Geographic Information System Mapping Research Articles

Investigating the role of the built environment, socio-economic status, and lifestyle factors in the prevalence of chronic diseases in Mashhad: PLS-SEM model

BackgroundChronic diseases remain a significant contributor to both mortality and disability in our modern world. Physical inactivity and an unhealthy diet are recognized as significant behavioral risk factors for chronic diseases, which can be influenced by the built environment and socio-economic status (SES). This study aims to investigate the relationship between the built environment, SES, and lifestyle factors with chronic diseases.MethodsThe current study was conducted in Mashhad’s Persian cohort, which included employees from Mashhad University of Medical Sciences (MUMS). In the study, 5,357 participants from the cohort were included. To assess the state of the built environment in Mashhad, a Geographic Information System (GIS) map was created for the city and participants in the Persian Mashhad study. Food intake and physical exercise were used to assess lifestyle. A food frequency questionnaire (FFQ) was used to assess food intake. To assess food intake, the diet quality index was computed. To assess the link between variables, the structural model was created in accordance with the study’s objectives, and partial least square structural equation modeling (PLS-SEM) was utilized.ResultsThe chronic diseases were positively associated with male sex (p < 0.001), married (p < 0.001), and higher age (p < 0.001). The chronic diseases were negatively associated with larger family size (p < 0.05), higher SES (p < 0.001), and higher diet quality index (DQI) (p < 0.001). No significant relationship was found between chronic disease and physical activity.ConclusionFood intake and socioeconomic status have a direct impact on the prevalence of chronic diseases. It seems that in order to reduce the prevalence of chronic diseases, increasing economic access, reducing the class gap and increasing literacy and awareness should be emphasized, and in the next step, emphasis should be placed on the built environment.

The Jarring Reality: Adolescent and Pediatric Gunshot Trauma on the Rise in a Major Urban Institution.

Firearms are the leading cause of death in the pediatric population, and the incidence of firearm injury is on the rise in the United States. The purpose of this study was to examine the incidence of pediatric gunshot wounds (GSWs) in New Orleans from 2012 to 2023 and evaluate factors that contribute to mortality. A retrospective analysis of pediatric GSWs was conducted using the trauma database at a Level I trauma center in New Orleans, Louisiana. All patients aged 0 to 18 years with a GSW between January 1, 2012, and January 1, 2023, were evaluated. Demographic data, Injury Severity Score (ISS), hospital length of stay (LOS), discharge disposition, intent of injury, mortality, firearm type, orthopaedic injuries, and related surgical procedures were extracted from the database. Linear regression was used to assess the trend of GSWs over time, and logistic regression was used to identify variables that predicted mortality. Statistical significance was defined as α = 0.05. Geographic information system (GIS) mapping was conducted using the ZIP code location of injury to identify geographic areas with the greatest number of GSWs. A significant increasing trend was observed in the number of pediatric GSWs when adjusted for changes in population (P = 0.014), and the number of GSWs increased 43.2% over the duration of the study. Higher ISS, shorter length of stay, and self-inflicted intent of injury were notable predictors of mortality. Three hundred fifty-four patients (31.4%) had at least one orthopaedic injury, and of the survivors, 365 patients (35.6%) required at least one surgical procedure in the operating room during their admission. The number of GSWs in the pediatric population in New Orleans presenting at a large level 1 trauma center demonstrated a statistically significant increasing trend over the past 11 years. ISS and self-inflicted intent were predictive of mortality within this pediatric patient population.

Perception and Belief of Healthcare Workers regarding the COVID-19 Vaccine: A Cross-Sectional Study and Geographic Information System Mapping

The COVID-19 new deaths’ highest numbers in Southeast Asia were reported from Indonesia, with 8,784 new deaths. Healthcare workers (HCWs) have several roles in the COVID-19 vaccine implementation. This study aimed to assess COVID-19 vaccine perception and belief among HCWs and identify demographic mapping by using the geographical information system. This study used a quantitative method with cross-sectional design, wherein 112 registered HCWs in the Indonesian hospital and healthcare centre were shared a survey questionnaire using Google Forms® and then visualised it through geographic mapping. The perception of HCWs regarding the COVID-19 vaccine was considered good but in contrast to the belief. There was a significant relationship between age [ p value = .021; odds ratio (OR): 2.692; 95% confidence interval (CI): 1.229–5.896] and also HCWs’ workplace ( p value = .040; OR: 0.231; 95% CI: 0.0620–0.862) towards the belief of HCWs regarding the COVID-19 vaccine. Developing strategies to decrease HCWs and public hesitation and increase trust is vital for implementing vaccination programmes.

Geographic Information System Mapping Tool for Rainwater Harvesting in the United States

Geographic Information System Mapping Tool for Rainwater Harvesting in the United States

Data Management System - Water Supply and Channeling with GIS Mapping

GIS mapping enables visualizing spatial data related to water sources, distribution networks, infrastructure assets, and other pertinent information crucial for effective management. Asset management functionalities enable tracking the condition, maintenance schedules, and lifecycle of water infrastructure assets, ensuring optimal utilization and performance. This paper presents the design and implementation of an integrated data management system tailored for water supply and channeling, augmented with Geographic Information System (GIS) mapping capabilities. The system aims to streamline the management of water resources, optimize distribution networks, and enhance decision making processes for water utility providers and stakeholders. The system architecture comprises several key components: GIS mapping, data collection mechanisms, database management, sensor integration, network analysis tools, asset management functionalities, decision support systems, remote monitoring and control capabilities, and user interfaces. Data collection mechanisms encompass various sources such as sensors, surveys, satellite imagery, and existing databases to gather comprehensive datasets on water sources, quality parameters, infrastructure condition, and demographic factors. A robust database management system is employed to efficiently store, manage, and secure the collected data, facilitating easy retrieval and analysis. Real-time sensor data from water meters, flow meters, and pressure sensors are integrated into the system to provide insights into water usage patterns, network performance, and system health. Network analysis tools utilize GIS capabilities to optimize the layout of distribution networks, identify areas for expansion, and improve operational efficiency Remote monitoring and control capabilities empower operators to monitor and manage water systems from a centralized location, enabling proactive maintenance, leak detection, and emergency response. Security measures are implemented to safeguard sensitive water infrastructure data against unauthorized access, cyber threats, and data breaches, ensuring the integrity and confidentiality of the system. Overall, the integrated data management system presented herein offers a comprehensive solution for optimizing water supply and channeling operations, enhancing efficiency, reliability, and sustainability while minimizing costs and environmental impact

Agro-morphological characterization and genotypic diversity of chilli (Capsicum. frutescens) landraces of Bangladesh

Samples of 82 chilli (Capsicum spp.) genotypes grown during the Rabi season of 2016-17 were characterized based on 28 qualitative and 7 quantitative agro-morphological markers. The genotypes were collected and conserved in the Plant Genetic Resources Centre (PGRC) of the Bangladesh Agricultural Research Institute (BARI). The Chi-square (χ 2) tests and the Shannon Diversity Index (H´) were used for the genetic diversity study. All the characters under study were distributed independently except cotyledon leaf shapes and leaf pubescence density. The values of H´ of the genotypes varied widely, the maximum values being observed for the cotyledon leaf shape, leaf density, and anthocyanin leaf spot on the fruit. In contrast, the lowest values were recorded for pigmentation of node and leaf color. The estimated coefficient of variation was the highest while it was the lowest for one thousand seed weights. No variation was observed for calyx annular constriction of fruit. Analytical functions of DIVA-GIS (Geographic Information System) mapping localized the accessions into 10 districts and marked the areas of insufficient collection. Accession AMA-248 belonging to Mymensingh was almost round fruit. Accessions RAI-232 and RAI-80 belonging to Chattogram, AMA-335 belonging to Sherpur, AMA-361 belonging to Jamalpur, RAI-80 belonging to Chattogram, and AHI-01 belonging to Jhenaidah were elongated fruit. Germplasms collected from Chattogram showed the maximum variation in traits of cotyledon leaf color and stem color at the seedling transplanting age. The variation observed in different characters could be important characters in selecting parents for breeding programs.

ENVIRONMENTAL SUSTAINABILITY IN CROSS-BORDER ENERGY TRADE: THE CASE OF THE KAZAKHSTAN – CHINA PIPELINE

The article delves into the nuanced realm of environmental sustainability in the context of the KazakhstanChina pipeline – a crucial conduit for oil and gas resources spanning borders. Rigorous methodologies, including Geographic Information System (GIS) mapping and community engagement, unveil the environmental challenges intrinsic to cross-border energy projects. From habitat disruption and pollution to the potential for oil spills, the negative impacts demand strategic mitigation. The article advocates for a multifaceted approach, recommending the incorporation of advanced technologies, strategic route planning, and diversified energy sources. Emphasizing community engagement, transparency, and international cooperation, the proposed sustainable practices aim to balance the imperatives of economic growth with ecological preservation. As the world grapples with increasing energy demands, the Kazakhstan-China pipeline stands as a poignant case study, prompting a collective reevaluation of our approach to global energy trade. Through proactive measures and collaborative efforts, it is possible to navigate the delicate equilibrium between energy security and environmental stewardship, fostering a future where sustainable practices underscore the foundation of cross-border energy endeavors.

Sources apportionments of heavy metal(loid)s in the farmland soils close to industrial parks: Integrated application of positive matrix factorization (PMF) and cadmium isotopic fractionation

Understanding the source identification and distribution of heavy metal(loid)s in soil is essential for risk management. The sources of heavy metal(loid)s in farmland soil, especially in areas with rapid economic development, were complicated and need to be explored urgently. This study combined geographic information system (GIS) mapping, positive matrix factorization (PMF) model and cadmium (Cd) isotope fingerprinting methods to identify heavy metal(loid) sources in a typical town in the economically developed Yangtze River Delta region of China. Cd, As, Cu, Zn, Pb, Ni and Co in different samples were detected. The results showed that Cd was the most severely contaminated element, with an exceedance rate of 78.0 %. GIS mapping results indicated that the hotspot area was located in the northeastern area with prolonged operational histories of electroplating and non-ferrous metal smelting industries. The PMF model analysis also identified emissions from smelting and electroplating enterprises as the main sources of Cd in the soil, counted for 49.28 %, followed by traffic (25.66 %) and agricultural (25.06 %) sources. Through further isotopic analysis, it was found that in soil samples near the industrial park, the contribution of electroplating and non-ferrous metal smelting enterprises to cadmium pollution was significantly higher than other regions. The integrated use of various methodologies allows for precise analysis of sources and input pathways, offering valuable insights for future pollution control and soil remediation endeavors.

GIS-MCE based suitability analysis for sustainable estate development in Ede North LGA Osun State, Nigeria

Ideal areas for housing developments that are, for the most part, sustainable present critical issues in the planning and development of eco-friendly urban settlements. With this in mind and explicitly, Ede North LGA of Osun State was chosen as the case study, where a methodological framework for a land suitability analysis was adopted to identify suitable lands for real estate development with sustainability as a focus in the realization of global UN goals. This present study falls within the framework of the Multi-Criteria Evaluation (MCE) analysis, which can be integrated into Geographic Information Systems (GIS) together with the Analytic Hierarchical Process (AHP), useful for decision-making in site selection studies. For this purpose, computed relative weights from the pairwise comparisons (AHP) of 5 sub-models, i.e., terrain, environmental, socio-economic, available land, and soil factors, were inserted into the suitability analysis function of GIS and combined in a ‘Weighted Sum’ overlay operation to produce a final suitability map on a scale of 1–5, with 5 being the highest preference. Lastly, the ‘Locate Region’ Spatial Analyst tool was applied to the final suitability map to locate three (3) optimum locations to site the estate developments. Conclusively, in view of the analysis and discoveries made in this research, finding optimum locations via the GIS-MCE-based land suitability model for future housing development needs may prove highly resourceful. Hence, this study can likewise give a significant direction to future land use changes and practical and cost-effective arrangements in urban areas where conditions are similar to those in Ede North LGA.

Integrating socio-economic vulnerability factors improves neighborhood-scale wastewater-based epidemiology for public health applications

Wastewater Based Epidemiology (WBE) of COVID-19 is a low-cost, non-invasive, and inclusive early warning tool for disease spread. Previously studied WBE focused on sampling at wastewater treatment plant scale, limiting the level at which demographic and geographic variations in disease dynamics can be incorporated into the analysis of certain neighborhoods. This study demonstrates the integration of demographic mapping to improve the WBE of COVID-19 and associated post-COVID disease prediction (here kidney disease) at the neighborhood level using machine learning. WBE was conducted at six neighborhoods in Seattle during October 2020 – February 2022. Wastewater processing and RT-qPCR were performed to obtain SARS-CoV-2 RNA concentration. Census data, clinical data of COVID-19, as well as patient data of acute kidney injury (AKI) cases reported during the study period were collected and the distribution across the city was studied using Geographic Information System (GIS) mapping. Further, we analyzed the data set to better understand socioeconomic impacts on disease prevalence of COVID-19 and AKI per neighborhood. The heterogeneity of eleven demographic factors (such as education and age among others) was observed within neighborhoods across the city of Seattle. Dynamics of COVID-19 clinical cases and wastewater SARS-CoV-2 varied across neighborhood with different levels of demographics. Machine learning models trained with data from the earlier stages of the pandemic were able to predict both COVID-19 and AKI incidence in the later stages of the pandemic (Spearman correlation coefficient of 0·546 - 0·904), with the most predictive model trained on the combination of wastewater data and demographics. The integration of demographics strengthened machine learning models' capabilities to predict prevalence of COVID-19, and of AKI as a marker for post-COVID sequelae. Demographic-based WBE presents an effective tool to monitor and manage public health beyond COVID-19 at the neighborhood level.

Morphometric Analysis of Dachigam Drainage Basin Using Geo-Spatial Technology (GST)

The examination of morphometric parameters through quantitative analysis proves highly valuable in assessing river basins, prioritizing watersheds for soil and water conservation, and natural resources management. In this study, an in-depth analysis of the Dachigam Catchment has been conducted, focusing on the quantification of various morphometric parameters. The research employs Geographic Information System (GIS) techniques to assess morphometric parameters in the micro-watersheds of the Dachigam Catchment. Linear and areal morphometric parameters were systematically derived and tabulated based on linear and shape characteristics of drainage channels, utilizing GIS and topographical maps (scale 1:50,000). The morphometric analysis conducted in the Dachigam catchment indicates that the basin has an elongated shape, suggesting that peak discharges would generally exhibit a flat profile and take time to rise. The drainage network within the basin is predominantly of the dendritic type, signifying a degree of homogeneity in texture. The slope analysis shows that the Dachigam watershed has a significant amount of steep terrain, ranging from 0 to 74.63%. The majority of the area falls within the 24 to 35% slope range. Examination of the aspect indicates that slopes oriented towards the south and southwest are the most prevalent in the basin. The watershed's mean bifurcation ratio is 1.71. This low ratio suggests that the watershed hasn't experienced significant distortion. In contrast, high bifurcation ratios exceeding 5 typically indicate distorted drainage patterns. The research illustrates how remote sensing and GIS methods can effectively analyze watershed morphometrics. These techniques offer valuable insights for planners and decision-makers involved in watershed-level planning and drainage basin management.

The Effect of Extreme Temperature and Air Pollutant on Cardiovascular Disease in Ondo-State Nigeria

This study investigates the relationship between extreme temperatures, air pollutants, and cardiovascular disease in Ondo State, Nigeria. Geographic Information System (GIS) satellite mapping techniques were utilized to analyze the spatial distribution of environmental parameters including methane, carbon monoxide, nitrogen dioxide, sulfur dioxide, and temperature across Ondo State. Data was obtained for the years 2020, 2021, and 2022 to allow for temporal analysis. The parameters exhibited some variations across different local government areas, indicating potential differences in exposure. 2020 data on cardiovascular disease and death cases revealed distinct spatial patterns, with northern regions showing higher disease burden compared to southern areas. The findings highlight the need for targeted public health interventions based on region-specific environmental and health profiles. Enhanced monitoring, continued research on emerging factors, and translation of evidence into policies and community initiatives is recommended to mitigate environmental risks and promote cardiovascular health across Ondo State. Overall, the study demonstrates the utility of GIS mapping for investigating complex connections between environmental factors and health outcomes to guide informed prevention strategies.

Integrating Landscape Character Assessment with Community Values in a Scenic Evaluation Methodology for Regional Landscape Planning

The Landscape Character Assessment (LCA) method from the UK has proven effective in identifying landscape values and characteristics through a comprehensive mapping process. However, it is predominantly expert-led and lacks an evaluation of scenery, hindering the inclusion of the broader community’s preferences and visual attachment to their landscape. In Australia, the application of the Scenic Amenity Methodology (SAM) using Geographical Information System (GIS) mapping has engaged communities but has often overlooked the importance of landscape character. To overcome these limitations, this study presents an innovative scenic assessment methodology, referred to as modified Scenic Amenity Methodology (modified SAM). The methodology establishes landscape character types (LCTs) to map scenic preference ratings derived from community photo surveys. Simultaneously, it incorporates the visual exposure of the landscape from publicly accessible viewpoints, modelled using a Digital Elevation Model (DEM). The combination of scenic preferences and visual exposure enables mapping of the scenic amenity values held by the community. This methodology was first trialled in Bundaberg, then Cairns, the Whitsunday Islands, and, most recently, Toowoomba in Queensland, Australia. This paper presents the results of the Toowoomba study and reports on the challenges and limitations of informing landscape character type (LCT) values through a public photo survey, developing a scenic preference map from ratings of photos across a region, a map of the visual exposure of landscape elements from key public viewing locations, and, ultimately, a map of scenic amenity values across the Toowoomba Region. It indicates that integrating previous LCA approaches with public participation through community preferences is indeed feasible for regional landscape planning.

Neighbourhood identity: a methodological perspective from Nantes-France

PurposeThis research aims to develop a method for defining the identity of multilayered neighbourhoods by taking a case study in Nantes/France. It utilizes the urban identity concept to achieve this goal, which is defined by physical and identificatory relation to the neighbourhood.Design/methodology/approachThe methodology includes historical periodical analysis, housing form and architectural stylistic definition, visualization and geographic information system (GIS) mapping. The research conducts spatial analysis to reveal the physical component of the urban identity of the neighbourhood and interviews (No = 50) with dwellers for the identificatory relation, asking about neighbourhood tangible/non-tangible elements. All these data are mapped through GIS.FindingsThe study found that the physical component is defined by three urban layers (identified as industrial, reconstruction and development, and post-industrial) and eleven housing typologies. As for the identificatory relation, the authors found that the interviewees mostly identified with their neighbourhood, whereas a minority did not. The most important form of identification with the neighbourhood was its atmosphere, and as reasons were given, the neighbourhood's positively evaluated quality, good location and social values.Originality/valueIt proposes the definition of the physical component through urban layers and housing typologies. The identificatory relation also considers the identification of the residents with the neighbourhood's tangible/non-tangible urban elements.

Opportunities for rail in the transport of carbon dioxide in the United States

The deployment of carbon management strategies like carbon capture and storage (CCS) and carbon dioxide removal (CDR) at scale will require significant investments in transport infrastructure to deliver CO2 to reliable storage. While pipeline transport has dominated the conversation due to economic reasons, there is increasing evidence that other modes may become viable alternatives when considering scale, regional opportunities, and social acceptance. This paper assesses the viability of rail for CO2 transport in the United States using market analysis, techno-economic assessment and geographic information systems mapping. We believe rail presents many advantages, notably in existing infrastructure with established right-of-ways, but also as an instrument to address unwanted effects of our energy transition, particularly in coal communities. We find that the strategic replacement of coal as a freight commodity could translate into 100 Mt/yr of CO2 movement by rail by 2050, and support up to 60,000 jobs in that industry. Further, we find that while rail pricing is notoriously volatile, there is strong support for rail being the least cost option over pipeline for volumes under 2 Mt CO2 per year, which aligns well with smaller, more risk-averse, and distributed carbon management projects that are scheduled to deploy over the next decade. Rail can also be an alternative in regions where CO2 pipeline projects have had limited success, like in the Midwest, where CO2 is captured from ethanol plants that are already serviced by rail networks. Likewise, rail can service roughly 25% of point-source CCS opportunities that are not proximal to projected trunk pipeline networks, of which 94% are located 1-mile from railroad. Finally, rail may be an integral part of CDR development in regions that are not coterminous with geologic storage, particularly in the Western and Northern US.

Hydroclimatic projection: statistical learning and downscaling model for rainfall and runoff forecasting

Abstract The study is carried out to investigate the surface runoff depth with changing precipitation due to climate change in the study area where sandy loam and loamy soil are dominant. In this study, future rainfall is projected by a statistical downscaling model (SDSM) using a set of predictors derived from a Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate model (GCM) [the Norwegian Earth System Model (NorESM)] with updated scenarios SSP 4.5 and SSP 8.5. Daily rainfall values for the observed period (1981 to 2014) are validated using statistical learning and evaluated with matrices, namely, root mean square error (RMSE), coefficient of correlation, and Nash–Sutcliffe efficiency (NSE), which are found to be valid for further predictions. Rainfall projections show a decrease in rainfall trend of 50% from 2030 to 2040 for scenario SSP 4.5 and an increase of 7% from 2040 to 2050. Predicted rainfall for scenario SSP 8.5 shows a similar trend of decreasing rainfall of 24% for the period 2030–2040 and an increase of 19% in the period 2040–2050. Furthermore, these rainfall values are spatially modelled in a geographic information system (GIS) and rainfall maps are obtained. The obtained rainfall map, land-use map, and soil map are overlaid to compute curve numbers and runoff depths. A similar trend of decrease in runoff is observed for the period 2030–2050. The overall trend of climate change shows a water-stressed scenario.

Impact of current anthropogenic activities on Blesbokspruit wetland microbiome and functions

Till present, natural wetlands have been continuously subjected to intensive pollution stress in recent years, mainly because of the rapidly growing industrialization and urbanization that are associated with a myriad of anthropogenic activities and land use practices. These man-made sources of pollution change the chemical properties of the natural wetlands, which in turn alter their microbial ecological biodiversity and functions. For the first time, the impact of the current anthropogenic activities and land use practices on the Blesbokspruit wetland chemical status and their consequential effect on the microbial structure and functions were investigated. Sites of high pollution intensity were identified using geographic information systems mapping (GISMapping) and the wetland microbiome and functional profile were studied through the use of high throughput shotgun metagenomics sequencing analysis. The predominant phyla that stemmed along the Blesbokspruit wetland were found to be Proteobacteria which was more dominant in water (93 %) than in the sediments (89 %), followed by firmicutes which was more abundant in sediments (9 %) than in water (6 %), and Bacteroidetes were relatively low in abundance within both the sediments (2 %) and the overlying water (1 %). The genera Klebsiella (70.4 %–28.2 %), Citrobacter (52.0 %–30.6 %), Escherichia (51.0 %–8.4 %), and Lynsinibacillus (9.3 %–1.5 %) were observed in most water and sediment samples. Within the six polluted sites, Site 2 was found to be the most highly polluted site in the Blesbokspruit wetland with very high COD (900 mg/L), TOC (11.60 mg/L), NO3− (39.74 mg/L), NO2− (12.64 mg/L), PO43 (4.14 mg/L), Fl− (143.88 mg/L), Cl− (145.95 mg/L) concentrations recorded in the water and high levels of TOC (0.37 mg/L), TC (6.92 %), TN (1.82 %), TS (0.53 %) in sediments. The microbial community structure and functions were found to be strongly influenced by the high organic content from the intense agricultural activities and sewage spillages and heavy metals from the mining activities nearby.

The Risk-Informed Asset-Centric (RIACT) Urban Resilience Enhancement Process: An Outline and Pilot-Case Demonstrator for Earthquake Risk Mitigation in Portuguese Municipalities

This study introduces the Risk-Informed Asset-Centric (RIACT) process, a novel approach aimed at enhancing the safety and resilience of urban infrastructures against natural and man-made disasters. The RIACT method stands out for its comprehensive analysis of potential disruptions to urban areas, encompassing their susceptibility to disasters, vulnerability factors, and recovery capabilities. This study’s primary focus is on Portugal’s 308 municipalities, assessing their preparedness and ability to recover from earthquake events. Methodologically, the study involved employing geographic information system (GIS) mapping to evaluate earthquake risks, combining data on regional risk areas, demographic and economic profiles, and resilience capacities. This approach provides a detailed understanding of each municipality’s strengths and areas in need of improvement, encouraging a collaborative framework for sharing and implementing best practices. The results reveal significant variations in earthquake resilience across different regions, highlighting the need for tailored resilience strategies. Conclusively, the RIACT method not only enhances the resilience of individual municipalities but also contributes to a broader national strategy for urban resilience, offering valuable insights for effective disaster risk management and recovery planning.

Use of GIS and BIM for the integration of tunnel design and construction process in conventional tunneling

This paper presents the development of a framework for the parametric design of tunnels using geographic information system (GIS) mapping and building information modelling (BIM). According to the framework, a parametric representation of each system component (e.g., layers of rock mass, size of excavation, topography, fault geometry, primary lining, secondary lining, rock bolts, etc.) can be incorporated in the GIS model with high levels of detail and used for the automatic generation of numerical models for the design of tunnel construction. This parametric evaluation allows the designer to consider several levels of detail for each component, for the efficient design and process optimization in conventional tunneling. Information between parametric analysis and numerical simulations can be freely exchanged with significantly reduced computational effort while results can be exported into BIM. It is anticipated that the proposed framework could lead to a comprehensive, yet efficient analysis of complex conventional tunneling project using parametric evaluations at the early design stages.

Geographic information system-aided evaluation of epidemiological trends of dengue serotypes in West Bengal, India.

West Bengal is a dengue-endemic State in India, with all four dengue serotypes in co-circulation. The present study was conceived to determine the changing trends of circulating dengue virus (DENV) serotypes in five consecutive years (2015-2019) using a geographic information system (GIS) during the dengue season in West Bengal, India. Molecular serotyping of dengue NS1 sero-reactive serum samples from individuals with ≤5 days of fever was performed using conventional nested reverse transcriptase-PCR. GIS techniques such as Getis-Ord Gi* hotspot analysis and heatmap were used to elucidate dengue transmission based on the received NS1-positive cases and vector data analysis was used to point out risk-prone areas. A total of 3915 dengue NS1 sero-positive samples were processed from most parts of West Bengal and among these, 3249 showed RNA positivity. The major circulating serotypes were DENV 3 (63.54%) in 2015, DENV 1 (52.79%) in 2016 and DENV 2 (73.47, 76.04 and 47.15%) in 2017, 2018 and 2019, respectively. Based on the NS1 positivity, dengue infections were higher in males than females and young adults of 21-30 yr were mostly infected. Getis-Ord Gi* hotspot cluster analysis and heatmap indicate that Kolkata has become a hotspot for dengue outbreaks and serotype plotting on maps confirms a changing trend of predominant serotypes during 2015-2019 in West Bengal. Co-circulation of all the four dengue serotypes was observed in this study, but only one serotype became prevalent during an outbreak. Representation of NS1-positive cases and serotype distribution in GIS mapping clearly showed serotypic shift in co-circulation. The findings of this study suggest the need for stringent surveillance in dengue-endemic areas to limit the impact of dengue and implement better vector-control strategies.