Geographic Information System Model Research Articles

Identification of cycle lane priorities based on observed and potential cyclist trips using GIS, the case of the Metropolitan Area of Mendoza, Argentina

ABSTRACT In Latin America and developing countries, urban planners often lack sufficient data on cyclists’ modal share and profile to establish effective cycling lane networks. Mendoza, Argentina, is such a city. This research aimed to identify adequate locations for new cycle lanes by speculating on potential cycle trips from observed patterns of existing cyclists for a coherent demand-based network. The study proposed a novel, simple, cost-effective Geographic Information Systems model that uses spatial and human data and presents results cartographically for policymakers. Statistical analysis of observed bicycle trips enabled constructing a local profile of cyclists, from which ‘potential trips’ were speculated: trips made by other modes, but which could have been biked given their distance, duration, purpose, and the traveler’s age. Geo-referencing observed and potential trips to their ‘sub-zones’ of origin and destination produced a migratory synthesis map highlighting general flows and densities. Superimposing this on the current cycling infrastructure exposed the critical gaps. As a broader aim, this geomatic method is readily transferable to other developing world settings. Actions for Mendoza and broader applications are proposed, significant limitations discussed, and further research suggested, noting issues of gender equity and safety.

Annual soil loss assessment of smallholder rubber growing lands in the Kalutara District, Sri Lanka using the RUSLE model in GIS

Soil loss resulting from various land management practices in traditional rubber-growing areas of Sri Lanka has been a major issue of concern and one of the factors responsible for declining rubber land productivity. There is no or limited information on the spatial variability of soil loss from rubber lands in Sri Lanka's traditional rubber growing areas. This constraint has had a significant impact on the effective management of soil conservation in rubber-growing lands. This study focuses on assessing the soil loss from the smallholder rubber-growing lands in the Kalutara District using remotely sensed satellite image-based Digital Elevation Model (DEM), rainfall grid data, and prepared soil maps with ground-level surveys by Natural Resource Management Centre (NRMC) Sri Lanka. The factors including rainfall, topography, land area and the crop-specific coefficient for the rubber-growing lands in the study area were analyzed using the Revised Universal Soil Loss Equation (RUSLE) and Geographic Information System (GIS). The study revealed that about 30% of smallholder rubber-growing lands fall under the risk to severe risk categories of soil loss while about 60% of rubber lands are under the low-risk category. About 8,500 ha of smallholder rubber lands can be categorized as risk to severe risk for soil loss whilst about 15,000 ha are under the low-risk category for soil loss. The findings of this study are useful in the implementation of an effective soil conservation management plan and has the potential in applying this methodological approach in other areas of Sri Lanka for various crops.

Assessing the Effects of Urbanization on Water Flow and Flood Events Using the HEC-HMS Model in the Wirynka River Catchment, Poland

Floods are among the most devastating natural disasters in small urban watersheds resulting in loss of life and enormous damage to property and posing a serious threat to the economy. Appropriate modeling can be a useful tool to prevent and reduce such flood risks. It is currently important to use hydrological models to assess and predict the water availability of river basins due to climate change to develop a strategy to cope with the changing environment. The study used an integrated approach combining a geographic information system (GIS) and hydrological modeling to assess the impact of urbanization on flash floods in peri-urban developing areas. The modeling was carried out for two spatial aspects relating to 1990 and 2018. The model allowed simulation of the river water flow that can occur under different rainfall probabilities. The study showed that rapid urbanization adversely affects hydrological processes. Reduced infiltration capacity increases water runoff, thereby increasing the risk of flooding or waterlogging. Therefore, it is extremely important to properly manage rainwater in the area.

Spatial variation of coastal wetland vulnerability to oil spill stress in the Bohai Sea

Due to their position at the land–sea interface, coastal wetlands are extremely vulnerable to oil spills. However, few studies have focused on the vulnerability of coastal wetlands to oil spill hazards. In this study, we developed a spatial vulnerability approach and assessment based on the vulnerability scoping diagram in the Chinese Bohai Sea, where oil spill events are serious and frequent, and aimed to assess the spatial heterogeneity of the vulnerability of coastal wetlands under oil spill stress by integrating various indicators for exposure, sensitivity, and adaptive capacity into a composite index. This study normalized and aggregated data across sub-indices and combined related information with a geographic information system (GIS) model. The weighted results indicated that oil spill sources, as well as the natural environment in which the wetlands were located, dominated the degree of vulnerability of various coastal wetlands. The vulnerability assessment results suggest that there are obvious spatial variations among the different wetlands surrounding the Bohai Sea and each wetland had a different level of vulnerability, and highlights the need to enhance adaptive capacity to reduce vulnerability. This paper provides a quantitative assessment method for determining the spatial variation of coastal wetland vulnerability under oil spill stress, which can be used to better understand potential oil spill risks in coastal wetlands to support spill prevention and improve response readiness in the future.

Geomatics-Based Modeling and Hydrochemical Analysis for Groundwater Quality Mapping in the Egyptian Western Desert: A Case Study of El-Dakhla Oasis

Groundwater is the single source of water in El-Dakhla Oasis, western desert, Egypt. The main objective of this study is an assessment of groundwater in the area for agriculture and drinking compared to Egyptian and World Health Organization criteria. Most the contamination of water in the study area comes from human and agricultural activities. Thirty soil profiles were studied in the area and we assessed soil quality. Seventy-four samples were taken from the area’s groundwater wells to assess the chemical characteristics of the groundwater. Moreover, the contamination of groundwater by farming and anthropogenic activities was assessed using a land use/land cover (LULC) map. Nine standard water criteria were determined to assess groundwater quality for agriculture. Furthermore, the resulting risk to human health and agricultural crops has been addressed. Therefore, the drinking quality of groundwater samples is graded as low as the hydrochemical study showed high TH, EC, TDS, Ca2+, Mg2+, Mn2+, and Fe2+ contents of 40.5%, 2.7%, 1.4%, 3.8%, 1.6%, 86.5%, and 100%, respectively. Human health is risked by drinking this water, which negatively affects hair, skin, and eyes, with greatest exposure to enteric pathogens. Using these criteria, the majority of groundwater samples cause harmful effects on soil types and are toxic to sensitive crops (vegetable crops). In conclusion, the output of this research is a map showing groundwater suitable for consumption and agriculture in El-Dakhla Oasis based on all indices using the Geographic Information Systems (GIS) model. Additionally, there was evidence of a linear relationship between soil quality and irrigation water quality (R2 = 0.90). This emphasis on tracking changes in soil/water quality was brought on by agricultural practices and environmental variables.

A comparative modeling of landslides susceptibility at a meso-scale using frequency ratio and analytic hierarchy process models in geographic information system: the case of African Alpine Mountains (Rif, Morocco)

A comparative modeling of landslides susceptibility at a meso-scale using frequency ratio and analytic hierarchy process models in geographic information system: the case of African Alpine Mountains (Rif, Morocco)

Phytochemical Analysis and Habitat Suitability Mapping of Cardiocrinum cordatum (Thunb.) Makino Collected at Chiburijima, Oki Islands, Japan.

Cardiocrinum cordatum, known as ubayuri in Japan, has antihypertensive properties and has been shown to inhibit angiotensin-converting enzyme (ACE), which contributes to the production of angiotensin II, a hypotensive substance in the renin−angiotensin system. C. cordatum has been the subject of various studies as a useful plant and is applied as a functional food. Due to the limited distribution, loss of natural habitat by frequent natural disasters, and environmental conditions, the chemical content and biological activity of C. cordatum have been drastically affected. Obtaining a stable supply of Cardiocrinu cordatum material with high biological activity is still a challenge. Understanding the native habitat environment and suitable cultivation sites could help in solving this issue. Therefore, in the current study we investigated the effect of environmental parameters on the hypertensive and antioxidant activities of C. cordatum collected at Chiburijima, Oki Islands, Shimane Prefecture, Japan. We also predicted the habitat suitability of C. cordatum using a geographic information system (GIS) and MaxEnt model with various conditioning factors, including the topographic, soil, environmental, and climatic factors of the study area. A total of 37 individual plant samples along with soil data were collected for this study. In vitro assays of ACE inhibitory and antioxidant activity were conducted on the collected samples. The results show that plants at 14 out of 37 sites had very strong ACE inhibitory activity (IC50 < 1 mg mL−1). However, the collected plants showed no signs of strong antioxidant activity. Statistical analysis using analysis of variance (ANOVA) showed that BIO05 (F value = 2.93, p < 0.05), nitrate−nitrogen (F value = 2.46, p < 0.05), and silt (F value = 3.443, p < 0.05) significantly affected ACE inhibitory activity. On the other hand, organic carbon content (F value = 10.986, p < 0.01) was found to significantly affect antioxidant activity. The final habitat suitability map shows 3.3% very high and 6.8% high suitability regions, and samples with ACE inhibition activity were located within these regions. It is recommended further investigations and studies are conducted on C. cordatum in these locations. The prediction suitability model showed accuracy with AUC-ROC of 96.7% for the study area.

GIS-Based Approach Applied to Study of Seasonal Rainfall Influence over Flood Vulnerability

Flooding occurrence is one of the most common phenomena that impact urban areas, and this intensifies during heavy rainfall periods. Knowing the areas with the greatest vulnerability is of paramount importance as it allows mitigating actions to be implemented in order to minimize the generated impacts. In this context, this study aimed to use Geographic Information System (GIS) tools to identify the areas with greater flooding vulnerability in Espírito Santo state, Brazil. The study was based on the following methodological steps: (1) a Digital Elevation Model (DEM) acquisition and watersheds delimitation; (2) maximum and accumulated rainfall intensity calculations for the three studied periods using meteorological data; (3) a land use and occupation map reclassification regarding flood vulnerability and fuzzy logic application; (4) an application of Euclidean distance and fuzzy logic in hydrography and water mass vector variables; (5) a flood vulnerability model generation. Based on the found results, it was observed that the metropolitan and coastal regions presented as greater flood vulnerability areas during the dry season, as in these regions, almost all of the 9.18% of the state’s area was classified as highly vulnerable, while during rainy season, the most vulnerable areas were concentrated in Caparaó and in the coastal and immigration and metropolitan regions, as in these regions, almost all of the 12.72% of the state’s area was classified as highly vulnerable. In general, by annually distributing the rainfall rates, a greater flood vulnerability was observed in the metropolitan and coastal and immigration regions, as in these areas, almost all of the 7.72% of the state’s area was classified as highly vulnerable. According to the study, Espírito Santo state was mostly classified as a low (29.15%) and medium (28.06%) flood vulnerability area considering the annual period, while its metropolitan region has a very high flood vulnerability risk. Finally, GIS modeling is important to assist in decision making regarding public management and the employed methodology presents worldwide application potential.

Abstract 270: Eligibility Of Out-of-hospital Cardiac Arrest Patients For Extracorporeal Cardiopulmonary Resuscitation In The United States: A Geographic Information System Model

Introduction: Recent evidence suggest that ECPR may improve survival rates for nontraumatic out-of-hospital cardiac arrest (OHCA). Eligibility criteria for ECPR are often based on patient age, clinical variables, and facility capabilities. Expanding access to ECPR across the U.S. requires a better understanding of how these factors interact with transport time to ECPR centers. Methods: We constructed a Geographic Information System (GIS) model to estimate the number of ECPR candidates in the U.S. We utilized a Resuscitation Outcome Consortium (ROC) database to model time-dependent rates of ECPR eligibility and the Cardiac Arrest Registry to Enhance Survival (CARES) registry to determine the total number of OHCA patients who meet pre-specified ECPR criteria within designated transportation times. Potential ECPR centers were categorized as Extracorporeal Membrane Oxygenation (ECMO)-ready, ECMO-capable, or percutaneous coronary intervention (PCI)-capable centers using data from the Extracorporeal Life Support Organization and the American Hospital Directory. The combined model was used to estimate the total ECPR candidates and the effects of patient and hospital factors on ECPR eligibility. Results: There were 588,203 OHCA patients in the CARES registry from 2013 to 2020. After applying clinical eligibility criteria, 22,104 (3.76%) OHCA patients were eligible for ECPR. The rate of ROSC increased with longer resuscitation time, which resulted in fewer ECPR candidates. The proportion of OHCA patients eligible for ECPR increased with older age cutoffs. Only 1.68% (9,889/588,203) of OHCA patients in the U.S. were eligible for ECPR based on a 45-minute transportation time to an ECMO-ready center model, representing the current state of ECPR in the U.S. The proportion of OHCA patients eligible for ECPR ranged from 0.68% (4,027 patients in the 15-minute transportation time to ECMO-ready center model) to 2.81% (16,558 patients in the 45-minute transportation time to PCI-capable center model). Conclusions: Less than 2% of OHCA patients are eligible for ECPR in the U.S. GIS models can identify the impact of clinical criteria, transportation time, and hospital capabilities on ECPR eligibility to inform future implementation strategies.

Principal Component Analysis (PCA)–Geographic Information System (GIS) Modeling for Groundwater and Associated Health Risks in Abbottabad, Pakistan

Drinking water quality is a major problem in Pakistan, especially in the Abbottabad region of Pakistan. The main objective of this study was to use a Principal Component Analysis (PCA) and integrated Geographic Information System (GIS)-based statistical model to estimate the spatial distribution of exceedance levels of groundwater quality parameters and related health risks for two union councils (Mirpur and Jhangi) located in Abbottabad, Pakistan. A field survey was conducted, and samples were collected from 41 sites to analyze the groundwater quality parameters. The data collection includes the data for 15 water quality parameters. The Global Positioning System (GPS) Essentials application was used to obtain the geographical coordinates of sampling locations in the study area. The GPS Essentials is an android-based GPS application commonly used for collection of geographic coordinates. After sampling, the laboratory analyses were performed to evaluate groundwater quality parameters. PCA was applied to the results, and the exceedance values were calculated by subtracting them from the World Health Organization (WHO) standard parameter values. The nine groundwater quality parameters such as Arsenic (As), Lead (Pb), Mercury (Hg), Cadmium (Cd), Iron (Fe), Dissolved Oxygen (DO), Electrical Conductivity (EC), Total Dissolved Solids (TDS), and Colony Forming Unit (CFU) exceeded the WHO threshold. The highly exceeded parameters, i.e., As, Pb, Hg, Cd, and CFU, were selected for GIS-based modeling. The Inverse Distance Weighting (IDW) technique was used to model the exceedance values. The PCA produced five Principal Components (PCs) with a cumulative variance of 76%. PC-1 might be the indicator of health risks related to CFU, Hg, and Cd. PC-2 could be the sign of natural pollution. PC-3 might be the indicator of health risks due to As. PC-4 and PC-5 might be indicators of natural processes. GIS modeling revealed that As, Pb, Cd, CFU, and Hg exceeded levels 3, 4, and 5 in both union councils. Therefore, there could be greater risk for exposure to diseases such as cholera, typhoid, dysentery, hepatitis, giardiasis, cryptosporidiosis, and guinea worm infection. The combination of laboratory analysis with GIS and statistical techniques provided new dimensions of modeling research for analyzing groundwater and health risks.

GEO-Accumulation Index for Heavy Metals in Groundwater: a Study of Bompai and Sharada Industrial Estates, Kano Metropolis, Nigeria

The level of concentration of heavy metal in soil is detrimental to groundwater quality in Sharada and Bompai industrial areas. A total of 40 sampling points were selected from both two areas. The digested samples were analysed for As, Cd, Cr, Cu, Co, Ni and Pb using Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-OES) machine. Geographic Information Systems (GIS) was used to model and present the spatiotemporal changes of the pollution sources and factors affecting the levels of pollution. Pollution in water and potential risks introduced by heavy metal accumulation were assessed using Geo-accumulation index. Results indicated that Cadmium and Arsenic are the most vulnerable around Bompai with mean of 0.2-0.4/mg/l and 1.3-1.6 mg/l respectively, Geo-accumulation index mapping showed that Arsenic, Cadmium and Copper to have high accumulation index ranging from strong to very strong (0.99-1.48), (0.01-0.22) and (0.27-0.33) respectively, Cobalt to have moderate contamination in Sharada (wet season) with (-0.39-0.12) while lead and Chromium has the least contamination index (uncontaminated). GIS modelling and mapping indicated that all the heavy metals were found in the groundwater of the two areas but among them Cd is more concentrated. South-western part of the study area confronts the most serious heavy metal pollution, hence the need for groundwater treatment before drinking to avoid dangers associated with heavy metals.

Cascade Reservoirs: An Exploration of Spatial Runoff Storage Sites for Water Harvesting and Mitigation of Climate Change Impacts, Using an Integrated Approach of GIS and Hydrological Modeling

Torrents play an essential role in water resources through rainfall in arid to semi-arid mountainous regions, serving large populations worldwide, and are also crucial in maintaining the downstream environment. The natural flows (floods, ephemeral flows) in arid hill regions result in potential hydrological fluctuations caused by climate change. However, the feasibility of eventual storage in remote hilly catchments would force a more sudden change. The current study was conducted in the lower part of the Khirthar National Range in the Sindh province of Pakistan, with the aim to explore spatial runoff storage sites for sustainable development to mitigate the impacts of climate change in arid areas. In total, 83 years of precipitation data were used to estimate water availability, along with satellite imagery for LULC pre- and post-monsoon conditions, delineation of watersheds, and identification of potential runoff storage locations and return periods, using Remote Sensing (RS)/Geographical Information System (GIS) 10.5.1, HEC-HMS 3.1, and Origin Pro 9.0 for statistical approaches. The model delineated two potential watersheds: Goth Sumar, covering an area of 61.0456 km2, wherein ten cascading reservoirs were identified, and Goth Baro, covering an area of 14,236 km2, wherein two cascading reservoirs were identified. Different storage capacities were determined for the cascade-type reservoirs. The maximum live volumetric potential storage of the reservoirs varies from 0.25 to 1.32 million cubic meters (MCM) in the villages of Baro and Sumar. The return periods have been estimated at 5, 10, 20, 25, 50, and 75 years, corresponding to 12.35, 16.47, 21.43, 21.72, 25.21, and 40.53 MCM for Goth Sumar, while Goth Baro’s storage capacity has been estimated at 2.88, 3.84, 5.00, 5.06, 5.88, and 9.45 MCM, respectively. All results obtained were authenticated using accuracy assessment, validation, and sensitivity analysis. The proposed potential storage sites were recommended for a planning period of five years. The live storage capacity of the identified cascade reservoirs can be improved by raising the marginal banks and developing the spillways to control inlet and outlet flow in order to maintain internal pressure on the reservoir banks. The stored water can be used for climate-friendly agricultural activities to increase crop production and productivity. The proposed study area has extensive experience with flood irrigation systems and rainwater harvesting to sustain agriculture due to rainfall being the only water resource (WR) in the region. However, the study area has enormous potential for surface runoff WRs, especially during the rainy season (monsoon); the current 2022 monsoon is showing flooding. The modeling approaches of Remote Sensing, GIS, and HEC-HMS play an important role in delineating watershed areas, developing hydrographs, and simulating water availability for different return periods by minimizing cost and time.

Spatio–Temporal Variability Characteristics of Coastal Soil Salinization and Its Driving Factors Detection

The utilization efficiency of land resources in the coastal area of the Yellow River Delta has been deeply affected by salinization hazards. Key to improvement of the utilization efficiency of resources in this area is to grasp the spatio–temporal variability law of soil salinity and identify the driving factors of salinization. Wudi County in the coastal area of the Yellow River Delta is taken as the study area. Based on the data obtained from field measurements and laboratory analysis, the characteristics of soil salinity in spring and summer were analyzed by classical statistical methods; the spatial differentiation characteristics of salinization were analyzed from two–dimensional and three–dimensional perspectives using the geographic information system (GIS) and groundwater modeling system (GMS); the time variation characteristics of salinization were quantitatively analyzed by introducing the salinization severity index (Si) and the dominant index of salinization degree change (Ci). The results show that: (1) In the study area, the soil salinity of the surface layer (0–15 cm) in summer is lower than that in spring, but the sub–surface layer (15–30 cm), the middle layer (30–45 cm) and the bottom layer (45–60 cm) are all larger than the corresponding layers in spring, and the correlation between the soil salinity of each layer in summer is generally lower than that in spring. (2) In two–dimensional space, the areas with a surface soil salinity greater than 0.4% in both seasons are mainly located in the northern part of the study area; in three–dimensional space, the soil is mainly moderately salinized in both seasons, and the complexity of the distribution of the salt profile is higher in summer than in spring; (3) Mashanzi Town was the area most seriously affected by salinization in both seasons (Si values were greater than three); In the process of seasonal alternation, the dominant change type of salinized soil is from mild aggravation to moderate, with Ci value of 38.43%, followed by severe alleviation to moderate, with Ci value of 35.49%; (4) The driving factors of soil salinization in spring are mainly the soil salinity of the subsurface and middle layer, and soil water content; and in summer, mainly the soil salinity of subsurface layer, vegetation coverage and vegetation cover type. The interaction between any two factors has greater influence on the spatial variation of salinization than the corresponding single factor.

Smart power-to-gas deployment strategies informed by spatially explicit cost and value models

Green hydrogen allows coupling renewable electricity to hard-to-decarbonize sectors, such as long-distance transport and carbon-intensive industries, in order to achieve net zero emissions. Evaluating the cost and value of power-to-gas is a major challenge, owing to the spatial distribution and temporal variability of renewable electricity, CO2 and energy demand. Here, we propose a method, based on geographic information system (GIS) and techno-economic modeling, to: (i) compare the levelized cost and levelized value of power-to-gas across locations; (ii) identify potential hotspots for their future implementation in Switzerland; and (iii) set cost improvement targets as well as smart deployment strategies. Our method accounts for the spatial and temporal (both hourly and seasonal) availability of renewable electricity and CO2 sources, as well as the presence of gas infrastructure, heating networks, oxygen and gas demand centers. We find that only green hydrogen plants connected directly to run-of-river hydropower plants are currently profitable in Switzerland (with NPV per CAPEX ranging between 2.3-5.6). However, considering technological progress by 2050, a few green hydrogen plants deployed in the demand centers and powered by rooftop PV electricity will also become economically attractive. Moreover, a few synthetic methane plants connected to run-of-river hydropower plants currently show slight profitability (NPV per CAPEX reaching values up to 1.3) and in 2050 (NPV per CAPEX up to 3.1), whereas those connected to rooftop PV will remain uneconomical even in 2050. Based on our findings, we devise a long-term roadmap for policy makers and project developers to plan future green hydrogen projects. The proposed methodology, which is applied to Switzerland, can be extended to other countries.

Environmental productivity index GIS‐based model to estimate prickly pear biomass potential availability for biogas production

AbstractNowadays, climate change is the environmental issue facing the world. To reach the 2030 European Union goals, recently, biogas production through anaerobic digestion has developed significantly, by using alternative biomass sources due to the competition between food and no‐food products. In this regard, Opuntia ficus‐indica (OFI) has been suggested as a suitable new biomass for producing biomethane within the context of circular economy. In this study, a predictive methodology was applied by combining the Nobel model of environmental productivity index (EPI) and geographic information system (GIS), with the aim of estimating OFI biomass amount, as well as biogas and electricity potential production. The GIS analyses allowed the identification of the most suitable territorial areas for producing biogas from OFI, and an estimation of electricity production. The achieved results are highly valuable information for strategic planning of biogas sector development and could be relevant to the intervention priorities established by the EU.

Evaluation of urban sustainability based on GO-SRA: Case study of Ha-Chang and Mid-southern Liaoning urban agglomerations in northeastern China

With economic development and environmental degradation, more scholars have realized the importance of urban sustainability. Government of Northeastern China institutes policies to promote local sustainability, in which urban agglomerations are the core of regional development. This paper measured the sustainability of 20 cities in Ha-Chang Urban Agglomeration (HCUA) and Mid-southern Liaoning Urban Agglomeration (MSLUA) through 18 indicators selected from three dimensions of economy, society and environment. A growth-oriented sequential relationship analysis (GO-SRA) method was proposed to calculate indicator weights, which considers the growth rate and annual growth trend of indicators. In addition, the sustainability of the 20 cities was analyzed in detail with the Coupling Coordination Degree Model, Geographic Information System and Obstacle Degree Model. Results showed that these cities had good development trends, but their sustainability was not satisfactory. Although the level of coupling coordination among the three dimensions was on the rise, the improvement was not ideal. Environmental dimension had obvious advantages, but it lacked development momentum. Furthermore, we found that per capita actual utilization of foreign capital was the main obstacle factor to development. Finally, we proposed corresponding suggestions for sustainable development of these cities according to current situation.

Agroforestry potential of Kanchanpur District, Nepal using remote sensing and Geographic Information System

Abstract. Subedi PB, Mahara S, Paudel S , Bhandari J, Thagunna RS. 2023. Agroforestry potential of Kanchanpur District, Nepal using remote sensing and Geographic Information System. Asian J Agric 7: 65-74. Researchers are interested in agroforestry because it can reduce poverty and land degradation, mitigate climate change, and improve food security. This study aimed to determine the land potential for agroforestry in Kanchanpur District, Nepal, using Geographic Information System modeling concepts and a variety of ancillary (soil fertility) and satellite data (Digital Elevation Model, wetness, Normalized Difference Vegetation Index, and Land Use Land Cover) sets. It was accomplished by logically integrating various thematic layers in the GIS domain. For Kanchanpur District of Nepal's Sudurpaschim Province, agroforestry suitability maps showed that 76.14 percent was very high suitable, 3.12 percent was highly suitable, 13.89 percent was medium, 5.67 percent was low suitable, and 1.15 percent was very low suitable. The use of remote sensing and GIS to find suitable land for agroforestry have significant impacts, which will significantly aid in the study of agroforestry practices and the estimation of crucial factors for optimal productivity. Such analyses and results will undoubtedly assist agroforestry policymakers, and planners put it into practice and expanding in new areas. GIS modeling has enormous potential for land resource mapping, eventually contributing to the benefit of poor rural people, especially farmers, and helping ensure food and environmental security and a sustainable livelihood.

Eligibility of out-of-hospital cardiac arrest patients for extracorporeal cardiopulmonary resuscitation in the United States: A geographic information system model

BackgroundRecent evidence suggest that extracorporeal cardiopulmonary resuscitation (ECPR) may improve survival rates for nontraumatic out-of-hospital cardiac arrest (OHCA). Eligibility criteria for ECPR are often based on patient age, clinical variables, and facility capabilities. Expanding access to ECPR across the U.S. requires a better understanding of how these factors interact with transport time to ECPR centers. MethodsWe constructed a Geographic Information System (GIS) model to estimate the number of ECPR candidates in the U.S. We utilized a Resuscitation Outcome Consortium (ROC) database to model time-dependent rates of ECPR eligibility and the Cardiac Arrest Registry to Enhance Survival (CARES) registry to determine the total number of OHCA patients who meet pre-specified ECPR criteria within designated transportation times. The combined model was used to estimate the total number of ECPR candidates. ResultsThere were 588,203 OHCA patients in the CARES registry from 2013 to 2020. After applying clinical eligibility criteria, 22,104 (3.76%) OHCA patients were deemed eligible for ECPR. The rate of ROSC increased with longer resuscitation time, which resulted in fewer ECPR candidates. The proportion of OHCA patients eligible for ECPR increased with older age cutoffs. Only 1.68% (9,889/588,203) of OHCA patients in the U.S. were eligible for ECPR based on a 45-minute transportation time to an ECMO-ready center model. ConclusionsLess than 2% of OHCA patients are eligible for ECPR in the U.S. GIS models can identify the impact of clinical criteria, transportation time, and hospital capabilities on ECPR eligibility to inform future implementation strategies.

GIS-based assessment of land-agroforestry potentiality of Jharkhand State, India

Scaling-up agroforestry areas in various forms is a scientific path towards achieving various sustainable development goals (SDGs), especially improving livelihood, reducing poverty, conserving environment and biodiversity, and transforming climate change. In this study, the effort was made to investigate the land potentiality for agroforestry at the district level in Jharkhand State, India by applying geographic information system (GIS) modeling technology using climate (temperature and precipitation), topography (slope and elevation), ecology (percent tree cover and normalized difference vegetation index (NDVI)), and social economics (poverty rate and tribal dominance) factors. The results revealed that six districts of Jharkhand State had agroforestry potential greater than 60.00%. The highest agroforestry suitability was found in Simdega District (78.20%), followed by Pakur (76.25%), West Singhbhum (72.70%), Dumka (68.84%), Sahibganj (64.63%), and Godda (63.43%) districts. Additionally, we identified 513 out of 32,620 villages of Jharkhand State potentially suitable (agroforestry suitability ≥80.00%) for agroforestry with the objective of life improvement among marginalized society. Under the outside forest area, 8.58% of the total geographical land of Jharkhand State was wasteland, much of which was found suitable for agroforestry practices. The agroforestry setups in those wastelands can absorb 637 ​t carbon annually in long run and can provide direct economic benefits to the locals besides additional income for carbon emission reduction. This study concluded that Jharkhand State has plenty of high potential land for agroforestry, and adoption of agroforestry at the village level must be given high priority. This study could guide the nodal authorities to prepare appropriate strategies for scaling the tree cover in agroforestry systems in village-level landscape planning which needs policy attention and investment for achieving 9 out of the 17 SDGs.

Application for simulating public health problems during floods around the Loei River in Thailand: the implementation of a geographic information system and structural equation model

BackgroundFloods cause not only damage but also public health issues. Developing an application to simulate public health problems during floods around the Loei River by implementing geographic information system (GIS) and structural equation model (SEM) techniques could help improve preparedness and aid plans in response to such problems in general and at the subdistrict level. As a result, the effects of public health problems would be physically and mentally less severe.MethodsThis research and development study examines cross-sectional survey data. Data on demographics, flood severity, preparedness, help, and public health problems during floods were collected using a five-part questionnaire. Calculated from the population proportion living within 300 m of the Loei River, the sample size was 560 people. The participants in each subdistrict were recruited proportionally in line with the course of the Loei River. Compared to the empirical data, the data analysis examined the causal model of public health problems during floods, flood severity, preparedness, and help. The standardized factor loadings obtained from the SEM analysis were substituted as the loadings in the equations for simulating public health problems during floods.ResultsThe results revealed that the causal model of public health problems during floods, flood severity, preparation, and help agreed with the empirical data. Flood severity, preparedness, and aid (χ2 = 479.757, df = 160, p value <.05, CFI = 0.985, RMSEA = 0.060, χ2/df = 2.998) could explain 7.7% of public health problems. The computed values were applied in a GIS environment to simulate public health problem situations at the province, district, and subdistrict levels.ConclusionsFlood severity and public health problems during floods were positively correlated; in contrast, preparedness and help showed an inverse relationship with public health problems. A total of 7.7% of the variance in public health problems during floods could be predicted. The analysed data were assigned in the GIS environment in the developed application to simulate public health problem situations during floods.