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The Global Wind Atlas (GWA) provides high-resolution databases and maps of the wind resource for all land points and for water points within 200 km of the coastline, excluding Antarctica. The GWA is used to identify and understand the global, national, regional, and local potential for wind energy and to guide energy specialists, policymakers, and planners in the transition to a sustainable energy system. This information is vital to ensuring the growth of wind energy, helping to transition to a sustainable energy system, which will mitigate climate change and meet the world’s need for reliable, affordable, and clean energy. The GWA uses the established numerical wind atlas methodology to downscale coarse-resolution wind data to microscale, using linearized flow modeling and high-resolution topographic data. There have been three versions of the GWA, each using mesoscale model data at successively higher spatial resolution. A website and geographic information system (GIS) files support quick and in-depth analysis. Validation data and analysis, using measurements from tall masts located worldwide, are also provided through the web application. The development process of the GWA involves a dialogue between meteorological modelers, wind energy development experts, web designers, and representatives of the end users to provide accurate data in a dynamic and relevant way. This article outlines the general method, specific development, and application of the Global Wind Atlas.

The present study was conducted on the river Yamuna, which passes through Delhi-NCR from Baghpat to Chhainssa, a distance of about 125 km, at six sampling locations to evaluate the concentrations of heavy metals in surface water using heavy metal pollution index (HPI) approach. The river serves both urban-industrial and rural areas in the study area; hence, domestic, industrial, and agricultural wastes are being contributed greatly in the contamination of river water. The Yamuna River is one of the major tributaries of the river Ganga originated in the Himalayas and is flowing through a varied geological terrain. Metals such as iron (Fe), copper (Cu), cobalt (Co), zinc (Zn), lead (Pb), cyanide (CN), nickel (Ni), and chromium (Cr) in selected sites of Yamuna River water were determined by using atomic absorption spectrophotometer. The concentrations of Fe, Cu, Co, Zn, Pb, CN, Ni, and Cr in the river water were found to be in the range of 40–190, 50–120, 4–66, 840–1800, 2–40, 100–600, 88–253, and 35–52 μg/L, respectively. The results show that the maximum heavy metal content was found at sampling site S3 (Nizamuddin) followed by S6 (Chhainssa), S4 (Okhla), S1 (Baghpat), S5 (Manjhawali), and S2 (Pachahira). The heavy metal data was integrated in GIS environment for preparing spatial distribution maps of sampling sites. A scatter plot matrix was created to assess the pattern and interrelationships between heavy metals. The average concentration of heavy metals was recorded high, often exceeding the permissible limits for drinking of surface water prescribed by the Bureau of Indian Standards (BIS) and World Health Organization (WHO). Based on HPI (varies from 98.2 to 555.1), about 85% of the river water was classified as highly polluted; hence, it is not recommended for drinking. Overall, significant variations were observed in concentrations of heavy metals from one location to the other which may be because of toxic industrial effluents and domestic sewage wastes being added to the river water by various anthropogenic activities in the study area. The present work highlights the pollution load of heavy metals in the river Yamuna and also advocates an urgent attention towards minimizing the health risk of people residing not only along the river banks and surrounding regions but also for city population.

Popular navigation applications and services optimize routes based on either distance or time, disregarding drivers’ preferences when suggesting routes. Various unknown circumstances may affect users’ travel behaviors between two locations on the road network, hence it is complicated to provide satisfactory personalized route recommendations. In this paper, it is believed that users’ travel behaviors are implicitly reflected and can be learned from their historical Global Positioning System (GPS) trajectories. The Behavior-based Route Recommendation (BR2) method is proposed to compute personalized routes based exclusively on users’ travel preferences. The concepts of appearance and transition behaviors are defined to describe users’ travel behaviors. The behaviors are extracted from users’ past travels and the missing behaviors, of unvisited locations, are estimated with the Optimized Random Walk with Restart technique. Furthermore, the temporal dependency of travel behaviors is considered by constructing a time difference interval histogram. A behavior graph is generated to allow the maximum probability route computation with the shortest path algorithm, resulting in the most likely route to be taken by a user. An extension is proposed, named BR2+, to better consider the temporal dependency and incorporate distance in the recommendation process. Experiments conducted on two real GPS trajectory data sets demonstrate the efficiency and effectiveness of the proposed method. In addition, a web-based geographic information system (GIS) called MPR is implemented to demonstrate differences in route recommendation when time, distance, or users’ preferences are considered, besides providing insight about users’ movement through data visualization of their spatial and temporal coverage.

The integration of Building Information Modeling (BIM) and Geographic Information System (GIS) has been identified as a promising but challenging topic to transform information towards the generation of knowledge and intelligence. Achievement of integrating these two concepts and enabling technologies will have a significant impact on solving problems in the civil, building and infrastructure sectors. However, since GIS and BIM were originally developed for different purposes, numerous challenges are being encountered for the integration. To better understand these two different domains, this paper reviews the development and dissimilarities of GIS and BIM, the existing integration methods, and investigates their potential in various applications. This study shows that the integration methods are developed for various reasons and aim to solve different problems. The parameters influencing the choice can be summarized and named as “EEEF” criteria: effectiveness, extensibility, effort, and flexibility. Compared with other methods, semantic web technologies provide a promising and generalized integration solution. However, the biggest challenges of this method are the large efforts required at early stage and the isolated development of ontologies within one particular domain. The isolation problem also applies to other methods. Therefore, openness is the key of the success of BIM and GIS integration.

The primary concern of an air traffic controller is to ensure the safety and fluidity of ever-increasing air traffic. This requires effective training through practical work supervised by instructors. Based on certain rules called separation rules, the trainee must find a solution to a traffic configuration defined by flight plans (FPL) initially containing a number of conflicts. This solution will then be compared to the one proposed by the instructor. The purpose of this article is to replace the instructor with a Geographical Information System (GIS) solution combined with a genetic algorithm which, from a set of FPLs, will find the best solution to ensure on the one hand the safety of the aircraft but also minimizing the distance and the changes to be made. The application will use the GAMA platform, very suitable for this and a set of tests composed of actual exercises will be performed to validate the work.

Flood susceptibility mapping is essential for characterizing flood risk zones and for planning mitigation approaches. Using a multi-criteria decision support system, this study investigated a flood susceptible region in Bihar, India. It used a combination of the analytical hierarchy process (AHP) and geographic information system (GIS)/remote sensing (RS) with a cloud computing API on the Google Earth Engine (GEE) platform. Five main flood-causing criteria were broadly selected, namely hydrologic, morphometric, permeability, land cover dynamics, and anthropogenic interference, which further had 21 sub-criteria. The relative importance of each criterion prioritized as per their contribution toward flood susceptibility and weightage was given by an AHP pair-wise comparison matrix (PCM). The most and least prominent flood-causing criteria were hydrologic (0.497) and anthropogenic interference (0.037), respectively. An area of ~3000 sq km (40.36%) was concentrated in high to very high flood susceptibility zones that were in the vicinity of rivers, whereas an area of ~1000 sq km (12%) had very low flood susceptibility. The GIS-AHP technique provided useful insights for flood zone mapping when a higher number of parameters were used in GEE. The majorities of detected flood susceptible areas were flooded during the 2019 floods and were mostly located within 500 m of the rivers’ paths.

Forest fires have caused considerable losses to ecologies, societies, and economies worldwide. To minimize these losses and reduce forest fires, modeling and predicting the occurrence of forest fires are meaningful because they can support forest fire prevention and management. In recent years, the convolutional neural network (CNN) has become an important state-of-the-art deep learning algorithm, and its implementation has enriched many fields. Therefore, we proposed a spatial prediction model for forest fire susceptibility using a CNN. Past forest fire locations in Yunnan Province, China, from 2002 to 2010, and a set of 14 forest fire influencing factors were mapped using a geographic information system. Oversampling was applied to eliminate the class imbalance, and proportional stratified sampling was used to construct the training/validation sample libraries. A CNN architecture that is suitable for the prediction of forest fire susceptibility was designed and hyperparameters were optimized to improve the prediction accuracy. Then, the test dataset was fed into the trained model to construct the spatial prediction map of forest fire susceptibility in Yunnan Province. Finally, the prediction performance of the proposed model was assessed using several statistical measures—Wilcoxon signed-rank test, receiver operating characteristic curve, and area under the curve (AUC). The results confirmed the higher accuracy of the proposed CNN model (AUC 0.86) than those of the random forests, support vector machine, multilayer perceptron neural network, and kernel logistic regression benchmark classifiers. The CNN has stronger fitting and classification abilities and can make full use of neighborhood information, which is a promising alternative for the spatial prediction of forest fire susceptibility. This research extends the application of CNN to the prediction of forest fire susceptibility.

Keywords: SARS-CoV-2, COVID-19, Geographical information systems, Pandemic, Italy

Illustrating a wide range of practical applications, GIS in Hospital and Healthcare Emergency Management explains how hospitals and healthcare facilities can improve their emergency management and disaster preparedness through the use of GIS. The text aims to raise the level of understanding of the role of GIS in emergency management planning among hospital healthcare emergency managers, risk managers, decision-makers, and regulating and accrediting organizations. The book covers spatial aspects of planning, preparedness, response, and recovery. A CD-ROM with color images, useful forms, exercises, and additional resources is also included.

The benefits brought by the integration of Building Information Modelling (BIM) and Geographic Information Systems (GIS) are being proved by more and more research. The integration of the two systems is difficult for many reasons. Among them, data incompatibility is the most significant, as BIM and GIS data are created, managed, analyzed, stored, and visualized in different ways in terms of coordinate systems, scope of interest, and data structures. The objective of this paper is to review the relevant research papers to (1) identify the most relevant data models used in BIM/GIS integration and understand their advantages and disadvantages; (2) consider the possibility of other data models that are available for data level integration; and (3) provide direction on the future of BIM/GIS data integration.