normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference water index (NDWI) and land surface temperature (LST) doing weighted overlay among them. To determine the land cover pattern, and change at Narayanganj Upazila in the years 2001, 2011, and 2021, the land cover type was divided into four categories (build-up, vegetation, water bodies, and barren soil) in geographic information system (GIS) and Earth Resources Data Analysis System (ERDAS) Imagine 2014. The overall accuracy of LULC in 2001, 2011, and 2021 was 90.08, 91.34, and 92.02, respectively. And the value of the kappa coefficient for the years 2001, 2011, and 2021 was 0.91, 0.89, and 0.90, respectively. This study demonstrates an increase of 14.22% in built-up area and destruction of 15.5% of vegetation, 3.26% of barren soil, and 1.05% of the waterbody in the previous 21 years in Narayanganj Upazila. This study will help the administration, agricultural directorates, Pourashava office, and city corporation authority to take necessary measures to mitigate the adverse impacts of land cover change. They can make new rules and regulations on the construction of buildings, restrictions on filling water bodies and measures to conserve vegetation.

Machine Learning and Hyperparameters Algorithms for Identifying Groundwater Aflaj Potential Mapping in Semi-Arid Ecosystems Using LiDAR, Sentinel-2, GIS Data, and Analysis

Aflaj (plural of falaj) are tunnels or trenches built to deliver groundwater from its source to the point of consumption. Support vector machine (SVM) and extreme gradient boosting (XGB) machine learning models were used to predict groundwater aflaj potential in the Nizwa watershed in the Sultanate of Oman (Oman). Nizwa city is a focal point of aflaj that underlies the historical relationship between ecology, economic dynamics, agricultural systems, and human settlements. Three hyperparameter algorithms, grid search (GS), random search (RS), and Bayesian optimisation, were used to optimise the parameters of the XGB model. Sentinel-2 and light detection and ranging (LiDAR) data via geographical information systems (GIS) were employed to derive variables of land use/land cover, and hydrological, topographical, and geological factors. The groundwater aflaj potential maps were categorised into five classes: deficient, low, moderate, high, and very high. Based on the evaluation of accuracy in the training stage, the following models showed a high level of accuracy based on the area under the curve: Bayesian-XGB (0.99), GS-XGB (0.97), RS-XGB (0.96), SVM (0.96), and XGB (0.93). The validation results showed that the Bayesian hyperparameter algorithm significantly increased XGB model efficiency in modelling groundwater aflaj potential. The highest percentages of groundwater potential in the very high class were the XGB (10%), SVM (8%), GS-XGB (6%), RS-XGB (6%), and Bayesian-XGB (6%) models. Most of these areas were located in the central and northeast parts of the case study area. The study concluded that evaluating existing groundwater datasets, facilities, current, and future spatial datasets is critical in order to design systems capable of mapping groundwater aflaj based on geospatial and ML techniques. In turn, groundwater protection service projects and integrated water source management (IWSM) programs will be able to protect the aflaj irrigation system from threats by implementing timely preventative measures.

The Effect of Urban Expansion on Peripheral Agricultural Lands in Makurdi City

This study investigated the effects of urban sprawl on peripheral agricultural lands in Makurdi City over the period 1999-2012. The study conducted a land use and vegetation cover change (LULCC) analysis using remote sensing (RS) and geographic information system (GIS) data to analyze two epochs of Land Sat imagery, 1999 and 2012, acquired to achieve the objective. It was found that between 1999 and 2012, urban sprawl had a negative impact on peripheral agricultural land in Makurdi town, as it significantly reduced peripheral agricultural land. The study also revealed that despite the adverse impact of urban sprawl on peripheral agricultural lands in Makurdi town, its socio-economic impact has led to the establishment of infrastructural facilities such as roads, electricity, markets, and parks. Therefore, the study recommended that the agricultural landscape plan be incorporated by the government into urban land use planning systems so that urbanization does not interfere with agricultural activities and thus maximize their collective benefits.

Roadway Vulnerability Assessment against Hurricanes Using Satellite Images

Infrastructures such as roadways, power lines, and communications networks play a critical role in our society. However, they are also susceptible to failures, especially those caused by extreme events, quickly affecting large geographical areas. Predicting where and when these failures will occur with high confidence is very difficult because of the stochastic nature of such events. Nevertheless, it is possible to know in advance which areas are more vulnerable and plan accordingly. This paper aims to use remote sensing techniques based on satellite images to detect roadways’ vulnerabilities to hurricanes. Each road is assigned a vulnerability score based on the information retrieved from the satellite image and other geographic information system (GIS) data, which can be used to create a vulnerability map of the area. Both vegetation exposure along the roads and the consequences of the closures are considered in the vulnerability assessment. The study area has been selected in Tallahassee, Florida, U.S., where a high-resolution satellite image was acquired in September 2018, 2 weeks before Hurricane Michael. The findings of this work can help management teams and city responders to identify the most vulnerable regions at risk of disruption and organize resources before the event.

Geographic information system data considerations in the context of the enhanced bathtub model for coastal inundation

AbstractThis study determines input data appropriateness for geographic information system‐based enhanced bathtub model (eBTM) modeling of coastal inundation in an urban setting. Various tests were conducted concerning digital surface models (DSMs) to determine: (a) the highest appropriate resolution achievable from available LiDAR data and consider variations between derived sub‐meter DSMs; (b) optimal DSM horizontal resolution for coastal inundation modeling based on “out‐the‐box” solutions; and (c) mechanisms to address the challenge presented by DSMs regarding overhanging structures for a study site in False Bay, South Africa. Results showed that while sub‐meter DSMs are achievable, low point cloud densities may result in the misrepresentation of structures, which affects the inundation extents. High horizontal resolution DSMs are required for inundation modeling in an urban setting to account for narrow thoroughfares. Challenges posed by first return LiDAR depicting bridges as solid structures could be circumvented by modifying the input water source for the eBTM processing.

A critical review of the fair value of land in Kerala, India

PurposeProperty tax continues to be the most viable, steady, progressive and genuine source of income for an independent local government administration in India. Kerala has one of the most complex cadastral and property taxation systems in the country. In 2008, the Kerala government introduced “Fair value of land” for all landed property in Kerala, which is calculated per Are and notified by the Government of Kerala on the website. This paper presents the outcome of the spatial mapping of the fair value of land in Kerala and its comparison with actual land value collected through advertisements and surveys.Design/methodology/approachThe methodology used in this study analyzed the existing fair value system of land valuation in Kerala and identified its drawbacks. Fair value is integrated into cadastral data in geographical information system (GIS) and spatially analyzed to identify the discrepancies in fair value fixation. The actual land value for 837 locations is collected from online advertisements and verified through a field survey. A paired t-test analysis follows this to compare the fair value and actual land value of the study area.FindingsThe disparity between the original land value and the registration value in Kerala has resulted in the undervaluation of land resources by the government. The fair value fixed by the government is significantly lower than the actual land value based on the statistical comparison results. It is observed that the actual land value is multiple times higher than the government fixed land value. Also, the spatial distribution of the village level fair value shows the randomness in fixation and abnormalities existing at numerous locations.Practical implicationsThe results indicate that the traditional comparison method used for fair value calculation by the Kerala government is not suitable in Indian scenarios due to the unreliability of registration values. The study thus points to the importance of developing a scientific method for determining the land value that would consider various spatially influencing parameters.Originality/valueThe current study provided an in-depth analysis of the land valuation system of the Kerala government. The strengths and weaknesses of the existing system are analyzed through statistical methods.

Design and implementation of geographic information system with mobile terminal and 5G network

AbstractRemote insurance mainly completes quotation, insurance application and payment through the PC terminal and mobile terminal, and the premium directly enters the insurance account. This article takes remote insurance as an example. First, it analyses the requirements of the remote insurance and insurance system, and then collects data from the mobile terminal. The system geographic information system (GIS) server subsystem and GIS information management subsystem are designed in three aspects. GPS data and video image data are collected and processed in the data acquisition subsystem, forwarded by the GIS server, and sent to the GIS information management subsystem. Geographic information system information management realises online loss determination through basic information query, geographic location identification, scheduling, path planning, real‐time tracking, image information etc. on the mobile terminal, combined with insurance and insurance business. In terms of implementation, the GIS information management platform and Google Maps are used to realise the monitoring, scheduling and path planning of the mobile terminal GIS online platform. And conduct experimental research on the content‐centric 5G structure. The results show that the transmission interference time of the content‐centric network‐based 5G structure is 390 ms, which is much lower than the 800 ms demanded for GSM‐R system. It proves that the content‐centric network‐based 5G structure has good mobile performance; the test of the content‐centric network verifies that the content‐centric network has the advantages of balancing network load and preventing network congestion; meanwhile, the test of the proposed The functions and performance of the GIS system proposed in this paper are tested. The results meet the user requirements.

Built Environments And Child Health in WalEs and AuStralia (BEACHES): a study protocol

IntroductionChildhood obesity and physical inactivity are two of the most significant modifiable risk factors for the prevention of non-communicable diseases (NCDs). Yet, a third of children in Wales and Australia...

Geospatial tools offer fresh perspective on patient care.

By combining geographic information system (GIS) technology with data from electronic health records, health registries, and other sources, clinicians can have a powerful tool to improve patient care, says geospatial pharmacy specialist Jing Wu. Wu is a former account executive at Esri in Arlington, VA, where she helped healthcare organizations incorporate GIS technology into their platforms. She said that by viewing patients through a GIS lens, pharmacists and other clinicians are better able “to think about our patients within the context of their lives [and] create more targeted interventions.” “I think that’s going to be the standard of practice,” Wu added. The Centers for Disease Control and Prevention (CDC) describes GIS as computer-based tools for storing, visualizing, analyzing, and interpreting geographic data. The many things GIS data can map include disease cases, hospitals, clinic types and sites, roads, public transit routes, waterways, and land boundaries. CDC uses GIS data for research on population health, toxicology, chronic disease prevention, disease surveillance, and other health issues.

Research into the Optimal Regulation of the Groundwater Table and Quality in the Southern Plain of Beijing Using Geographic Information Systems Data and Machine Learning Algorithms

The purpose of this paper is to provide new ideas and methods for the sustainable use of groundwater in areas with serious groundwater overexploitation and serious groundwater pollution. Geographic information systems (GIS) were combined with machine learning algorithms, water resources optimization technology, and groundwater numerical simulation to optimize the regulation of the groundwater table and quality beneath the Daxing District in the southern plain of Beijing. By collecting local consumption and supply data and observations of the groundwater table and quality in the connected aquifer beneath Daxing for the years 2006–2020, the corresponding water demands and groundwater impact were extrapolated for the years 2021–2025 based on the basis of the existing development model. Through the combination of GIS and machine learning algorithms, the NO3-N concentration of local groundwater monitoring points in wet years, normal years, and dry years were predicted. With respect to NO3-N pollution, three new groundwater exploitation regimes were devised, which we numbered 1 to 3. The optimal allocation of water resources was then calculated for wet year, typical year, and dry year scenarios for the year 2025. By comparing the water shortage, groundwater utilization rate, and NO3-N pollution under the new groundwater exploitation regimes, the optimal groundwater exploitation mode for the three different types of hydrological year was determined. The results indicate that NO3-N pollution was greatly reduced after the adoption of the optimal regimes and that the groundwater table demonstrated rapid recovery. These results can be of great help in realizing the management, supervision, and regulation of groundwater by combining GIS with machine learning algorithms.

Residential proximity to agricultural pesticide use and risk-taking behaviors in young adults from the CHAMACOS study

BackgroundPrenatal pesticide exposure has been associated with poorer neurodevelopment during childhood, which could lead to greater risk-taking behaviors and delinquency in adolescence. This association may be augmented by adversity exposure. ObjectivesEvaluate the relationship between prenatal pesticide exposure and risk-taking behavior in young adults at 18-years of age. Assess whether adversity exposure modifies these associations. MethodsParticipants included mother-child dyads (n = 467) enrolled in the Center for the Health Assessment of Mothers and Children Of Salinas (CHAMACOS) study, a longitudinal birth cohort set in the agricultural Salinas Valley of California. We estimated agricultural pesticide use within one km of maternal residences during pregnancy using a geographic information system, residential addresses, and California's Pesticide Use Reporting data. We used Bayesian hierarchical regression to evaluate associations of prenatal exposure to a mixture of 11 neurotoxic pesticides with self-reported police encounters, risk-taking behaviors, and unique types and frequency of delinquent acts. We also evaluated effect modification of these relationships by adversity exposure. ResultsWe observed generally null associations of neurotoxic pesticide use with risk-taking behaviors. Prenatal residential proximity to chlorpyrifos use was associated with higher risk of a police encounter, a delinquent act, and higher incidence of both unique types of acts committed and total frequency of delinquent acts. Prenatal residential proximity to dimethoate use was associated with a higher incidence of police encounters and methomyl with a higher risk of committing a delinquent act. There were no consistent differences when stratified by the number of adverse childhood experiences. ConclusionsWe observed mostly null associations between prenatal residential proximity to agricultural pesticide use and risk-taking behaviors at age 18, with little evidence of effect modification by childhood adversity. There were suggestive associations for chlorpyrifos use with having any police encounter and with all measures of delinquent acts that warrant confirmation in other studies.

A GIS enhanced data analytics approach for predicting nursing home hurricane evacuation response.

Nursing homes (NHs) are responsible for caring for frail, older adults, who are highly vulnerable to natural disasters, such as hurricanes. Due to the influence of highly uncertain environmental conditions and varied NH characteristics (e.g., geo-location, staffing, residents' health conditions), the NH evacuation response, namely evacuating or sheltering-in-place, is highly uncertain. Accurate prediction of NH evacuation response is important for emergency management agencies to accurately anticipate the NH evacuation demand surge with healthcare resources proactively planned. Existing hurricane evacuation research mainly focuses on the general population. For NH evacuation, existing studies mainly focus on conceptual studies and/or qualitative analysis using a single source of data, such as surveys or resident health data. There is a lack of research to develop analytics-based method by fusing rich environmental data with NH data to improve the prediction accuracy. In this paper, we propose a Geographic Information System (GIS) data enhanced predictive analytics approach for forecasting NH evacuation response by fusing multi-source data related to storm conditions, geographical information, NH organizational characteristics as well as staffing and residents characteristics of each NH. In particular, multiple GIS features, such as distance to storm trajectory, projected wind speed, potential storm surge and NH elevation, are extracted from rich GIS information and incorporated to improve the prediction performance. A real-world case study of NH evacuation during Hurricane Irma in 2017 is examined to demonstrate superior prediction performance of the proposed work over a large number of predictive analytics methods without GIS information.

Risk Mapping and Spatial Modeling of Human Cystic Echinococcosis in Iran from 2009 to 2018: A GIS-Based Survey.

Cystic echinococcosis (CE) is one of the most important parasitic infections in subgroup seven common neglected diseases of humans and animals. It is in the list of 18 neglected tropical diseases of the WHO. We aimed to analyze the situation of the disease in Iran using Geographical Information System (GIS) and satellite data analysis. The data obtained from the Ministry of Health and Medical Education, Tehran, Iran and other related centers from 2009 to 2018 were analyzed using GIS. Then, the spatial distribution maps of the disease were generated, and the hot spots of the disease in Iran were determined using spatial analysis of ArcGIS10.5 software. Geographically weighted regression (GWR) analysis in ArcGIS10.5 was used to correlate the variables affecting the disease including temperature, relative humidity, normalized different vegetation index (NDVI) and incidence of hydatidosis. Data analysis was performed by Linear regression analysis and SPSS 21 software using descriptive statistics and chi-square test. Zanjan, Khorasan Razavi, North Khorasan, Chaharmahal Bakhtiari, Hamedan, Semnan, and Ardabil provinces were the hot spots of CE. The results of geographical weighted regression analysis showed that in Khorasan Razavi, North Khorasan, Chaharmahal Bakhtiari, Hamedan, Semnan, Ardabil, Zanjan, Qazvin, and Ilam provinces, the highest correlation between temperature, humidity, vegetation density and the incidence of hydatidosis was observed (P<0.001). The use of maps could provide reliable estimates of at-risk populations. Climatic factors of temperature, humidity, NDVI had a greater impact on the probability of hydatidosis. These factors can be an indicator used to predict the presence of disease. Environmental and climatic factors were associated with echinococcosis.

A Schelling Extended Model in Networks—Characterization of Ghettos in Washington D.C.

Segregation affects millions of urban dwellers. The main expression of this reality is the creation of ghettos which are city parts characterized by a combination of features: low income, poor cultural level… Segregation models have been usually defined over regular lattices. However, in recent years, the focus has shifted from these unrealistic frameworks to other environments defined via geographic information systems (GIS) or networks. Nevertheless, each one of them has its drawbacks: GIS demands high-resolution data, that are not always available, and networks tend to have limited real-world applications. Our work tries to fill the gap between them. First, we use some basic GIS information to define the network, and then, run an extended Schelling model on it. As a result, we obtain the location of ghettos. After that, we analyze which parts of the city are segregated, via spatial analysis and machine learning and compare our results. For the case study of Washington D.C., we obtain an 80% accuracy.

Repairing geometric errors in 3D urban models with kinetic data structures

3D urban models created either interactively by human operators or automatically with reconstruction algorithms often contain geometric and semantic errors. Correcting them in an automated manner is an important scientific challenge. Prior work, which traditionally relies on local analysis and heuristic-based geometric operations on mesh data structures, is typically tailored-made for specific 3D formats and urban objects. We propose a more general method to process different types of urban models without tedious parameter tuning. The key idea lies on the construction of a kinetic data structure that decomposes the 3D space into polyhedra by extending the facets of the imperfect input model. Such a data structure allows us to re-build all the relations between the facets in an efficient and robust manner. Once built, the cells of the polyhedral partition are regrouped by semantic classes to reconstruct the corrected output model. We demonstrate the robustness and efficiency of our algorithm on a variety of real-world defect-laden models and show its competitiveness with respect to traditional mesh repairing techniques from both Building Information Modeling (BIM) and Geographic Information Systems (GIS) data.

Machine Learning for Prediction of Cognitive Health in Adults Using Sociodemographic, Neighbourhood Environmental, and Lifestyle Factors.

The environment we live in, and our lifestyle within this environment, can shape our cognitive health. We investigated whether sociodemographic, neighbourhood environment, and lifestyle variables can be used to predict cognitive health status in adults. Cross-sectional data from the AusDiab3 study, an Australian cohort study of adults (34–97 years) (n = 4141) was used. Cognitive function was measured using processing speed and memory tests, which were categorized into distinct classes using latent profile analysis. Sociodemographic variables, measures of the built and natural environment estimated using geographic information system data, and physical activity and sedentary behaviours were used as predictors. Machine learning was performed using gradient boosting machine, support vector machine, artificial neural network, and linear models. Sociodemographic variables predicted processing speed (r2 = 0.43) and memory (r2 = 0.20) with good accuracy. Lifestyle factors also accurately predicted processing speed (r2 = 0.29) but weakly predicted memory (r2 = 0.10). Neighbourhood and built environment factors were weak predictors of cognitive function. Sociodemographic (AUC = 0.84) and lifestyle (AUC = 0.78) factors also accurately classified cognitive classes. Sociodemographic and lifestyle variables can predict cognitive function in adults. Machine learning tools are useful for population-level assessment of cognitive health status via readily available and easy-to-collect data.

Perancangan Sistem Pemetaan Wilayah Calon Pelanggan Dengan Menggunakan Qgis Pada Pt. Indonesia Comnets Plus (Icon+) Sbu Bengkulu

Quantum GIS is an open source Geographic Information System (GIS) software. Geographic Information Systems (GIS) can be used or run on various operating systems. QGIS aims to be a GIS for displaying GIS data and providing common functions and features. This research was conducted to design a mapping system for prospective customers using QGIS at PT. Indonesia Comnets Plus (ICON+) SBU Bengkulu. The research method uses the literature study method, the observation method and the interview method. The benefit of the system designed is that for marketing ICON+ SBU Bengkulu, there is no need to guess the location of network installation in areas where customer information is not yet clear.

Study on city digital twin technologies for sustainable smart city design: A review and bibliometric analysis of geographic information system and building information modeling integration

• Analyzes the future research trend of GIS and BIM integration through literature review. • GIS and BIM integration has been the basic technology to realize smart cities. • Future research should focus on smart city and urban rail transportation. Geographic information system (GIS) data provide geospatial data on cities and spatial analysis functions that are essential for urban design. Building information modeling (BIM) includes a digital entity of construction, a passive presentation of micro-digital information on real entities, and an active application of models in the entire life cycle realization of the architecture, engineering, and construction industries. A combination of these technologies could provide a core technology for the urban digital twin to support sustainable smart city design. Through an insightful literature review, this paper summarizes the different disciplinary classifications of GIS and BIM functional integration, distills the value of data, and discusses the ontology-based data integration approach that GIS and BIM should take in the future to conduct research on integration applications in smart cities. To verify this view, keyword analysis, co-country analysis, and co-citation and coupling analyses are conducted using CiteSpace. GIS and BIM integration has attracted much attention. However, a professional disconnect and fragmented composition pose challenges in the field of GIS and BIM integration. Future research should focus on smart city planning, updating, management; ontology-based GIS and BIM data integration platform; and operation; and the collaborative management of urban rail transportation engineering.

Point‐of‐interest detection from Weibo data for map updating

AbstractPoints‐of‐interest (POIs) geographic information system data are increasingly important for supporting map generation and navigation services, although updating their semantic and location information still largely depends on manual labor. In this study, we propose a novel method to automatically detect the changes in POIs from Chinese text and check‐in position data provided by the Chinese social media platform, Weibo. The proposed method includes three steps: (1) POI name recognition; (2) location confirmation; (3) and change detection. First, we propose recognizing a POI's name from Weibo text using the improved conditional random field algorithm. Then, we detect the location of each named POI by integrating the text address with the check‐in position. The changes in the detected POIs are recognized by extracting the status words from Weibo text and a three‐level status word database. To verify the effectiveness of the proposed method, we examine Wuhan as a case and detect the changes in the commercial POI using real‐world Weibo data collected from January to September 2020. Based on the validation of three common map platforms, the data provided and the manual field investigation of 55 random samples, the identification accuracies for newly added POIs, the unchanged POIs, and expired POIs are approximately 100, 95.8, and 91.7%, respectively.