Geographic Information System Analysis Research Articles

Spatially explicit vulnerability analysis of contaminant sources in a karstic watershed in southeastern Mexico

A water vulnerability assessment was performed on the Río Grande-Lagos de Montebello watershed, in southeastern region of Chiapas, Mexico. This is a karstic environment highlighted by well-preserved lakes, close to Guatemala border. We used multicriteria analysis to identify areas vulnerable to water contamination at subbasin scale using geographic information system and spatial analysis. Potential contamination sources were georeferenced and overlapped with precipitation and slope layers to identify areas prone to contaminant transport and infiltration. The results showed that both irrigation and rainfed agriculture were the main contamination sources, followed by wastewater discharge. Supported by land use and forest change analysis, agricultural activities were the most important drivers of change. A water vulnerability map at subbasin scale is presented as an outcome, which enables the ranking of water contamination potential in presence of different contamination sources using the weighted sum and linear normalization methods. Two subbasins were identified within the most vulnerable categories, Comitán and El Triunfo. No previous water vulnerability analyses have been performed for this region. We propose this vulnerability map as a decision-making tool for sustainable water management at watershed scale in areas with scarce information and increasing water quality degradation processes.

Fractional Linear Regression Equation in Agricultural Disaster Assessment Model Based on Geographic Information System Analysis Technology

Abstract This article combines geographic information system (GIS) technology and database technology to analyse agricultural, natural disasters. The article uses a fractional linear regression equation to define the comprehensive intensity grading standard of the disaster-causing factors of torrential rain. At the same time, we use GIS to superimpose the agricultural vulnerability index into the storm disaster risk zoning to obtain the degree of agricultural impact under different levels of risk. At the end of the thesis, the model is applied to actual case analysis to verify the effectiveness of the algorithm model.

Shoreline changes due to construction of groyne field in north of Chennai Port, India.

Chennai Port (13.099872° N, 80.297407° E), located along the southeast coast of India, has been a hub for maritime trade since the fifteenth century. An artificial harbour was initially constructed in 1881 which underwent numerous expansions in the following years. The breakwaters of the harbour intercepted the heavy sediment-laden littoral drift along the coast, resulting in the formation of the world's second-longest urban beach south of the port, i.e. on its up-drift side. Meanwhile, the coast north of the port, i.e. the down-drift side, experienced intense erosion due to a lack of sediment supply and forces induced on the coast due to waves and currents. The shoreline change study in this paper investigates a shoreline stretch of about 6km (protected by transitional groynes), north of Chennai Port by dividing it into three segments. The rate of shoreline changes over a period of about 12years across three different segments was assessed using statistical parameters by employing remote sensing techniques complemented with geographical information system (GIS) and digital shoreline analysis system (DSAS) tool. It is inferred that the coast has witnessed accretion and sizable growth in beach width has been observed post the construction of groyne field.

Sustainability of Urban Regions and Migration in Pakistan: A GIS Analysis

The concept of sustainable migration has emerged recently after realizing the potential of migration in framing and altering the social, economic and environmental structures at destination, especially in the context of sustainable development goals (SDGs). An empirical investigation on the link between regional sustainability and sustainable migration is rare in literature, especially in the context of Pakistan’s urban areas. Present study aims at analysing the relationship between the two by geographic information system (GIS) spatially. This study shows that out of thirteen urban regions, hosting above-average migration along with positive in-migration growth, the migration towards nine regions is unsustainable. Two mega cities namely, Karachi and Lahore are included. This highlights the sustainable growth of regions, specifically and the

Environmental risk factor assessment for major respiratory disorders in metropolitan cities of India using VIIRS Suomi Aerosol data and Google Trends.

This study has investigated the association between the amount of atmospheric aerosols and the occurrences of Asthma, Chronic Obstructive Pulmonary Disease (COPD) and Lung Cancer in Delhi, Mumbai, Chennai, Kolkata and Bengaluru. Aerosol Optical Thickness (AOT) data of Visible Infrared Imaging Radiometer Suite (VIIRS) and Google Trends (GT) have been used to acquire information regarding the abundance of atmospheric aerosols and the occurrences of the respiratory diseases respectively. The result of Granger causality test between AOT and GT has shown that Delhi, Mumbai and Chennai were quite vulnerable to the three respiratory diseases whereas Bengaluru did not display so much vulnerability to these ailments. Kolkata was not so much vulnerable to Asthma but did exhibit susceptibility to the other two diseases. GT is validated by correlating with Annual Morbidity data of Delhi. The result of Granger causality test between Particulate Matter (diameter ≤ 10 μm) (PM10) data and GT validates the result of Granger causality between AOT and GT, and shows the trustworthiness of GT and AOT. Thus, this study also proves the usefulness of VIIRS AOT and GT as dependable sources of information on atmospheric aerosols and prevalence of the respiratory diseases respectively, and the effectiveness of Granger causality test as a tool of analysis in health and geographic information systems (GIS).

Land Suitability Assessment for Pulse (Green Gram) Production through Remote Sensing, GIS and Multicriteria Analysis in the Coastal Region of Bangladesh

The agricultural potential of Bangladesh’s coastal region has been threatened by the impact of climate change. Pulse crops with high nutritional value and low production costs such as green gram constitute an important component of a healthy and accessible diet for the country. In order to optimize the production of this important staple, this research aims to promote climate-smart agriculture by optimizing the identification of the appropriate land. The objective of this research is to investigate, estimate, and identify the suitable land areas for green gram production based on the topography, climate, and soil characteristics in the coastal region of Bangladesh. The methodology of the study included a Geographic Information System (GIS) and the Multicriteria Decision-Making approach: the Analytical Hierarchy Process (AHP). Datasets were collected and prepared using Landsat 8 imagery, the Center for Hydrometeorology and Remote Sensing (CHRS) data portal and the Bangladesh Agricultural Research Council. All the datasets were processed into raster images and then reclassified into four classes: Highly Suitable (S1), Moderately Suitable (S2), Marginally Suitable (S3), and Not Suitable. Then, the AHP results were applied to produce a final green gram suitability map with four classes of suitability. The results of the study found that 12% of the coastal area (344,619.5 ha) is highly suitable for green gram production, while the majority of the land area (82.3% of the area) shows moderately suitable (S2) land. The sensitivity analysis results show that 3.3%, 63.4%, 28.0%, and 1.2% of the study area are S1, S2, S3, and NS, respectively. It is also found that the highly suitable land area belongs mostly to the southeastern part of the country. The result of this study can be utilized by policymakers to adopt a proper green gram production strategy, providing special agricultural incentive policies in the highly suitable area as a provision for the increased food production of the country.

Using Neural Networks for Bicycle Route Planning

This paper presents the usage of artificial neural networks (NNs) in bicycle route planning. This research aimed to check the possibility of NNs to transfer human expertise in bicycle route design by training the NN on an already established set of bicycle routes and then using the trained NN to design the routes on the novel area. We created two NNs capable of choosing the best route among the given road network by training them on two different areas. The bicycle routes produced by NNs were the same at best and had 75% overlap at the worst compared to those produced by human experts. Furthermore, the mean square error for all of our NN models varied from 0.015 and 0.081. We compared this new approach to the traditional multicriteria GIS (geographic information system) analysis (MA) that requires the human expert to define the bicycle route selection criteria. The benefit of using NN over the MA was that the NN directly transfers the human expertise to a model. In contrast, the MA needs the expert to select multiple criteria and adjust their weights carefully.

Spatial Analysis of Regional and Income Inequality in the United States

Understanding the spatial or geographical dependence of income inequality and regional inequality is crucial in the study of inequality. This paper employs a multi-scale, multi-mechanism framework to map and analyze historical patterns of regional and income inequality in the United States (US) by using state and regional panel data spanning over a century. To explore the patterns systematically and see the role of spatial partitioning, we organize the data around several established geographical partitions before conducting various geographical information system (GIS) analyses and statistical techniques. We also investigate the spatial dependence of income inequality and regional inequality. We find that spatial autocorrelation exists for both types of inequality in the US. However, the magnitude of spatial dependence for regional inequality is declining whereas it is volatile for income inequality over time. While income inequality has been at its peak in the most recent decades, we also notice that regional inequality is at its lowest point. As for the choice of partitioning, we observe that within inequality dominates for Census Divisions and Bureau of Economic Analysis (BEA) regions. Conversely, we see that between inequality overall contributes the most to the inequality among Census Regions.

Groundwater potential zones mapping by applying GIS, remote sensing and multi-criteria decision analysis in the Ghiss basin, northern Morocco

The Ghiss watershed, located in a Mediterranean semi-arid area, is currently experiencing a prolonged drought. The scarcity of potable surface water has driven the population and the government to intensify the exploration of groundwater resources. Conventional methods used for groundwater exploration are generally expensive, time-consuming and are limited in space. Therefore, we suggest with the present study the application of geospatial techniques (Geographical Information Systems (GIS) and Remote Sensing) and Multi-criteria decision analysis as a non-conventional and more accessible method of groundwater exploration in the Ghiss basin. Accordingly, eleven factors of high importance concerning the delineation of groundwater potential zones were weighted depending on their relative importance using the Analytical Hierarchy Process's (AHP) pairwise comparison matrix. The GIS-based analysis of multiple thematic layers (drainage density, lineament density, roughness, lithology, land use and land cover, elevation, curvature, topographic wetness index, topographic position index, rainfall and slope) has revealed that “poor” and “moderate” groundwater potential constitute respectively 7% (58.59 km2) and 55% (460.35 km2) of the area, while “good” and “very good” groundwater potential represent respectively 35% (292.95 km2) and only 3% (25.11 km2) of the area. Our model results were validated using depth to water level (DWL) data of the Ghiss watershed and this comparison showed an acceptable conformity (70%) with GWPZs mapped for the Ghiss basin. It was also determined that the degree of conformity to generated GWPZ models using fewer parameters is comparable to those using more parameters. Our results indicate that two parameters (Lineament Density and Lithology) were sufficient to obtain a similar conformity (68%) with depth to water level data.

Assessment of direct economic losses of flood disasters based on spatial valuation of land use and quantification of vulnerabilities: a case study on the 2014 flood in Lishui city of China

Abstract. Detailed and reliable assessment of direct economic losses of flood disasters is important for emergency dispatch and risk management in small- and medium-sized cities. In this study, a single flood disaster in Lishui city in 2014 was taken as an example to study and verify a method for rapid and detailed assessment of direct economic loss. First, based on a field investigation, the inundation data simulated by the one-dimensional hydrodynamic model and geographic information system (GIS) analysis method were verified. Next, the urban land use map and high-resolution land use classifications based on remote sensing data were fused and combined with expert questionnaire surveys, thereby providing the 47 types and values of land use. Then, based on the previous depth–damage function in the past study, the vulnerability curves of 47 types of land use in Liandu district were fitted by the lognormal cumulative distribution function and then calibrated using disaster loss report data. Finally, the distributions of the loss ratio and loss value were estimated by spatial analysis. It is found that the land use data have detailed types and value attributes as well as high resolution. Secondly, the vulnerability curves after function fitting and calibration effectively reflect the change characteristics of land use loss ratio in this area. Finally, except for the three types of land for agriculture, recreational and sports facilities, and green parking spaces, the optimized simulated total loss is CNY 322.6 million, which is 0.16 % higher than the statistics report data. The estimated loss ratio and loss value can reliably reflect the distribution pattern of disaster losses in detail, which can be applied by government and private sectors to implement effective disaster reduction and relief measures.

Artificial Neural Network Model in Spatial Analysis of Geographic Information System

In the past two decades, the computer technology industry has developed rapidly, and the geological prospecting industry is also undergoing a computerized and electronic revolution. The application technology of new geological information systems is gradually adding us to the spatial information system of geological prospecting projects. In order to deeply study the current situation of the artificial neural network model in the spatial analysis of our country’s geographic information system, this paper uses the traditional classification analysis method; database analysis and neural network analysis method of compensating samples were collected, an artificial model of the network is established, and the algorithm is simplified. And a neural network model is created. In the research of A and B counties’ geographic information system, using a new network model, 61 geological disasters were found in County A, of which 47 were landslides, 4 collapses, and 10 unstable slopes. There were 19 geographical disasters in County B, including 9 unstable slopes, 6 landslides and 4 collapses. In terms of geographic prediction combined with the network model, the comparison with the actual situation shows that the geographical distribution is 99.7% in the geographical and geological disaster-prone areas, and the geographical distribution is less in the nonprone areas, with a proportion of 0.3%. Geological disaster-prone areas of low points accounted for 76.9%, and the number of disaster-affected points in the low-prone areas accounted for 22.8%. The geographical and geological grades divided by the evaluation model are basically consistent with the actual grades, which can meet the needs of geographic evaluation. It is basically realized that starting from the model’s geographic information system, a more comprehensive and practical artificial neural network model is designed.

High-resolution data shows China’s wind and solar energy resources are enough to support a 2050 decarbonized electricity system

This study aims to provide a detailed spatial and temporal characterization of China’s wind and solar energy resource potential. Quantifying this potential is necessary to identify pathways to achieve a deep decarbonization of its electric power system as this nation pursues carbon neutrality by 2060. This study identifies and characterizes sites suitable for onshore wind and ground-mounted solar PV deployment, quantifies their electricity generation potential, and assesses their spatial heterogeneity across the country and temporal variability throughout the seasons. Resource potential estimates are obtained by combining the latest data with high spatiotemporal resolution with a geographic information system (GIS) analysis that compiles information on wind and solar energy resources, land use, surface elevation and slope, and geomorphology. Results show that China’s vast resource potential for wind and solar is enough to provide one-and-a-half times 2050′s expected electricity demand. Results also demonstrate that China’s resource-rich areas do not correspond to demand centers, except for provinces like Shandong, Hebei, and Jiangsu, which have high electricity demand and renewable potential. The seasonal patterns show that China should develop wind and solar energy simultaneously, to exploit wind’s highest potential during winter and early spring, and solar’s higher production during late spring and summer. These findings shed light on the sites that should be prioritized for renewable development and the need to expand power transmission capacity connecting energy-rich areas with load centers, and energy storage capacity and flexible resources to balance variable renewable output with load.

The state of the leprosy epidemic in Yunnan, China 2011-2020: A spatial and spatiotemporal analysis, highlighting areas for intervention.

Despite public health efforts to reduce the leprosy burden in Yunnan, China, leprosy remains an important public health problem in some specific areas. We analyzed the epidemiological characteristics and spatial distribution of leprosy in Yunnan, China, and provide data to guide disease prevention and control efforts. The surveillance data of newly detected leprosy cases in Yunnan, China, during 2011-2020 were extracted from the LEPROSY MANAGEMANT INFORMATION SYSTEM IN CHINA (LEPMIS), and spatial distribution analysis, spatial autocorrelation analysis, and spatiotemporal scanning were performed with ArcGIS 10.6.1, GeoDa 1.8.8, and SaTScan 9.4.3 software, respectively. A total of 1907 newly detected leprosy cases were reported in Yunnan, China, during 2011-2020. The new case detection rate (NCDR) decreased from 0.62 in 2011 to 0.25 in 2020, with an annual incidence of 0.41/100,000 population. The proportions of multibacillary (MB) cases, cases in female patients, cases causing grade 2 physical disability (G2D), and cases in pediatric patients were 67.07%, 33.93%, 17.99%, and 2.83%, respectively. The number of counties with an incidence above 1/100,000 population decreased from 30 in 2011 to 8 in 2020. The Moran's I of leprosy in Yunnan, China, during 2011-2020 ranged from 0.076 to 0.260, indicating the presence of spatial clusters. Local spatial autocorrelation (LSA) analysis showed that high-high cluster areas (hot spots) were mainly distributed in the southeastern, northern, and northwestern regions. Spatiotemporal scanning showed three clusters with high NCDRs. The probably primary clusters, occurring during January 1, 2011-December 31, 2015, covered 11 counties in the southeastern region (RR = 5.046515, LRR = 271.749664, P = 0.000). The number of leprosy cases in Yunnan decreased overall, although some high-NCDR regions remained. Geographic information system (GIS) analysis coupled with spatial analysis indicated regions with leprosy clusters. Continuous leprosy prevention and control strategies in Yunnan Province should be established, and interventions in high-risk regions should be prioritized and further strengthened.

A spatial cost-benefit-externality modelling framework for siting of variable renewable energy farms: A case in Bohol, Philippines

Site selection is an integral component of energy planning. For renewable technologies, Geographic Information System (GIS) and multi-criteria decision-making became the most prevalent approach for such a data-intensive task. However, site selection can also be carried out using GIS and Cost-Benefit Analysis (CBA). This paper aims to introduce a four-stage energy modelling framework for siting of Variable Renewable Energy (VRE) farms using GIS and CBA. Adopting a well-established framework, the first three stages are dedicated to finding suitable sites and estimating spatio-technical inputs. In the fourth stage, CBA is performed from the developer's perspective, and the viability of VRE technologies is assessed using the benefit-cost ratio. The model output is a viability map, and externalities are separately calculated to assist planning and policymaking. We implement this framework to assess the viability and estimate the externalities of utility-scale solar PV and wind farms in Bohol and find a modest potential for viable VREs in this province. The compensation effect, which is inherent to linear combination models, is unavoidable and is manifested in our results. We have also evaluated the model sensitivity to the model variables and parameters. This framework can be implemented in a study area of any geographic scale.

A Geospatial Analysis of Disclosure of and Social Reactions to Sexual Victimization on Twitter Using #MeToo

Twitter has emerged as a space for survivors of sexual violence to share unwanted sexual experiences and for web users to provide support and feedback. Because Twitter data is often associated with latitude and longitude points, geospatial mapping of tweets that utilized the hashtag #MeToo to discuss experiences of sexual violence may be useful in understanding ways to best deliver resources to survivors who seek help online. Accordingly, the present study conducted an exploratory geographic information systems (GIS) “hotspot” (i.e., special cluster of tweet events) analysis to examine the feasibility of mapping tweets including the hashtag #MeToo with associated latitude and longitude data (N = 777) to target interventions and resource provision. Data indicated that tweets with the hashtag #MeToo were dispersed across the United States in a manner inconsistent with population density and that concentrated points emerged in other countries. Data suggest that certain geographic regions are more motivated to discuss sexual violence online and are perhaps in more dire need of violence-related resources. Future research should incorporate GIS analysis of larger Twitter datasets to target resource provision toward populations engaged in help-seeking online. GIS analysis of Twitter discourse surrounding gender-based violence may also be utilized by law enforcement, healthcare agencies, and academicians to develop targeted interventions.

Geospatial Location Based Study for Establishment of New Public Health Centers: A Case of Adama City, Oromia, Ethiopia

This study focuses on establishing adequate public healthcare centers to reduce disease spread and untimely death of human lives in Adama City, Ethiopia. There is considerable evidence, owing to poor geographical accessibility, the primary healthcare facility is not reaching the majority of the population in developing nations. This case study has been carried out to identify and prioritize the suitable areas to develop healthcare centers using a Geographic Information System (GIS). Thematic maps and ancillary data are used to analyze location-allocation analysis in ArcGIS-10.4. Required field data was collected using GPS in Adama, Oromia, Ethiopia, and required supporting data from the Municipality of the City. The density of population is the significant input in calculating the standards for services-based location-allocation. Therefore, road networks, existing healthcare facilities, and population density are the critical parameters considered to identify the new healthcare centers to be established to cater to the people's needs. In addition, land use/cover classes and settlement location, proximity to the existing healthcare centers are also considered in GIS analysis for better results. The study identified eight suitable sites to develop healthcare centers in the city. Therefore, the government can utilize the recommendations for infrastructure development planning to improve healthcare facilities based on the accessibility.

GIS METHODOLOGICAL APPROACH TO DEVELOPING AND FORMING A VISUAL IMAGE OF DOWNTOWN AMMAN

Introduction: Amman’s growth expansion was shaped by its topography (valleys and hills), which generated a special character and patterns of the urban form. The city was built on seven hills, which are connected by steep stairs that serve as pathways to the downtown core. The rapid urbanization caused by uncontrolled population growth, as well as random and unplanned formations that were far from following any kind of aesthetic, resulted in the expansion of the center towards the surrounding seven hills. The continuous intersection of these hills caused their boundaries to blur and melt. As a result of the intense transformation, which is causing the hills’ appearance to become more uniform, their continuing overlap may completely destroy the unique character and imageability of downtown Amman. Methods: This research made use of topographical context analysis and the Geographical Information System (GIS) as well as its 3D modeling function to identify the optimal visibility and viewing angle when looking from the valley towards the hills and between the hills. Results and discussion: We identified the main vistas, i.e., highly significant views, which could subsequently be adopted as the locations for proposing landmark scenarios via 3D model GIS analysis. The projection process helped determine the significant views from the valley towards the hills and between the hills. Overall, three scenarios were proposed for the landmarks, out of which, the best one was chosen. It corresponds to the topographical analysis and the community needs, depends upon the local context and urban fabric, and is optimal for decision-making.

A Step-by-Step Guide for Automated Plant Canopy Delineation Using Deep Learning: An Example in Strawberry Using ArcGIS Pro Software

This publication presents a guide to image analysis for researchers and farm managers who use ArcGIS software. Anyone with basic geographic information system analysis skills may follow along with the demonstration and learn to implement the Mask Region Convolutional Neural Networks model, a widely used model for object detection, to delineate strawberry canopies using ArcGIS Pro Image Analyst Extension in a simple workflow. This process is useful for precision agriculture management.

Spatial distribution and main controlling factor of cadmium accumulation in agricultural soils in Guizhou, China

A large-scale investigation was conducted on the cadmium (Cd) content in the farmland soils of Guizhou to explore the spatial variation in soil Cd content, identify the main factors responsible for causing Cd pollution, and determine the zonation of Cd pollution. Multivariate statistical analysis, geographic information system (GIS) analysis, and decision tree methods were used to study the distribution, spatial variation, and pollution partitioning of Cd and the factors influencing the Cd accumulation in agricultural soils of the Guizhou province. Areas with high Cd content in agricultural soil were found to be concentrated in the high-altitude areas in the western region of Guizhou province. The results of the single factor pollution index showed that the proportion of sample sites with Cd class I (priority protection), II (security utilization), and III (strict control) in the agricultural soils of Guizhou province were 65.96%, 31.27%, and 2.77%, respectively. In high-altitude areas, the Cd content in the agricultural soils was mainly derived from the soil parent material. In contrast, mining activities and road traffic were the main factors Cd accumulation in agricultural soils in lower altitude areas.

Odor-aided analysis for landfill site selection: study of DOKAP Region, Turkey.

In our world, with the increase of factors such as the rapid and irresponsible consumption of natural resources, man-made environmental disasters, global warming, and pollution of water resources in our world, the need for more efficient storage and disposal of solid waste has arisen. The presentation of the data required to solve spatial problems such as storage, management, and location selection can be carried out extensively and effectively using geographic information systems (GIS). On the other hand, the unsatisfactory results obtained with GIS recently have made it mandatory to use spatial multiple criteria decision-making (S-MCDM) methods that include the decision-makers in the process. In this study, landfill site selection was carried out in eight provinces in the region under the responsibility of the Eastern Black Sea Project Regional Development Administration (DOKAP). GIS and S-MCDM were used together in this site selection process. A total of eight spatial data layers were used in the site selection application. Afterwards, storage areas determined as suitable via GIS analysis underwent additional evaluation, taking into account geological, seismic, and environmental factors as well as transportation costs. In addition to these multi-component evaluations, odor analyses were carried out on the proposed storage areas using the prevailing wind direction.