Wetlands are recognized as one of the world’s most valuable natural resources. With the increasing world population, human demands on wetland resources for agricultural expansion and urban development continue to increase. In addition, global climate change has pronounced impacts on wetland ecosystems through alterations in hydrological regimes. To better manage and conserve wetland resources, we need to know the distribution and extent of wetlands and monitor their dynamic changes. Wetland maps and inventories can provide crucial information for wetland conservation, restoration, and management. Geographic Information System (GIS) and remote sensing technologies have proven to be useful for mapping and monitoring wetland resources. Recent advances in geospatial technologies have greatly increased the availability of remotely sensed imagery with better and finer spatial, temporal, and spectral resolution. This chapter presents an introduction to the uses of GIS and remote sensing technologies for wetland mapping and monitoring. A case study is presented to demonstrate the use of high-resolution light detection and ranging (LiDAR) data and aerial photographs for mapping prairie potholes and surface hydrologic flow pathways.

Human detection from Unmanned Aerial Vehicles (UAV) is gaining popularity in the field of disaster management, crowd counting, people monitoring. Real time human detection from UAV is a challenging task, because of many constraints involved. This study proposes a system for real time detection of humans on videos captured from UAVs addressing three of these constraints namely, flying height, computation time and scale of viewing. The proposed method integrated an android application with a binary classifier based on Haar-features to automatically detect human / non-human class from UAV images. The video frames were parsed and detected humans from image frames were geo-localized and visualized on Google Earth. The performance was evaluated for geo-localization accuracy, computation time and detection accuracy, considering human coverage – pixel size relationship for various heights and scale factor. Based on flying height - human size relationship and tradeoff between detection accuracy vs computation time, the study came up with optimal parameters for OpenCV’s cv2.cascadeClassifier. detectMultiScale function. This paper establishes a strong ground for further research relating to real time human detection from UAV.

Machine learning (ML) is a subdivision of artificial intelligence in which the machine learns from machine-readable data and information. It uses data, learns the pattern and predicts the new outcomes. Its popularity is growing because it helps to understand the trend and provides a solution that can be either a model or a product. Applications of ML algorithms have increased drastically in G.I.S. and remote sensing in recent years. It has a broad range of applications, from developing energy-based models to assessing soil liquefaction to creating a relation between air quality and mortality. Here, in this paper, we discuss the most popular supervised ML models (classification and regression) in G.I.S. and remote sensing. The motivation for writing this paper is that ML models produce higher accuracy than traditional parametric classifiers, especially for complex data with many predictor variables. This paper provides a general overview of some popular supervised non-parametric ML models that can be used in most of the G.I.S. and remote sensing-based projects. We discuss classification (Naïve Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT)) and regression models (Random Forest (RF), Support Vector Machine (SVM), Linear and Non-Linear) here. Therefore, the article can be a guide to those interested in using ML models in their G.I.S. and remote sensing-based projects

This paper presents a method for a decentralised peer-to-peer software license validation system using cryptocurrency blockchain technology to ameliorate software piracy, and to provide a mechanism for software developers to protect copyrighted works. Protecting software copyright has been an issue since the late 1970's and software license validation has been a primary method employed in an attempt to minimise software piracy and protect software copyright. The method described creates an ecosystem in which the rights and privileges of participants are observed.

The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e. professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies. This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools.

Summary CoV-GLUE is an online web application for the interpretation and analysis of SARS-CoV-2 virus genome sequences, with a focus on amino acid sequence variation. It is based on the GLUE data-centric bioinformatics environment and provides a browsable database of amino acid replacements and coding region indels that have been observed in sequences from the pandemic. Users may also analyse their own SARS-CoV-2 sequences by submitting them to the web application to receive an interactive report containing visualisations of phylogenetic classification and highlighting genomic variation of potentially high impact, for example linked to primer mismatches.Availability and implementation Available at http://cov-glue.cvr.gla.ac.uk. Implemented using GLUE, an open source framework for the development of virus sequence data resources. Contact josh.singer@glasgow.ac.uk

Prestressed concrete sleepers (or railroad ties) are principally designed in order to carry wheel loads from the rails to the ground of railway tracks. Their design takes into account static and dynamic loading conditions. In spite of the most common use of the prestressed concrete crossties in railway tracks, there have always been many demands from rail engineers to improve serviceability and functionality of concrete crossties. For example, signaling, fiber optic, equipment cables are often damaged either by ballast corners or by tamping machine. There has been a need to re-design concrete crosstie to cater cables internally so that they would not experience detrimental or harsh environments. Also, many concrete crossties need a retrofit for automatic train control device and similar signaling equipment. In contrast, the effects of holes and web openings on structural capacity of concrete crossties have not been thoroughly investigated. This paper accordingly highlights the effect of holes and web openings on the toughness and ductility of concrete crossties. The outcome of this research enables better decision making process for retrofiting prestressed concrete crossties with holes and web opening in practice.

The internet can broadly be divided into three parts: surface, deep and dark among which the latter offers anonymity to its users and hosts [1]. Deep Web refers to an encrypted network that is not detected on search engine like Google etc. Users must use Tor to visit sites on the dark web [2]. Ninety six percent of the web is considered as deep web because it is hidden. It is like an iceberg, in that, people can just see a small portion above the surface, while the largest part is hidden under the sea [3, 4, and 5]. Basic methods of graph theory and data mining, that deals with social networks analysis can be comprehensively used to understand and learn Deep Web and detect cyber threats [6]. Since the internet is rapidly evolving and it is nearly impossible to censor the deep web, there is a need to develop standard mechanism and tools to monitor it. In this proposed study, our focus will be to develop standard research mechanism to understand the Deep Web which will support the researchers, academicians and law enforcement agencies to strengthen the social stability and ensure peace locally & globally.

This paper presents the validation of the End Point Rate (EPR) tool for QGIS (EPR4Q), a tool built-in QGIS Graphical Modeler to calculate the shoreline change by End Point Rate method. The EPR4Q tries to fill the gap of user-friendly and free open-source tool for shoreline analysis in Geographic Information System environment, since the most used software - Digital Shoreline Analysis System (DSAS) - although is a free extension, is suited for commercial software. Besides, the best free open-source option to calculate EPR called Analyzing Moving Boundaries Using R (AMBUR), since it is a robust and powerful tool, the complexity and heavy processes can restrict the accessibility and simple usage. The validation methodology consists of applying the EPR4Q, DSAS, and AMBUR on different examples of shorelines found in nature, extracted from the U.S. Geological Survey Open-File. The obtained results of each tool were compared with Pearson correlation coefficient. The validation results indicate that the EPR4Q tool created acquired high correlation values with DSAS and AMBUR, reaching a coefficient of 0.98 to 1.00 on linear, extensive, and non-extensive shorelines, guarantying that the EPR4Q tool is ready to be freely used by the academic, scientific, engineering, and coastal managers communities worldwide.

Resource Description Framework(RDF) is a data representation format of the Semantic Web, and its data volume is growing rapidly. Cloud-based systems provide a rich platform for managing RDF data. However, the distributed environment has performance challenges when it is processing with RDF queries that contain multiple join operations, such as network reshuffle, memory overhead. To get over these challenges, this paper proposed a spark-based RDF query architecture, which is based on Semantic Connection Set (SCS). First of all, this spark-based query architecture adopts the mechanism of re-partitioning class data based on vertical partitioning, which can reduce memory overhead and fast index data. Secondly, a method for generating query plans based on semantic connection sets is proposed in this paper. In addition, statistics and broadcast variable optimization strategies are used to reduce shuffling and data communication costs. The experiment of this paper is based on the latest SPARQLGX on the spark platform RDF system, two synthetic benchmarks are used to evaluate the query. The experiment result illustrates that the proposed approach in this paper is more efficient in data search than SPARQLGX.

Geospatial Information Systems (GIS) can provide a great environment for using machine learning algorithm for spatial data such as satellite images. Integrating this functionality with artificial intelligence algorithms for analyzing spatial data enables us to predict challenging disasters such as deforestation. Deforestation as an environmental problems has been recorded the most serious threat to environmental diversity and one of the main components of land-use change. In this paper, we investigate spatial distribution of deforestation using artificial neural networks and satellite imagery. We modeled deforestation process using various factors in determining the relationship between deforestation and environmental and socioeconomic factors. Hence, for this purpose, the proximity to roads and habitats, fragmentation of the forest, height from sea level, slope, and soil type are considered in the model. In this research, we modeled land cover changes (forests) to predict deforestation using an artificial neural network due to its significant potential for the development of nonlinear complex models. The procedure involves image registration and error correction, image classification, preparing deforestation maps, determining layers, and designing a multi-layer neural network to predict deforestation. The satellite images for this study are of a region in Hong Kong which are captured from 2012 to 2016. The results of the study demonstrate that neural networks approach for predicting deforestation can be utilized and its outcomes show the areas that destroyed during the research period.

Nowadays, the Internet of Things (IoT) concept plays a pivotal role in society and brings new capabilities to different industries. The number IoT solutions in areas such as transportation and healthcare is increasing and new services are under development. In the last decade, society has experienced a drastic increase in IoT connections. In fact, IoT connections will increase in the next few years across different areas. Conversely, despite these benefits, several challenges still need to be faced to enable efficient and secure operations (e.g., interoperability, security, standards, and server technologies). Furthermore, although efforts have been made to produce datasets composed of attacks against IoT devices, several possible attacks are not considered. Most existing efforts do not consider an extensive network topology with real IoT devices. The main goal of this research is to propose a novel and extensive IoT attack dataset to foster the development of security analytics applications in real IoT operations. To accomplish this, 33 attacks are executed in an IoT topology composed of 105 devices. These attacks are classified into seven categories, namely DDoS, DoS, Recon, Web-based, Brute Force, Spoofing, and Mirai. Finally, all attacks are executed by malicious IoT devices targeting other IoT devices.

Semantic Web technologies are being increasingly adopted by the geospatial community during last decade through the utilization of open standards for expressing and serving geospatial data. This was also dramatically assisted by an ever increasing access and usage of geographic mapping and location-based services via smart devices in people’s daily activities. In this paper we explore the developmental framework of a pure Javascript client-side GIS platform exclusively based on invocable geospatial Web services. We also extend Javascript utilization on the server side by deploying a node server acting as a bridge between open source WPS libraries and popular geoprocessing engines. The vehicle for such an exploration is a cross platform Web browser capable of interpreting Javascript commands to achieve interaction with geospatial providers. The tool is a generic Web interface providing capabilities of acquiring spatial datasets, composing layouts and applying geospatial processes. In an ideal form the end-user will have to identify those services, which satisfy a geo-related need and put them in the appropriate row. The final output may act as a potential collector of freely available geospatial web services. Its server-side components may exploit geospatial processing suppliers composing that way a light-weight fully transparent open Web GIS platform.

Web-Based GIS is a platform that deals with the representation, management and storage of locational information for real-time online retrieval, viewing and manipulations. Web-based GIS incorporate all the functions of information systems: gathering, storing, retrieving, visualizing and dissemination of spatial information on the Internet. In modern-day GIS serve as vital means to not only geo-referenced locational information that are been retrieved but also the development in web-based technologies as well as there involvements in decision making processes. In recent time the increased used of GIS on the Internet have totally transformed and made three distinct changes across our GIS environment. These are: (i) how are GIS data being accessed (ii) what are modes of its transmission and (iii) what available GIS functionalities are within the environment for analysis. This paper examines the applications and technologies associated with web-based GIS architectures with an emphasis on the internet GIS based structure and case studies of GIS Web-based applications in MapInfo, Microsoft Silverlight Technology and the ArcGIS Server using ArcGIS JavaScript API.

Is the amount of “greenness” within a 250-meter, 500-meter, 1000-meter or a 2000-meter buffer surrounding a person’s home a good predictor of their physical health? The evidence is inconclusive. We reviewed Web of Science articles that used geographic information systems buffer analyses to identify trends between physical health, greenness, and distance within which greenness is measured. Our inclusion criteria were: (1) use of buffers to estimate residential greenness; (2) statistical analyses that calculated significance of the greenness-physical health relationship; and (3) peer-reviewed articles published in English between 2007 and 2017. To capture multiple findings from a single article, we selected our unit of inquiry as the analysis, not the article. Our final sample included 260 analyses in 47 articles. All aspects of the review were in accordance with PRISMA guidelines. Analyses were independently judged as more, less, or least likely to be biased based on the inclusion of objective health measures and income/education controls. We found evidence that larger buffer sizes, up to 2,000m, better predicted physical health than smaller ones. We recommend that future analyses use nested rather than overlapping buffers to evaluate to what extent greenness not immediately around a person’s home (i.e., within 1,000-2,000m) predicts physical health.

In this study, our goal was to research land-use change by combining spatio–temporal land use/land cover monitoring (LULC (1989–2019) and urban growth modeling (1999–2039) in Benslimane, Morocco, to determine the effect of urban growth on different groups based on cellular automata (CA) and geospatial methods. A further goal was to test the reliability of the AC algorithm for urban expansion modeling. To do this, four years of satellite data were used at the same time as population density, downtown distance, slope, and ground road distance. The LULC satellite reported a rise of 3.8 km2 (318% variation) during 1989–2019. Spatial transformation analysis reveals a good classification similarity ranging from 89% to 91% with the main component analysis (PCA) technique. The statistical accuracy between the satellite scale and the replicated built region of 2019 gave 97.23 %t of the confusion matrix overall accuracy, and the region under the receiver operational characteristics (ROC) curve to 0.94, suggesting the model's high accuracy. Although the constructed area remains low relative to the total area of the municipality's territory, the LULC project shows that the urban area will extend to 5,044 km2 in 2019, principally in the western and southwestern sections. In 2019–2039, urban development is expected to lead to a transformation of the other class (loss of 1,364 km2), followed by vegetation cover (loss of 0.345 km2). In spatial modeling and statistical calculations, the GDAL and NumPy Python 3.8 libraries were successful.

Groundwater is an important role of the environment in natural resources. The major sources of groundwater contamination in this study were open discharges of domestic sewage, inadequate sewerage system, open defecation, septic tanks, soak pits, contaminated water pools, unorganized solid waste dumping and use of fertilizers, pesticides for agriculture deteriorated the condition. In this present study revealed that the physical and chemical characteristics of ground water in different areas of Kannur district in Kerala have been determined different seasons with respect to its suitability for drinking and agricultural purposes. For this study the groundwater samples were collected during pre-monsoon and post-monsoon seasons from 70 wells representing the entire the study area. The groundwater samples were analyzed for Physico-chemical characteristics using standard techniques in laboratory and compared with standards. The samples were analyzed with reference to the WHO and BIS standards. The groundwater quality information of the entire study area have been prepared using statistical and GIS technique for all the parameters. This paper proved in GIS will be helpful for measuring, monitoring and managing the groundwater pollution in the study area and suggested to protect groundwater resources in the environment.

COVID-19 pandemic event requires a rapid response from various organizations at the international and national levels. One important response is the provision of information sharing facilities and monitoring of the spread of cases around the world, JHU CSSE developed the Dashboard in January 2020 and followed by WHO the same month for the WHO COVID-19 dashboard. Both dashboards have distributed information as expected by the user with their respective pros and cons. JHU CSSE Dashboard provides faster information with good access to mobile device users even though the display and color selection are less attractive. Information on the WHO COVID-19 Dashboard is often late but more data appearances and variations and comparisons between countries can be made. In the Indonesian context, ESSC for COVID-19 Geoportal as Esri Indonesia initiative has been developed with the support of data and information from various parties and developed with the principles of big data management which are fully supported by adequate spatial portal developer software from Esri. Particularly in Indonesia, there is not yet an adequate system to support spatial based decision making at the local level, therefore the development of a GIS dashboard to support provincial and district governments is highly recommended.

Wetlands are one of the crucial natural resources. They provide invaluable biodiversity resources, aid in water quality improvement, support ground water recharge, help in moderating climate change and support flood control. Environment is in the other hand, where we live and something, we are very familiar with our day to day life. Geographic Information Systems (GIS), Remote Sensing and Global Positioning System (GPS) were a useful tool for wetland and environmental change analysis and to improve on the classification accuracy. This study investigates population and environmental change of Jarmet wetland and its surrounding area change analysis over the period of 1972 to 2015. The purpose of this study was to show land use/ land cover change of Jarmet wetland and its surrounding environment over years as a response to population growth. For this purpose, multi-temporal satellite imageries (Landsat MSS 1972, TM1986, ETM+ 2000, 2005 and 2015 and SRTM 2000) were obtained and used for LULC change analysis, elevation analysis and change detection analysis. ERDAS Imagine 2015, ARC GIS 10.5.1, Global Mapper11, ENVI 5.0 and DNR Garmin softwares were used to process the image data and accuracy assessment analysis. The result of LULC showed that there is spatial reduction in wetland, forest, Shrubland and grassland in the period of 43 years (1972-2015) by -1,722.8 ha, -296.2 ha, -1,718.7 ha and -661.9 ha respectively, due to increase in the farmland and plantation area as a response to overpopulation, lack of environmental policy implementation and irresponsible for natural resource degradation. The accuracy assessment of LULC change are done for recent satellite image showed the overall accuracy of 84.06% with Kappa index 75.19% this means this classification is accurately classified and handle greater than 75% of error. Finally, this study suggests that create strictly natural resource conservation law, stopping illegal expansion of farmland, educating society about the value of natural resource especially wetland and create a source of income for society rather than farming.

Over the years, the Egyptian temples at Luxor city have been intensely investigated, but most of these studies just focused on the classical sides of the archaeological and historical descriptions. Many of the environmental problems are inevitable results of the unplanned urban crawling around the monuments temples. This paper aims at assessing the environmental changes around some temples of Luxor City using Remote sensing and GIS techniques. In particular, a historical database made up of Corona and Landsat TM data have been investigated along with the new acquisitions of Quickbird2 and Sentinel2. Results from our investigation highlighted rapid changes in urban and agricultural areas, which adversely affected the Egyptian monumental temples causing serious degradation phenomena. Using the information obtained from our RS&GIS based analysis, mitigation strategies have been also identified for supporting the preservation of the archaeological area.

Enzymes are in high demand for very diverse biotechnological applications. However, natural biocatalysts often need to be engineered for fine-tuning their properties towards the end applications, such as the activity, selectivity, stability to temperature or co-solvents, and solubility. Computational methods are increasingly used in this task, providing predictions that narrow down the space of possible mutations significantly and can enormously reduce the experimental burden. Many computational tools are available as web-based platforms, making them accessible to non-expert users. These platforms are typically user-friendly, contain walk-throughs, and do not require deep expertise and installations. Here we describe some of the most recent outstanding web-tools for enzyme engineering and formulate future perspectives in this field.

This study proposes a novel hybrid machine learning approach for modeling of rainfall-induced shallow landslides. The proposed approach is a combination of an instance-based learning algorithm (k-NN) and Rotation Forest (RF), state of the art machine techniques that have seldom explored for landslide modeling. The Lang Son city area (Vietnam) is selected as a case study. For this purpose, a spatial database for the study area was constructed, and then, was used to build and evaluate the hybrid model. Performance of the model was assessed using Receiver Operating Characteristic (ROC), area under the ROC curve (AUC), success rate and prediction rate, and several statistical evaluation metrics. The results showed that the model has high performance with both the training data (AUC = 0.948) and the validation data (AUC = 0.848). The results were compared with those obtained from soft computing techniques i.e. Random Forest, J48 Decision Trees, and Multilayer Perceptron Neural Networks. Overall, the performance of the proposed model is better than those obtained from the above methods. Therefore, the proposed model is a promising tool for landslide modeling. The research result can be highly useful for land use planning and management in landslide prone areas.

The sea level rise, a consequence of climate change, is one of the biggest challenges that countries and regions with coastal lowland areas will face in the medium term. This study proposes a methodology for assessing the vulnerability to sea level rise (SLR) on the Atlantic coast of Portugal mainland. Some scenarios of extreme sea level for different return periods and extreme flooding events were estimated for 2050 and 2100, as proposed by the European Union Directive 2007/60/EC. A set of physical parameters are considered for the multi-attribute analysis technique implemented by the Analytic Hierarchy Process, in order to define a Physical Vulnerability Index fundamental to assess coastal vulnerability. For each SLR scenario, coastal vulnerability maps, with spatial resolution of 20 m, are produced at national scale to identify areas most at risk of SLR, constituting key documents for triggering adaptation plans for such vulnerable regions. For 2050 and 2100, it is estimated 903 km² and 1146 km² of vulnerable area, respectively, being the district of Lisbon the most vulnerable district in both scenarios. Results are available through a Web Map Service, for Portuguese public entities, and through a web map viewer for public and communities in general.

This paper aims to investigate carbon monoxide (CO) concentrations on roadways of Karachi, potential blood levels carboxy-hemoglobin (COHb) in Karachi. Geographical information system (GIS) was used for spatial analysis of diseases potentiality while an interpolation technique has been applied for surface generation with town boundaries and later evaluates risk areas.The higher concentration of carbon monoxide in the ambient is mainly due to automobile emissions. The City center and CBD areas are more perilous.

Spatio-temporal aspects of data lead to critical information. Sensors capture data at all scales continually so it is imperative that useful information be extracted ubiquitously and regularly. Location plays a vital part by helping understand relations between datasets. It is crucial to link developmental works with spatial attributes and current challenge is to create an open platform that manages real-time sensor data and provides critical spatial analytics atop expert domain knowledge provided in the system. That is a two-faced problem where the solution tackles not only data from multiple sources but also runs data management platform, a spatial data infrastructure(SDI) as backbone framework able to harness sensor web(SW). The paper proposes development of such a globally shared open spatial expert system(ES), SmaCiSENS, a first of a kind geo-enabled knowledge based(KB) ES for multiple fields, smarter cities to climate modeling. SmaCiSENS is integration of SW and SDI with domain KB on data and problems, ready to infer solutions. The paper describes an architecture for semantic enablement for SW, SDI; connect interfaces, functions of SDI and SW, and sensor data application program interfaces (APIs) to better manage climate modeling, geohazard, global changes, and other vital areas of attention and action.

Choosing the optimal location for a city based on sound environmental geomorphology planning is of the utmost importance to achieving environmental sustainability, as it can spare the State and other decision-making entities a great deal of stress in the long run. GIS offers great potential for environmental planners to choose the most appropriate places for the cities of the future, especially when coupled with environmental geomorphological analyses. The State of Kuwait seeks sustainable development through the implementation of clear and specific urban plans, some of which suffer from a severe lack of geomorphological and spatially based environmental planning. This study aims to: 1) Conduct suitability modelling for establishing new cities in Kuwait, 2) Assess the current 2005-2030 urban plan, and 3) Propose possible recommendations and solutions for potential urban problems. The study relies on integrating several methods to devise a framework that will aid researchers and decision-makers in selecting optimal locations for built structures based on analysis and modelling (e.g., digital elevation model, geologic mapping, geomorphology, natural hazards, heritage/archaeological sites, military areas, oil fields, soils). Using this methodology in choosing city sites contributes to achieving sustainable development, reducing city problems, saving countries’ budgets, and saving lives. Results from this study enhance understanding of how environmental geomorphology, when combined with GIS, can be harnessed to achieve sustainable urban development in the Arabian Gulf countries and other desert countries.

The mental workload induced by a Web page is essential for improving the user’s browsing experience. However, continuously assessing the mental workload during a browsing task is challenging. In order to face this issue, this paper leverages the correlation between stimuli and physiological responses, which are measured with high-frequency, non-invasive psychophysiological sensors during very short span windows. An experiment was conducted to identify levels of mental workload through the analysis of pupil dilation measured by an eye-tracking sensor. In addition, a method was developed to classify real-time mental workload by appropriately combining different signals (electrodermal activity (EDA), electrocardiogram, photoplethysmography (PPG), electroencephalogram (EEG), temperature and eye gaze) obtained with non-invasive psychophysiological sensors. The results show that the Web browsing task involves on average four levels of mental workload. Also, by combining EEG with the PPG and EDA, the accuracy of the classification reaches 95.73 %.

The accurate assessment and monitoring of soil erosion is of great significance for guiding food production and ensuring ecological security, and it is a current research hotspot. In this paper, remote sensing and geographic information systems (GISs) are combined with the Revised Universal Soil Loss Equation (RUSLE model) to carry out research on soil erosion monitoring and make a quantitative evaluation. According to five factors, including rainfall erosivity, soil erodibility, topography, vegetation cover, crop management and water and soil conservation measures, the distribution of the soil erosion rate in Jilin Province in 2019 was mapped, and the soil erosion rate was divided into 5 levels according to the degree of erosion, including very slight, slight, moderate, severe and extremely severe erosion. Based on the segmented S-slope factor model and the unique topographical features of the study area, the relationships among the soil erosion rate, erosion risk level, erosion area, erosion amount and slope angle (θ) were systematically analysed, and a slope angle of 15° was identified as the threshold for soil erosion on sloped farmland in Jilin Province. The total soil erosion in Jilin Province was 402.14×106 t in 2019, the average soil erosion rate was 21.6 t·ha-1·a-1, and the average soil loss thickness was 1.6 mm·a-1; these values were far greater than the soil erosion rate risk threshold of 10 t ·Ha-1·a-1. Thus, the province has a strong level of soil erosion. We conclude that soil degradation is accelerating, and food production and the ecological environment will face severe challenges. It is suggested that soil erosion control should be carried out according to different types and slopes of land, with an emphasis on the management of forestland and farmland because forestland and farmland are currently the first types of land to be managed in Jilin Province. This paper aims to explore a timely, fast, efficient and convenient soil erosion monitoring and evaluation method and provide effective monitoring tools for agricultural water and soil conservation, ecological safety management and stable food production in Jilin Province and similar black soil areas.

This is study follows environmental diversity assessment for geotourism development in Asir region. Geotourism seeks to supporting the tourism landscape in its interaction with the historical and archaeological, architectural or immaterial heritage, and requires diversification in terms of product, market and geographical potential. The study is based on various tourist facades and environmental diversity in Asir. As tourism development is a comprehensive undertaking involving many sectors, and these are the challenges to which the country’s tourism industry should respond to promote domestic tourism. quality and spatial pattern of tourism resources, climate comfort, and natural disaster possibility. Based on analyze multi-source datasets collected, geomorphological features of this area, we created a GIS database comprising geologic and topographic maps, and satellite images using these datasets. The findings of the study provided valuable insights into the role of environmental diversity in achieving tourism. The study examined the interrelationship between tourism and environmental diversity.

Cloudburst is one of the most devastating and frequently occurring natural hazardous events in Indian Himalayan region. Localized deep cumulus convective clouds have a capability of giving enormous amount of rainfall over a limited horizontal area, within a short span of time. Whenever, such events occur, lead to flash floods causing landslides, house collapses, dislocation of traffic, and human casualties on a large scale. Therefore, it is necessary to predict the cloudburst inundation zones accurately to avoid damage associated with them. For this, high resolution Digital Elevation Model generated from CartoSat-1 (Stereo pair) were integrated in MIKE 11 Hydrodynamic 1D model to generate longitudinal profile of the study area and to find water level, peak discharge, flow velocity, flow width at different reaches along the Asi ganga and Bhagirathi river, to know the Cloudburst flood inundation scenario. On 3^rd August 2012 one of the major Cloudburst event occurred in Asi Ganga Valley in Indian Himalayan region which was considered for simulation of hydrodynamic model. For a Cloudburst event, 100 mm/hr rainfall was considered for the simulation of the hydrodynamic model. It is observed that the discharge rise from 50 m³/s to 549.164 m³/s (an abrupt increase of about 10 times) within 1 hr at Sangamchetty in Asiganga river and at Joshiyara area rise from 600 m³/s to 3378.69 m³/s (an abrupt increase of about 5 times) within 4 hr in Bhagirathi river. Similarly the water level rises around 3 m and 6m in Asi Ganga and Bhagirathi rivers respectively. Flash Flood inundation areas due to Cloudburst on 3^rd August 2012 were demarcated from the simulation results in GIS environment.

The non-existence of a land ownership database in most of the developing countries moves the inhabitants to the occupation of public lands. Some of this situation are the origin to areas of informal housing, commerce and agriculture and in the end into new ​​informal settlements. Informal settlements become a serious problem in developing countries. The most common typology of informal settlements is that they are the population settled in public lands without any infrastructures and against the administrator's will. Thought this action the result in an uncontrolled land occupation process that promotes new informal areas without any proper built-up utilities, located in risk areas on the territory, barely ensuring the minimum requirements for a heaty living of the population and in various cases incentives to an informal economy. The process of build a cadastral map in informal settlement areas is a fundamental base to support the future transformation of illegal areas and to regulate the occupation of new subdivision planning and into the creation of new expansion areas. In this paper, it is presented a methodology developed to be applied to support a new register of land and to management. The transformation of informal settlement areas. The model to register the land tenure has been associated with allows the process application to multiple typology of informal settlements. The model to register land tenure has developed on a series of qualitative and quantitative data that determine the identification and classification of the buildings and its physical and functional description. The model was developed using Geographic Information System and with an initial survey of existing land titles of possession and public proposals to develop new expansion areas. A case study of the method is presented, where the land management model was implemented - Chã da Caldeiras in Ilha do Fogo an informal settlement in Cape Verde. The results are a great acceptance of the proposal by the population and local authorities and the starting of the implementation phase.

Flash flooding is one of the most devastating natural events that leads to enormous and recurring loss of life. Kuwait was subjected to severe rainstorms in the winter of 2018 and 2020 followed by an extreme violent flood that had not been known in Kuwait since 1976. It resulted in several geomorphological and environmental impacts in urban and desert areas. This produced some positive results, such as geomorphological activity in landforms, the flow of some valleys and the prosperity of wildlife in the Kuwaiti desert. Negative results included some problems in the metropolitan area and destruction of some road networks that intersect the main valleys, and which were not equipped with crossings for avoiding floods. There was also the emergence of some problems in the infrastructure. Study of flash floods requires the involvement of all scientific and executive bodies to avoid environmental risk. The study aims to: 1- Monitor geomorphological and environmental changes. 2- Assess the impact of floods in the urban areas and on infrastructure. 3- Modeling the impact. 4- Creating solutions and adaptions to the flash flood. The study uses several methods such as remote sensing (RS), geographic information systems (GIS), hydrologic modeling and fieldwork to evaluate the impact of flash flood hazards on the sustainable urban development of Kuwait state. This approach is rarely used in Kuwait. We propose a novel method that could help decision-makers and planners in determining inundated flood zones before planning future urban developments in Kuwait, and help them to manage flood water, by identifying the most appropriate places for storage to exploit water in agriculture and drinking.

With the rapid surge in technological advancements, an equal amount of investment in technology-embedded teaching has become vital to pace up with the ongoing educational needs. Distance education has evolved from the era of postal services to the use of ICT tools in current times. With the aid of globally updated content across the board, technology usage ensures all students receive equal attention without any discrimination. Importantly, web-based teaching allows all kind of students to learn at their own pace, without the fear of being judged, including professionals who can learn remotely without disturbing their job schedules. Having web-based content allows low-cost and robust implementation of the content upgradation. An improved, yet effective, version of the education using such tools is Hybrid Learning (HL). This learning mode aims to provide luxurious reinforcement to its legitimate candidates while maintaining the quality standards of various elements. Incorporated with both traditional and distance learning methods, along with exploiting social media tools for increased comfort level and peer-to-peer collaboration, HL ultimately facilitates the end user and educational setup. The structure of such a hybrid model is realized by delivering the study material via a learning management system (LMS) designed in compliance with quality standards, which is one of the fundamental tackling techniques for controlling quality constraints. In this paper, we present the recently piloted project by COMSATS University Islamabad (previously known as COMSATS Institute of Information Technology) which is driven by technology-embedded teaching model. This model is an amalgam of the traditional class room model with the aid of state-of-the-art online learning technologies. The students are enrolled as full-time students, with all the courses in traditional classroom mode, except one course offered as hybrid course. This globally adapted model helps the students to benefit from both face-to-face learning as well as gaining hands-on experience on technology-enriched education model providing flexibility of timings, learning pace, and boundaries. Our HL model is equipped with two major synchronous and asynchronous blocks. The synchronous block delivers real-time live interaction scenarios using discussion boards, thereby providing a face-to-face environment. Interactions via social network has witnessed equally surging improvement in the output performance. The asynchronous block refers to the lecture videos, slides and handouts, prepared by imminent professors, available 24/7 for students. To ensure quality output, our HL model follows the course learning outcomes (CLOs), and program learning outcomes (PLOs) as per international standards. As a proof of concept, we have deployed a mechanism at the end of each semester to verify the effectiveness of our model. This mechanism fundamentally surveys the satisfaction levels of all the students enrolled in the HL courses. With the surveys already conducted, a significant level of satisfaction has been noted. Extensive results from these surveys are presented in the paper to further validate the efficiency and robustness of our proposed HL model.

Meteorological drought indicators are commonly used for agricultural drought contingency planning in Ethiopia. Agricultural droughts arise due to soil moisture deficits. While these deficits may be caused by meteorological droughts, the timing and duration of agricultural droughts need not coincide with the onset of meteorological droughts due to soil moisture buffering. Similarly, agricultural droughts can persist even after the cessation of meteorological droughts due to delayed hydrologic processes. Understanding the relationship between meteorological and agricultural droughts is therefore crucial. An evaluation framework was developed to compare meteorological and agricultural droughts using a suite of exploratory and confirmatory tools. Receiver operator characteristics (ROC) was used to understand the covariation of meteorological and agricultural droughts. Comparisons were carried out between SPI-2, SPEI-2 and Palmer Z-index to assess intra-seasonal droughts and between SPI-6, SPEI-6 and PDSI for full-season evaluations. SPI was seen to correlate well with selected agricultural drought indicators but did not explain all the variability noted in agricultural droughts. The relationships between meteorological and agricultural droughts exhibited spatial variability which varied across indicators. SPI is better suited to predict non-agricultural drought states more so than agricultural drought states. Differences between agricultural and meteorological droughts must be accounted for better drought-preparedness planning.

With the proliferation of locating devices, more and more raw spatial trajectories are formed, and many works enrich these raw trajectories with semantics, and mine patterns from both raw and semantic trajectories, but access control of spatial trajectories is not considered yet. We present a multi-policy secure model for semantic spatial trajectories. In our model, Mandatory Access Control, Role Based Access Control and Discretionary Access control are all enforced, separately and combined, and we represent the model semi-formally in Ontology Web Language.

Food waste is one of the greatest challenge to sustainability in developed and developing countries. In the former, food waste is concentrated in the final part of the chain. Too Good To Go is a digital platform aimed to prevent food waste, where consumers can buy magic boxes from different outlets containing food products that are approaching their end-of-life. However, the mechanism of the platform enable the outlets to publish their offering much in advance of the pick-up time, raising the question whether the food sold on the platform is effectively a leftover or a planned production. Our empirical study gathers data from over 1200 outlets in Rome on the Too Good To Go platform.

Satellite imagery has had limited application in the analysis of pre-colonial settlement archaeology in the Caribbean; visible evidence of wooden structures perishes quickly in tropical climates. Only slight topographic modifications remain, typically associated with middens. Nonetheless, surface scatters, as well as the soil characteristics they produce, can serve as quantifiable indicators of an archaeological site, which can be detected by analysis of remote sensing imagery. A variety of data sets were investigated, with the intention to combine multispectral bands to feed a direct detection algorithm, providing a semi-automatic process to cross-correlate the datasets. Sampling was done using locations of known sites, as well as areas with no archaeological evidence. The pre-processed very diverse remote sensing data sets have gone through a process of image registration. The algorithm was applied in the northwestern Dominican Republic on areas that included different types of environments, chosen for having sufficient imagery coverage, and a representative number of known locations of indigenous sites. The resulting maps present quantifiable statistical results of locations with similar pixel value combinations as the identified sites, indicating higher probability of archaeological evidence. The results show the variable potential of this method in diverse environments.

This short note proposes a national Geographic Information System (GIS) - based health infrastructure to deal with epidemics and pandemics. Currently, there is no pan-India health infrastructure available that can compile, update, and report the spread of epidemic diseases. It not only curtails the opportunity of finding the real-time data on the spatial distribution of a disease but prevents one to inquire into the causes of the disease through epidemiological analysis. The proposed infrastructure in this study is a pan-India one and can broadly be divided into two parts, hotspot mapping and accessibility to services. In the first part, hospitals are proposed to act as nodes of data collection, sending data to a national GIS portal. This portal shall have the capabilities of plotting the data using map rendering services such as Google and Bing Maps. This way, hotspots can be visualized in no time, benefitting the government and common citizenry alike. The second part deals with the accessibility of citizenry to a wide range of services, i.e., healthcare services, grocery outlets, emergency services, baby food, and many other essential services of the day to day life. In order to implement this, we propose that the government need to enforce a mandatory submission of locational coordinates of all Goods and Services Tax (GST) enrolled service providers. Once the coordinates are submitted, the government can effectively control the opening and closing of services and inform the citizenry at the same time. The proposed infrastructure is going to help deal with the extraordinary situations during epidemic and pandemics similar to what the world is currently facing in the form of Corona Virus Disease 2019 (COVID-19). Furthermore, the infrastructure can be scaled up or down as per the spread of a disease. The health-GIS platform proposed in this concept paper, shall help India in controlling and managing the epidemic more efficiently.

The European Commission has been striving to achieve sustainable development in its rural areas for more than 25 years through funds aimed at modernizing the agricultural and forestry sectors, protecting the environment and improving the quality of life. But is sustainable rural development really being accomplished? This study sets out to answer this question in the case of Extremadura, a Spanish territory with Low Demographic Density and a Gross Domestic Product still below 75 % of the European average. Both qualitative and quantitative methodology have been employed, using a Principal Component Analysis the result of which has provided us with a model which shows how various behaviors coexist in the region in view of the distribution of current funding from the EAFRD. The most dynamic areas have received the largest amounts of funding and these are linked to the agricultural sector and to the protection of the environment, leaving aside the more depressed areas and the implementation of the LEADER Approach as well. Therefore, we have come to the conclusion that the current rural development in Extremadura is not sustainable enough.

The urbanisation phenomenon and related cities expansion and transport networks entail preventing the increase of population exposed to environmental pollution. Regarding noise exposure, the Environmental Noise Directive demands on main metropolis to produce noise maps. While based on standard methods, these latter are usually generated by proprietary software and require numerous input data concerning, for example, the buildings, land use, transportation network and traffic. The present work describes an open source implementation of a noise mapping tool fully implemented in a Geographic Information System compliant with the Open Geospatial Consortium standards. This integration makes easier at once the formatting and harvesting of noise model input data, cartographic rendering and output data linkage with population data. An application is given for a French city, which consists in estimating the impact of road traffic-related scenarios in terms of population exposure to noise levels both in relation to a threshold value and level classes.

The latest developments in digital humanities have increasingly enabled the construction of large data sets which can easily be accessed and used. These data sets often contain indirect localisation information, such as historical addresses. Historical geocoding is the process of transforming the indirect localisation information to direct localisation that can be placed on a map, which enables spatial analysis and cross-referencing. Many efficient geocoders exist for current addresses, but they do not deal with temporal information and are usually based on a strict hierarchy (country, city, street, house number, etc.) that is hard, if not impossible, to use with historical data. Indeed, historical data are full of uncertainties (temporal, textual, positional accuracy, confidence in historical sources) that can not be ignored or entirely resolved. We propose an open source, open data, extensible solution for geocoding that is based on gazetteers composed of geohistorical objects extracted from historical topographical maps. Once the gazetteers are available, geocoding an historical address is a matter of finding the geohistorical object in the gazetteers that is the best match to the historical address searched by the user. The matching criteria are customisable and include several dimensions (fuzzy string, fuzzy temporal, level of detail, positional accuracy). As the goal is to facilitate historical work, we also propose web-based user interfaces that help geocode (one address or batch mode) and display over current or historical topographical maps, so that geocoding results can be checked and collaboratively edited. The system has been tested on the city of Paris, France, for the 19th and the 20th centuries. It shows high response rates and is fast enough to be used interactively.

The nature of contemporary Spatial Data Infrastructures lies in the provision of geospatial information in an on-demand fashion. Though recent applications identified the need to react to real-time information in a time-critical way. In particular, research efforts in the field of geospatial Internet of Things have identified substantial gaps in this context, ranging from a lack of standardization for event-based architectures to the meaningful handling of real-time information as ''events''. This manuscript presents work in the field of Event-driven Spatial Data Infrastructures with a particular focus on sensor networks and the devices capturing in-situ measurements. The current landscape of Spatial Data Infrastructures is outlined and used as the basis for identifying existing gaps that retain certain geospatial applications from using real-time information. We present a selection of approaches - developed in different research projects - to overcome these gaps. Being designed for specific application domains, these approaches share commonalities as well as orthogonal solutions and can build the foundation of an overall Event-driven Spatial Data Infrastructure.

At least 15% of the population in the world has some type of disability. Unfortunately, this population has the problem of facing various accessibility barriers, to which technological barriers are also added. One of the most relevant obstacles is the one that arises as a result of the development of the technology itself when using the Information and Communication Technologies (ICT). Therefore, the objective of this article is to review the main uses of the Semantic Web tools and to group them together in order to be able to propose the design and construction of more personalized and flexible systems, which allows to help people with disabilities to perform some type of activity using ICT, in this way, knowledge can be modeled in different domains related to people who have some type of disability, using ontologies, and some ontological models can be reused for various types of disability depending on the case study. The usefulness of this study is to reveal that with the models presented, it is possible to construct a Meta ontology that includes some or all areas of disability.

The State of Kuwait seeks sustainable development through implementation of clear and specific urban plans, some of which suffer from a severe lack of geomorphological and spatially-based environmental planning, such as the use of geographic information systems (GIS). Choosing the optimal location for a city based on sound environmental planning is of the utmost importance to achieving environmental sustainability, as it can spare the State and other decision-making entities a great deal of stress in the long run. GIS, in particular, offers great potential for the environmental planner and decision-maker to choose the most appropriate places for the cities of the future, especially when coupled with geomorphological analyses. To achieve the Vision of Kuwait 2035, one of the State’s planning objectives focuses on Urban Planning for the Establishment of Environmental Cities that Achieve (UPEECA) environmental sustainability criteria. To gain a more holistic analysis related to the Vision, this study aims to: 1) Conduct suitability modelling for establishing new cities in Kuwait, 2) Assess the current 2005-2030 urban plan, and 3) Propose possible recommendations and solutions for potential urban problems. The study relies on integrating several methods to devise a framework that will aid researchers and decision-makers in selecting optimal locations for built structures based on analysis and modelling (e.g., digital elevation model, geologic mapping, geomorphology, natural hazards, heritage/archaeological sites, military areas, oil fields, soils)

The aim of this paper is to analyze the five grand parks in Dubai, United Arab Emirates (UAE) and provide a geoprocessing approach to different aspects such as sport, health, leisure, recreation, and public wellbeing. The study uses a hybrid of qualitative and quantitative approach as methodology. Sustainability offerings, accessibility for people of determination and special needs, typology and the geolocations of the grand parks plays crucial role in residents’ wellbeing. The paper concludes with recommendations for Dubai government to design new and innovative approaches to manage wellbeing of urban public places into the leisure environment for residents.

Flash flooding, a hazard which is triggered by heavy rainfall is a major concern in many regions of the world often with devastating results in mountainous elevated regions. We adapted remote sensing modelling methods to analyse one flood in July 2015, and believe the process can be applicable to other regions in the world. The isolated thunderstorm rainfall occurred in the Chitral River Basin (CRB), which is fed by melting glaciers and snow from the highly elevated Hindu Kush Mountains (Tirick Mir peak’s elevation is 7708 m). The devastating cascade, or domino effect, resulted in a flash flood which destroyed many houses, roads, and bridges and washed out agricultural land. CRB had experienced devastating flood events in the past, but there was no hydraulic modelling and mapping zones available for the entire CRB region. That is why modelling analyses and predictions are important for disaster mitigation activities. For this flash flood event, we developed an integrated methodology for a regional scale flood model that integrates the Tropical Rainfall Measuring Mission (TRMM) satellite, Geographic Information System (GIS), hydrological (HEC-HMS) and hydraulic (HEC-RAS) modelling tools. We collected and use driver discharge and flood depth observation data for five river sub-stream areas, which were acquired in cooperation with the Aga Khan Rural Support Program (AKRSP) organization. This data was used for the model’s calibration and verification. This modelling methodology is applicable for other regional studies especially for rough mountainous areas which lack local observations and river discharge gauges. The results of flood modelling are useful for the development of a regional early flood warning system and flood mitigation in hazardous flood risk areas. The flood simulations and prepared connected video visualization can be used for local communities. This approach is applicable for flood mitigation strategies in other regions.

This study aims to put forth a new method to study the socio-spatial boundaries by using georeferenced community-authored reviews for restaurants. In this study, we show that food choice, drink choice, and restaurant ambience can be good indicators of socio-economic status of the ambient population in different neighborhoods. To this end, we use Yelp user reviews to distinguish different neighborhoods in terms of their food purchases and identify resultant boundaries in 10 North American metropolitan areas. This data-set includes restaurant reviews as well as a limited number of user check-ins and rating in those cities. We use Natural Language Processing (NLP) techniques to select a set of potential features pertaining to food, drink and ambience from Yelp user comments for each geolocated restaurant. We then select those features which determine one’s choice of restaurant and the rating that he/she provides for that restaurant. After identifying these features, we identify neighborhoods where similar taste is practiced. We show that neighborhoods identified through our method show statistically significant differences based on demographic factors such as income, racial composition, and education. We suggest that this method helps urban planners to understand the social dynamics of contemporary cities in absence of information on service-oriented cultural characteristics of urban communities.

The epidemic of Coronavirus Disease 2019 (COVID-19) have affected all the regions of the world, nevertheless, in some countries there is a lack of studies on its main clinical and epidemiological features. We analyzed the incidence, incidence rates, and evolution of COVID-19 cases in Honduras from February 18-April 24, 2020.Methods: Using daily epidemiological data from surveillance about COVID-19 in Honduras, we calculated the rates of incidence (cases/100,000 population), and developed at national, departmental, and municipal levels GIS-based maps.Results: February 18 - April 24, 2020, a sum of 3,169 suspected COVID-19 cases have been assessed by RT-PCR, 533 (16.8%) of them were positive, for an incidence rate of 5.73 cases/100,000 pop. The highest peak was reached on March 31 (48 cases). The department with the highest number of cases and incidence rate was Cortes (383 cases, 71.9% of the total, 21.45 cases/100,000 pop). Discussion: The pattern and evolution of COVID-19 epidemic in Honduras has been particularly focused in the major urban areas, San Pedro Sula and Tegucigalpa, the capital city. Studies using geographical information systems linked with clinical disease characteristics are necessary to attain accurate epidemiological data for public health systems. Such information is also useful for assessment of the evolution of the pandemic and monitoring interventions.

Current global commitments to reduce emissions of greenhouse gases encourage national targets for renewable generation. Due to its small land mass, offshore wind could help Bahrain to fulfill its obligations. However, no scoping study has yet been carried out. The methodology presented here addresses this research need. It employs Analytical Hierarchy Process and pairwise comparison methods in a Geographical Information Systems environment. Publicly available land use, infrastructure and transport data are used to exclude areas unsuitable for development due to physical and safety constraints. Meteorological and oceanic opportunities are ranked, then competing uses are analyzed to deliver optimal sites for wind farms. The potential annual wind energy yield is calculated by dividing the sum of optimal areas by a suitable turbine footprint, to deliver maximum turbine number. Ten favourable wind farm areas were identified in Bahrain’s territorial waters, representing about 4% of the total maritime area, and capable of supplying 2.68 TWh/yr of wind energy or almost 10% of the Kingdom’s annual electricity consumption. Detailed maps of potential sites for offshore wind construction are provided in the paper, giving an initial plan for installation in these locations.

Although floods, as well as other natural disasters, can be considered relevant causes of intra-generational inequalities, the frequent catastrophes and the resulting damages to territory reflect the generalized indifference about inter-generational justice. Societal concerns, such as land protection, typically involve the administrative system performing proactive policies in the perspective of inter-generational solidarity, but subsidiarity has made more and more independent the local communities. As a consequence, the attention toward the long run effects – typically concerning the territorial system, as a whole, at the geographical scale – has been dispersed, and the proactive policies coming from the central government has became more ineffective. Regarding the case of the flood happened in 2009 in the Fiumedinisi-Capo Peloro hydraulic basin, in the northeastern part of Sicily, Italy, we propose an economic valuation – carried out by performing the method of the imputed preferences – in order to compare the expenses incurred by the public authorities responsible for protecting the territory to the costs of the rehabilitation of the damaged areas. Some considerations about the economic significance of the proactive policies for the arrangement of territory are addressed according to the role played by the social discount rate in the inter-temporal economic calculation.

Biogenic emissions affect the urban air quality as they are ozone and SOA precursors and should be taken into account when applying photochemical pollution models. The present study presents an estimation of the magnitude of Non-Methane Volatile Organic Compounds emissions (NMVOCs) emitted by vegetation over Greece. The methodology is based on computation performed with the aid of a Geographic Information System (GIS) and theoretical equations in order to develop an emission inventory on a 6x6 spatial resolution, in a temporal resolution of 1hr covering one year (2016). For this purpose, a variety of input data was used: improved satellite land-use data, land-use specific emission potentials, foliar biomass densities, temperature and solar radiation data. Hourly, daily and annual isoprene, monoterpenes and other volatile organic compounds (OVOCs) were estimated. In the area under study, the annual biogenic emissions were estimated up to 472 kt, consisting of 46.6% isoprene, 28% monoterpenes and 25.4% OVOCs. Results delineate an annual cycle with increasing values from March to April, while maximum emissions were observed from May to September, followed by a decrease from October to January.

With the availability of wearable health monitoring sensor modules like 3-Lead Electrocardiogram (ECG), Pulse Oximeter (SpO2), Galvanic Skin Response (GSR), Hall effect sensor (for measuring Respiratory Rate), Blood Pressure and Temperature measuring and sensing elements, it has now become possible to device a composite health status monitoring kit that can measure vital signs and other physiological parameters pertaining to human health in real time. Traditionally, the physiological parameters along with vital signs related examination was possible only in a hospitalized or ambulatory environment, however due to advances in sensing and embedded system technology and miniaturization of data acquisition and processing elements health monitoring has become possible even when individuals remain engaged in their day to day activities at the convenience of space and location. The patients or individuals subject to monitoring may suffer from a traumatic experience due to their medical condition and may need emergent incidence response and the critical care team may have to prepare for the treatment only after the patient arrives, which often is too late, as in case of cardiac arrests or severe injuries. The research focused on real-time health status monitoring and trauma scoring using standard physiological parameters along with standard telemetry protocols to make the critical care team aware of an emergent situation and prepare for a medical emergency. Vital signs and physiological parameters (heart rate, temperature, respiratory rate, and blood pressure, SpO2) were measured in real time from human subjects non-invasively. In order to enable monitoring of the patients engaged in day to day activities, errors due to the motion were removed using stationary wavelet transform correction (correlation coefficient of 0.9 after correction) and signals from various sensors were denoised, filtered and were encoded in a format suitable for further data analysis. A composite sensor kit capable of monitoring vital signs and physiological parameters can be very useful in incident response when an individual undergoes a traumatic experience related to stroke, cardiac arrest, fits or even injury, as along with monitoring information the kit can calculate scores related to trauma like the Injury Severity Score (ISS), National Early Warning Signs (NEWS), Revised Trauma Score (RTS). Trauma Injury Severity Score (TRISS), Probability of Survival (Ps) score. An open access database of vital signs and physiological parameters from Physionet, MIMIC 2 Numerics (mimicdb/numerics) database was used to calculate NEWS and RTS and to generate correlation and regression models using the vital signs/physiological parameters for a clinical class of patients with respiratory failure and admitted to Intensive Care Unit (ICU). NEWS and RTS scores showed no significant correlation (r = 0.25, p<0.001) amongst themselves, however together NEWS and RTS showed significant correlation with Ps (blunt) (r = 0.70, p<0.001). RTS and Ps (blunt) scores showed some correlation (r = 0.63, p<0.001) and NEWS score showed significant correlation (r = 0.79, p<0.001) with Ps (blunt) scores. Furthermore, since individuals have to be monitored regardless of location, these kits have to have a built-in capability to locate the individual so that the incident response team can locate the individual based on Global Positioning System coordinates (GPS). A Quantum GIS (Geographical Information System) application using real-time GPS coordinates (OpenStreetMap coordinates) was used to calculate the shortest path using QGIS Network Analysis tool to demonstrate the calculation of shortest path and direction to locate the nearest service provider in shortest time. Along with locating the nearest healthcare service provider, it would help if the critical care team could be made aware of the physiological parameters and trauma scores using standard protocols accepted across the globe. The physiological parameters from the sensors along with the calculated trauma scores were encoded according to a standard Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) coding system and International Code of Diseases (ICD) codes and the trauma information was logged to Electronic Health Records (EHR) using Fast Health Interoperability Resources (FHIR) servers. FHIR servers provided interoperable web services to log the event information in real time. It could be concluded that analytical models trained on existing datasets can help in analyzing a traumatic experience or an injury and the information can be logged using a standard telemetry protocol as a telemedicine initiative. These scores enable the healthcare service providers to estimate the extent of trauma and prepare for medical emergency procedures and find applications in general and military healthcare.

Merchant marine fleet is under inspections by several parties to ensure maritime regulation compliance. One of the major effects on implementation of regulations by International Maritime Organization (IMO) is indeed Port State Control. This article aims to analyze Paris Memoranda of Understanding (MOU) all detention remarks from 2013 to 2019 for EU15 countries (except Luxemburg and Austria) through an approach based on Analytical Hierarchy Process and demonstrate the results on Geographic Information System (GIS) to guide marine industry on detainable Port State Control remarks and country risk profile. While Analytical Hierarchy Process Approach has been used to indicate the ranking of basic maritime regulations from the perspective of the port state control , GIS help us to demonstrate the regional dispersion amongst EU15. The data of the detained vessel taken from the public website of Paris MOU and each report considered as a professional judgement that causes detention. It has been shown that almost all countries top priorities for regulation are Safety of Life at Sea (SOLAS) and Fire Safety Systems (FSS). Consequently, the results of the study can assist Port State Officers, ship crew, ship owners and managers presenting the facts of their inspection and able to improve themselves. The spatial analysis also expected to guide ship owners and managers to focus their vessel’s deficiencies to prevent sub-standardization.

On the occasion of Kynouria and in order to achieve the protection and projection of antiquities, a web-based model is proposed for highlighting individual monuments and archaeological sites, having in mind the historical and archaeological evidence of the region, the topography, the demographic profile and the tourist infrastructure, and combining them with the development programs for cultural routes. Therefore, creating suitable databases and mapping the monuments in the area are key prerequisites of the process, as they contribute to an objective assessment of the current situation and then to make rational decisions. In this frame, modern technology provides some important planning tools (GIS, GPS, and OMS) which allow recording and mapping of data, viewing the relationships between them in the area where they appear and managing their projection. The complete study of Kynouria’s archaeological routes contains the implementation of a website using free or open-source software, which should include all the necessary procedures and the historical and archaeological information material (text, maps, and photographs).

In recent years, Web APIs have become a de facto standard for exchanging machine-readable data on the Web. Despite this success though, they often fail in making resource descriptions interoperable due to the fact that they rely on proprietary vocabularies that lack formal semantics. The Linked Data principles similarly seek the massive publication of data on the Web, yet with the specific goal of ensuring semantic interoperability. Given their complementary goals, it is commonly admitted that cross-fertilization could stem from the automatic combination of Linked Data and Web APIs. Towards this goal, in this paper we leverage the micro-service architectural principles to define a SPARQL Micro-Service architecture, aimed at querying Web APIs using SPARQL. A SPARQL micro-service is a lightweight SPARQL endpoint that provides access to a small, resource-centric, virtual graph. In this context, we argue that full SPARQL Query expressiveness can be supported efficiently without jeopardizing servers availability. Furthermore, we demonstrate how this architecture can be used to dynamically assign dereferenceable URIs to Web API resources that do not have URIs beforehand, thus literally ``bringing'' Web APIs into the Web of Data. We believe that the emergence of an ecosystem of SPARQL micro-services published by independent providers would enable Linked Data-based applications to easily glean pieces of data from a wealth of distributed, scalable and reliable services. We describe a working prototype implementation and we finally illustrate the use of SPARQL micro-services in the context of two real-life use cases related to the biodiversity domain, developed in collaboration with the French National Museum of Natural History.

Here we introduce Literature Mapper, a Python QGIS plugin that provides a method for creating a spatial bibliography manager as well as a specification for storing spatial data in a bibliography manager. Literature Mapper uses QGIS’ spatial capabilities to allow users to add location information to a Zotero library, a free and open source bibliography manager. Literature Mapper enhances the citations in a user’s online Zotero database with geo-locations by storing spatial coordinates as part of traditional citation entries. Literature Mapper receives data from and sends data to the user’s online database via Zotero’s web API. By using Zotero as the backend data storage, Literature Mapper benefits from all of its features including shared citation Collections, public sharing, and an open web API usable by additional applications, such as web mapping libraries. We evaluate Literature Mapper’s ability to provide insights into the spatial distribution of published literature by mapping the study sites described in academic publications related to California’s coastal strand vegetation. The results of this exercise are presented in static and web map form.

Abstract Background: The highest incidence rate of covid-19 in Iran was reported from Shahroud County. This study was conducted by geographic information systems (GIS) to determine the geographical distribution of Covid-19 in 60 days. Study design: A cross-sectional study Methods: This study was conducted in counties covered by Shahroud University of Medical Sciences, namely Shahroud and Mayami, from February 20, 2020 to April 18, 2020. The GIS can better show the spread of epidemics. This software indicates geographical distribution of disease spread and is very helpful in controlling the epidemics. Therefore, maps of spatial distribution and risk of infection to COVID-19 were prepared in different regions of Shahroud county using Arc-GIS software to better implement health policies. Results: During this sixty-day period, 529 confirmed cases were detected, of which 51% were men and the average age was 55 years. The maps showed high-risk to risk-free regions. Shahroud and Bastam cities were known as the coronavirus hot spots. The eastern region of Shahroud has the highest number of cases but some high risk areas are spread throughout the Shahroud city due to high infectivity of virus. Risk-based time maps indicated a reduction in the risk of infection at the end of the research period due to some mitigation and suppression strategies. Conclusions: Shahroud and Bastam are the most dangerous cities that, the number of patients and dissemination has decreased over time because of tracking definite patients and people in contact with them and implementation of preventive care.

Solar3D is an open-source software application designed to interactively calculate solar irradiation at three-dimensional (3D) surfaces in a virtual environment constructed with combinations of 3D city models, digital elevation models (DEMs), digital surface models (DSMs) and feature layers. The GRASS GIS r.sun solar radiation model computes solar irradiation based on two-dimensional (2D) raster maps for given day, latitude, surface and atmospheric conditions. With the increasing availability of 3D city models and demand for solar energy, there is an urgent need for better tools to computes solar radiation directly with 3D city models. Solar3D extends GRASS GIS r.sun from 2D to 3D by feeding the model with input, including surface slope, aspect and time-resolved shading, that is derived directly from the 3D scene using computer graphics techniques. To summarize, Solar3D offers several new features which, as a whole, distinguish itself from existing 3D solar irradiation tools: (1) the ability to consume massive heterogeneous 3D city models, including massive 3D city models such as oblique airborne photogrammetry-based 3D city models (OAP3Ds or integrated meshes); (2) the ability to perform near real-time pointwise calculation for duration from daily to annual; (3) the ability to integrate and interactively explore large-scale heterogeneous geospatial data. (4) the ability to calculate solar irradiation at arbitrary surface positions including at rooftops, facades and the ground. Solar3D is publicly available at https://github.com/jian9695/Solar3D.

Highway alignment is an essential part of the highway planning and design phase. Highways are impacted by existing projects and surrounding context. The isolation of geo-technical analysis from highway planning and design also delays the planning process. This study therefore proposes a model that integrates building information modelling (BIM) and geographic information science (GIS) capabilities to facilitate the planning and design process. Semantic web technologies are used to integrate BIM and GIS data on a semantic level. The proposed model also helps to identify geohazards by providing geological analysis. The visualization of the proposed project can help reduce design errors and miscommunication, which, in turn, reduces project risk. In addition, the model facilitates highway alignment optimization by incorporating visualization, simulation, and analysis into the planning and design phase. The proposed model provides future opportunities for project professionals to have organized, reliable and dynamic ways to manage the project during construction.

The continuous increase in tuition fees in university education in many countries requires justification by the university authorities through improved learning resources that can guarantee employment opportunities for the students through hands-on industry training. This paper describes the design of curriculum of cloud computing module in collaboration with Amazon Web Services (AWS) Academy to include industry-based practical hands-on labs in the curriculum to improve the employability of Internet of Things (IoT) engineering students. This study introduces industry best practices and hands-on labs in cloud computing and discovered that students’ understanding and learning experience in the subject increase when exposed to real-life applications. The study blends academic theories in cloud computing with their applications as obtained in industry to enable students to have both the theoretical and practical skills that will prepare them for careers in cloud computing. To achieve this, cloud computing lecturers were trained by AWS Academy as a prerequisite to ensure that the tutors themselves acquire hands-on proficiency in cloud computing technologies. The study finds that students tend to be more engaged and learn better when academic theories and concepts are combined with real-world applications as obtained in the industry.

Failaka Island is located 20 kilometers east of the Kuwait mainland. The island includes archaeological sites dating back to the Bronze, Hellenistic, Christian, and Islamic ages. To develop the island as a tourist attraction the state is pursuing a new urban plan based on the island's environmental potential. This study is the basis of the urban plan depends on environmental criteria from the view of Geoarchaeology. The study analysis the land-use and land-cover changes of Failaka Island between 1958- 2018. It provides a topographic survey of the island's coastline and a classification of its geomorphological features and a state of the art identification of its archaeological sites by using a drone to make a terrain model. The study used a medium to high-resolution image analysis of the land-use and land-cover changes: WorldView2-50cm 2010 and 2018; Landsat 8; aerial photography; Drone images and a digital elevation model (DEM), to analysis the expected sea level changes by the end of this century. This study also created a geodatabase of the island that can be adapted for future studies. The results emphasize the importance of preserving the historical and ecological features of the island while developing its infrastructure.

The main restriction of the Semantic Web is the difficult of the SPARQL language, that is necessary to extract information from the Knowledge Representation also known as ontology. Making the Semantic Web accessible for people who do not know SPARQL, is essential the use of friendlier interfaces and a good alternative is Natural Language. This paper shows the implementation of a friendly prototype interface to query and retrieve, by voice, information from website building with the Semantic Web tools. In that way, the end users avoid the complicated SPARQL language. To achieve this, the interface recognizes a speech query and converts it into text, it processes the text through a java program and identifies keywords, generates a SPARQL query, extracts the information from the website and read it in voice, for the user. In our work Google Cloud Speech API makes Speech-to-Text conversions and Text-to Speech conversions are made with SVOX Pico. As results, we have measured three variables: The success rate in queries, the response time of query and a usability survey. The values of the variables allows the evaluation of our prototype. Finally the interface proposed provides us a new approach in the problem, using the Cloud like a Service, reducing barriers of access to the Semantic Web for people without technical knowledge of Semantic Web technologies.

COVID-19 breakout calls for immediate research explorations. The objective of this study is to perform a bibliometric analysis of all COVID-19-related publications in Science Citation Index Expanded (SCI-EXPANDED) in the early stage of the outbreak. Analysis parameters include performances of authors, institutes, and countries as well as distributions of Web of Science categories, journals, languages, and types of publications. Results show that 32% of total papers were published as editorial materials and an overwhelming production from Chinese research institutes. An association of research indexes with the number of cases was also found.

The necessity to investigate suitable alternatives to conventional fossil fuels has developed the interests in many renewable energy alternatives, especially biomass resources which are widely available and allow to reach both environmental and socio-economic improvements. Among the bioenergy solutions the anaerobic digestion technology makes it possible to produce biogas by reusing and valorising agricultural residues and by-products. In Southern Italy, to date, the development of biogas sector is still very limited, despite the importance of the agricultural sector, especially of citrus and olive cultivation. For this reason, in previous studies the availability of two by-products, i.e., citrus pulp and olive pomace, was analysed in order to choose the most suitable area for a sustainable development of new biogas plants according to the new Biogasdoneright concept. In this paper, after a resume of the multi-step methodology which allowed the computation of biogas production, it was demonstrated that 15.9 GWh-e electricity and 24.5 GWh-e heat per year could be generate by reusing only these two kind of by-products, and could satisfy approximate 17% of the total electricity demand of the agricultural sector (90.2 GWh-e/year) in Catania.

Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence of Free and Open-Source Models to estimate the sea-level impact can contribute to better coastal man-agement. This study aims to develop and to validate two different models to predict the sea-level rise impact supported by Google Earth Engine (GEE) – a cloud-based platform for planetary-scale environmental data analysis. The first model is a Bathtub Model based on the uncertainty of projections of the Sea-level Rise Impact Module of TerrSet - Geospatial Monitoring and Modeling System software. The validation process performed in the Rio Grande do Sul coastal plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses Bruun Rule formula implemented in GEE and is capable to determine the coastline retreat of a profile through the creation of a simple vector line from topo-bathymetric data. The model shows a very high cor-relation (0.97) with a classical Bruun Rule study performed in Aveiro coast (NW Portugal). The GEE platform seems to be an important tool for coastal management. The models developed have been openly shared, enabling the continuous improvement of the code by the scientific commu-nity.

Marketing is one of the important stages, the decisive factor affecting the success of production and business activities of every business. Therefore, marketing is considered to be the first and most important stage in the process of introducing, bringing products to market and branding of businesses, especially in the period when the disease situation is taking place very complicated worldwide. This study provides both qualitative and quantitative results for the following research objectives. Firstly, the study will assess the situation and analyze the appropriateness of marketing models in the current market situation trend. Secondly, the study proposes an approach in building a new type of marketing model, which is 3D web-based marketing applied in e-commerce to support the modeling of enterprise products in the form of Interactive 3D products are similar to real products. Thirdly, testing and evaluating 3D e-commerce web marketing model at Truong Hai Auto Showroom, Thai Nguyen City branch. This study also proposes a number of solutions for the research and deployment of 3D web marketing model application for businesses in the current market situation.

The water quality of the river is becoming deteriorated due to human interference. It is essential to understand the relationship between human activities and land-use types to assess the water quality of a region. GIS has the latest tool for analyzing the spatial correlation. Land use land cover and change detection is the best illustration to show the human interactions on land features. The study assessed water quality index of upper Ganga River near Haridwar, Uttarakhand and spatially correlated them with changing land use to reach a logical conclusion. At the upper course of Ganga along 78 Km long from Kaudiyala to Bhogpur, water samples were collected from five stations. For water quality index the physicochemical parameters like pH, EC, DO, TDS, CaCO3-, CaCO3, Cl¯, Ca++, Mg++, Na+, K+, F-, Fe2+ were considered. The result of the spatial analysis was evaluated through error estimation and spatial correlation. The root mean square error between spatial land use and water quality index of selected sampling sites was estimated as 0.1443. The spatial correlation between land-use change and site-wise differences in water quality index has also shown a high positive correlation with R² = 0.8455. The degree of positive correlation and root mean square error has strongly indicated that the water quality of the river at the upper course of Ganga is highly impacted through human activities.

There is a serious concern regarding framing rules and guidelines to regulate the content on OTT platforms that needs urgent attention. In the Current Scenario, the use of vulgar language in the public domain on OTT platforms needs to take seriously. Some OTT platforms heavenly use foul language, which is open to children that cause a huge psychological impact on them. The use of vulgar language, including profanity and bad words, has to be regulated strictly. Web series uses obscene, abusive words and foul language with excessive usage. This review paper highlights the excessive use of swear words, profane language, and vulgar expletives on OTT platforms.

In this paper we describe the potentialities of a tool for the visualization of experimental results directly on a three-dimensional model. The case study concerns the visualization of the results of a dynamic finite element analysis (FEA/FEM) applied to the calculation of seismic risk on works belonging to the Italian infrastructural heritage, specifically bridges, viaducts and overpasses. The project is based on finite element analysis performed on an exemplary set of 8 structures located on the Italian territory, performed by means of the open-source software framework OpenSees, according to the guidelines indicated in the Technical Standards for Construction NTC08. The application created for this project is classifiable as a webGIS, since all data are georeferenced and visualized on a map through an application executed through a browser. The graphical interface displays the interested works on the map of the Italian territory and allows to select them by mouse click. Following the selection, a 3D rendering of the model of the work and the surrounding terrain is shown, in which the results of the analysis are represented using color gradients directly on the three-dimensional model. The necessary tools are present for the selection of the type of result and for the animation in real time of the response of the work to the seismic action. The 3D representation is freely navigable by the user thanks to intuitive tools of panning, rotation and zoom through mouse and keyboard. The application takes advantage of HTML5, CSS and Javascript to show graphical features such as Cartesian diagrams of accelerograms used in modal analysis.

The interaction between different regions normally is reflected by the form of the stream. For example, the interaction of the flow of people and flow of information between different regions can reflect the structure of cities’ network, and also can reflect how the cities function and connect to each other. Since big data has become increasingly popular, it is much easier to acquire flow data for various types of individuals. Currently, it is a hot research topic to apply the regional interaction model, which is based on the summary level of individual flow data mining. So far, previous research on spatial interaction methods focused on point-to-point and area-to-area interaction patterns. However, there are a few scholars who study the hotspot interaction pattern between two regional groups with some predefined neighborhood relationship by starting with two regions. In this paper, a method for identifying a similar hotspot interaction pattern between two regional groups has been proposed, and the Geo-Information-Tupu methods are applied to visualize the interaction patterns. For an example of an empirical analysis, we discuss China’s air traffic flow data, so this method can be used to find and analyze any hotspot interaction patterns between regional groups with adjoining relationships across China. Our research results indicate that this method is efficient in identifying hotspot interaction flow patterns between regional groups. Moreover, it can be applied to any analysis of flow space that is used to excavate regional group hotspot interaction patterns.

Background: Zika virus (ZIKV) infection has significantly affected Latin America in 2015–2017. Most studies have been reported from Brazil and Colombia, and only a few from Central America. For these reasons we analyzed the incidence, incidence rates and evolution of cases in Honduras from 2016–2017. Methods: Using epidemiological weeks (EW) surveillance data on the ZIKV epidemics in Honduras, we estimated incidence rates (cases/100,000 population), and developed maps at national, departmental and municipal levels. Results: From 1 January 2016 to 31 December 2017, a total of 32,607 cases of ZIKV were reported (98.5% in 2016 for an incidence rate of 36.85 cases/100,000 pop; 1% confirmed by RT-PCR). The highest peak was reached on the EW 6°, 2016 (2,559 cases; 29.34 cases/100,000 pop). The department with the highest number of cases and incidence rate was Cortés (13,128 cases, 791.08 cases/100,000 pop in 2016). Discussion: The pattern and evolution of ZIKV infection in Honduras has been similar to that which occurred for chikungunya in 2015. As previously reported, infection with chikungunya involved predominantly the central and capital area of the country, reaching incidences there >750 cases/100,000 pop. Studies using geographical information systems linked with clinical disease characteristics are necessary to attain accurate epidemiological data for public health systems. Such information is also useful for assessment of risk for travelers who visit specific areas in a destination country.

Secure development is a proactive approach to cyber security. Rather than building a technological solution and then securing it in retrospect, secure development strives to embed good security practices throughout the development process and thereby reduces risk. Unfortunately, evidence suggests secure development is complex, costly, and limited in practice. This article therefore introduces security-focused prototyping as a natural precursor to secure development that embeds security at the beginning of the development process, can be used to discover domain specific security requirements, and can help organisations navigate the complexity of secure development such that the resources and commitment it requires are better understood. Two case studies–one considering the creation of a bespoke web platform and the other considering the application layer of an Internet of Things system–verify the potential of the approach and its ability to discover domain specific security requirements in particular. Future work could build on this work by conducting case studies to further verify the potential of security-focused prototyping and even investigate its capacity to be used as a tool capable of reducing a broader, socio-technical, kind of risk.

Background: After serious epidemics of chikungunya (CHIKV) and Zika (ZIKV) in the Americas, dengue (DENV) have reemerged in most countries. We analyzed the incidence, incidence rates, and evolution of DENV cases in Honduras from 2015-2018 and the ongoing 2019 epidemic. Methods: Using epidemiological weeks (EW) surveillance data on the DENV in Honduras, we estimated incidence rates (cases/100,000 population), and developed maps at national, departmental, and municipal levels. Results: From 1 January 2016 to 21 July 2019, a total of 109,557 cases of DENV were reported, 28,603 in 2019, for an incidence rate of 312.32 cases/100,000 pop this year; 0.13% laboratory-confirmed. The highest peak was reached on the EW 28°, 2019 (5,299 cases; 57.89 cases/100,000 pop). The department with the highest number of cases and incidence rate was Cortes (8,404 cases, 479.68 cases/100,000 pop in 2019). Discussion: The pattern and evolution of DENV epidemic in 2019 in Honduras has been similar to that which occurred for in 2015. As previously reported, this epidemic involved the north and central areas of the country predominantly, reaching municipality incidences there >1,000 cases/100,000 pop (1%). Studies using geographical information systems linked with clinical disease characteristics are necessary to attain accurate epidemiological data for public health systems. Such information is also useful for assessment of risk for travelers who visit specific areas in a destination country.

Remote sensing technology, especially using satellite images, has become essential support in many aspects of decision-making, particularly in disaster risk management. It requires a shorter period of data updates and less cost compared to conventional field observations and surveys. Yet, the intensive processing and high-powered computing resources are necessary to analyze satellite imagery data through Geographic Information System (GIS). In this paper, we introduce the identification and mapping of natural disaster impact in Indonesia using the open-source collaborative tool of Google Earth Engine (GEE) application which analyzes the relative temporal difference of Earth surface from three major satellite images: Sentinel-1, Sentinel-2, and Landsat-8. Taking the advantage of the geographical, geological, and demographic conditions of Indonesia's disaster-prone areas, we analyze relative difference from normalized difference vegetation index (NDVI) out of months before and after natural disaster occurrence to measure the impact of natural disaster in focus study areas. Given the high-vegetation nature of three main natural disaster impacted areas in Indonesia: Aceh, Palu, and Yogyakarta, we are able to simplify the analysis by highlighting areas with vegetative loss or gain after the event. Using an open-source GEE application, namely HazMapper, we identify and visualize the aftermath of the tsunami disaster in Aceh and Palu as well as the earthquake in Yogyakarta. Our study is potentially beneficial for government and decision-makers to utilize publicly available satellite images for disaster recovery and mitigation policy.

It is widely acknowledged that the integration of BIM and GIS data is a crucial step forward for future 3D city modelling, but most of the research conducted so far has covered only the semantic aspects of GIS-BIM integration. We present here the results of the GeoBIM project, in which we tackled three integration problems focussing instead on aspects involving geometry processing: (i) the automated processing of complex architectural IFC models, (ii) the integration of existing GIS subsoil data in BIM, and (iii) the georeferencing of BIM models for their use in GIS software. All the problems have been studied using real world models and existing datasets made and used by practitioners in the Netherlands. For each problem, we expose in detail the issues we faced, our proposed solutions, and our recommendations for a more successful integration.

The development of microbial products for cancer treatment has been in the spotlight in recent years. In order to accelerate the lengthy and expensive drug development process, in silico screening tools are systematically employed, especially during the initial discovery phase. Moreover, considering the steadily increasing number of molecules approved by authorities for commercial use, there is a demand for faster methods to repurpose such drugs. Here we present a review on virtual screening web tools, publicly available databases of molecular targets and libraries of ligands, with the aim to facilitate the discovery of potential anticancer drugs based on microbial products. We provide an entry-level step-by-step description of the workflow for virtual screening of microbial metabolites with known protein targets, as well as two practical examples using freely available web tools. The first case presents a virtual screening study of drugs developed from microbial products using Caver Web, a web tool that performs docking along a tunnel. The second case comprises a comparative analysis between a healthy isocitrate dehydrogenase 1, a mutant that results in cancer, using the recently developed web tool PredictSNP^Onco. In summary, this review provides the basic and essential background information necessary for virtual screening experiments, which may accelerate the discovery of novel anticancer drugs.

The authors introduce the GRASS GIS add-on module g.citation as an initial implementation of a fine-grained software citation concept. The module extends the existing citation capabilities of GRASS GIS, which until now only provide for automated citation of the software project as a whole, authored by the GRASS Development Team, without reference to individual persons. The functionalities of the new module enable individual code citation for each of the over 500 implemented functionalities, including add-on modules. Three different classes of citation output are provided in a variety human- and machine-readable formats. The implications of this reference implementation of scientific software citation for both for the GRASS GIS project and the OSGeo foundation are outlined.

In WPA3 secure connection is executed in two sequential stages. Firstly, in authentication and association stage a pairwise master key (PMK) is generated. Secondly, in post-association stage a pairwise transient key (PTK) is generated from PMK using the traditional 4-way handshake protocol. To reduce the heavy computation of the first stage PMK caching can be used. If client and AP are previously authenticated and has PMK cache, client can skip the first heavy stage and reuse the cached PMK to directly execute the 4-way handshake. But PMK caching is a very primitive technology to manage shared key between client and AP and there are many limitations; AP has to manage stateful cache for multiple clients, cache lifetime is limited, etc. Paired token (PT) \cite{LZ} is a new secondary credential scheme that provides stateless pre-shared key (PSK) in client-server environment. Server issues paired token (public token and secret token) to authenticated client where public token has the role of signed identity and secret token is a kind of shared secret. Once client is equipped with PT, it can be used for many symmetric key based cryptographic applications such as authentication, authorization, key establishment, etc. In this paper we apply the PT approach to WPA3 and try to replace the PMK caching with the one-time authenticated key establishment using PT. At the end of the authentication and association stage AP securely issues PT to client. Then in reassociation stage client and AP can compute the same one-time authenticated PMK from PT in stateless way and compute PTK using the traditional 4-way handshake protocol. Using this kind of stateless reassociation technology AP can provide high performance service to huge number of clients.

The internet has changed the way people perceived fiction to a new level. For instance, online forums have given people the opportunity to write without revealing their real identities. Especially in the Saudi context, online users were using these forums to write web novels that reflect their culture, lives, concerns, hopes and dreams. In this paper, we describe a dataset that was collected from one of the online forums that was used for sharing web novels among its readers. The collected dataset contains 1,267 novels between 2003-2015. This data set is available to the research community to analyze to gain a better understanding of the social, economical, and behavioral mindset that was manifested in the community in that decade.

Vehicle routing is a critical factor in municipal solid waste (MSW) collection planning and operations. Poor routing can introduce inefficiencies and cause targeted levels of services or performance to be missed irrespective of the level of resource application. Trial and error approaches have been proven to be not the best in the planning and prediction of expected performance. This study explores various Geographic Information System (GIS) tools and analysis techniques, and how they can be applied to optimizing vehicle routes in light of challenging site conditions. Using Adentan West residential area, suburb of Accra Ghana as a case study, current performance of the trial and error method was measured and a GIS computer model was used to evaluate various optimization scenarios to determine the level of savings that can be made. Field measurements were taking with Global Positioning System (GPS) devices for waste collection activities in areas with varying characteristics and conditions, and data analysed for one selected vehicle operating four days per week. It was found that, for a scenario where only the bin collection order was optimized while route selection was restricted by the ArcGIS Network Analyst, 2.6% of travel distance and 2.21% of travel time were saved. For the second scenario where only the route selection was optimized while order of bin collection was restricted, 4.1% and 1.5% of travel distance and time respectively were saved. For a third scenario where both the order of collection and route selection were together optimized, 10.9% and 3.7% of travel distance and time respectively were saved. Lastly, by regrouping all the bins for daily collection, 4.5% and 1.2% of travel distance and time respectively were saved. The results demonstrated that there is always room for optimization of solid waste collection routing irrespective of site constraints and other challenges that the nature of bin distribution pose to drivers. In developing countries like Ghana, where there is high demand for services in the face of limited road network access, application of GIS in optimization of routes will guide providers in planning and subsequently make more savings in fuel consumption, vehicle maintenance and cost of man-hours.

El Niño is a quasi-periodic pattern of climate variability and extremes often associated with hazards and disease. While El Niño links to individual diseases have been examined, less is known about the cluster of multi-disease risk referred to as an ecosyndemic, which emerges during extreme events. The objective of this study was to explore a mapping approach to represent the spatial distribution of ecosyndemics in Piura, Peru at the district-level during the first few months of 1998. Using geographic information systems and multivariate analysis, two methodologies were employed to map disease overlap of 7 climate-sensitive diseases and construct an ecosyndemic index, which was then mapped and applied to another El Niño period as proof of concept. The main findings showed that many districts across Piura faced multi-disease risk over several weeks in the austral summer of 1998. The distribution of ecosyndemics were spatially clustered in western Piura among 11 districts. Furthermore, the ecosydemic index in 1998 when compared to 1983 showed a strong positive correlation, demonstrating the utility of the index. The study supports PAHO efforts to develop multi-disease based and interprogrammatic approaches to control and prevention, particularly for climate and poverty-related infections in Latin America and the Caribbean.

Electric energy has become essential nowadays not only for the daily life of each of us but also for the economy of different countries. The dissemination of geographic information plays an important role in national development as it facilitates communication between managers, investors, and consumers in this sector. Since the management of electricity network data was previously done in Tunisia based on paper maps and plans, the purpose of this article is to present a case of planning based on GIS, Web, and 3D Web GIS, which would have significant positive consequences on this sector from a technical and financial sides with an improvement in customer satisfaction and the creation of an intelligent electricity network which will be a real decision-making tool. This work draws up an inventory of the network MV (Medium Voltage)/LV (Low Voltage) of the region of Medjez El Bab which routes electricity to the big centers of consumption with access to MV/LV subscribers. The analysis of the network's impedance allowed carrying out different scenarios to optimize performance and obtain more realistic routes. Many thematic maps were produced as part of this project (Slope map, Land use map, map of the MV voltage domains, map of the MV/LV transformer stations power, etc.). A three-dimensional virtual city has been developed to visualize the graphical and attribute data for the study area. A Web and 3D Web GIS applications that allows the publication of the interactive maps on the Web as well as the database information have been developed to offer users the possibility of consulting the produced products by internet. Finally, a website related to the study was developed.

Wikipedia, as "social machine", is a privileged place to observe the collective construction of concepts without central control. Based on Dahlberg's theory of concept, and anchored in the pragmatism of Hjørland - in which the concepts are socially negotiated meanings - the evolution of the concept of Semantic Web (SW) was analyzed in the English version of Wikipedia. An exploratory, descriptive and qualitative study was designed and we identified 26 different definitions (between 7-12-2001 and 12/31/2017) of which 8 are of particular relevance for their duration, with the latter being the two recorded at the end of the analyzed period. According to them, SW: "is a extension of the web"and "is a Web of Data"; the latter, used as a complementary definition, links to Berners-Lee's publications. In Wikipedia, the evolution of the SW concept appears to be based on the search for the use of non-technical vocabulary and the control of authority carried out by the debate. As a space for collective bargaining of meanings, the Wikipedia study may bring relevant contributions to a community's understanding of a particular concept and how it evolves over time.

Growing interest is awarded to the digitalization of the building permitting use case and many works are developed about the topic. However, the subject is very complex and many aspects are usually tackled separately, making it very hard for traditional literature reviews to grasp the actual progress in the overall topic. This paper unveils the detailed state of the art in Digital Building Permitting (DBP) by critically analyzing the literature by means of a set of coding tags (research progress, implementation, affected DBP workflow steps, ambitions addressed) assigned by a multidisciplinary team. The mainly addressed aspects of the digitalization of building permits resulted to be the technologies to check the compliance of design proposals against regulations, followed by the digitalization of regulations. Lacking aspects are instead the involvement of officers, scalability of solutions and interoperability of data, intended both as data validation and as integration of geoinformation with building models.

Coal combustion in thermal power plants releases ash. Ash is reported to cause different adverse health hazards in humans and other organisms. Owing to the presence of radionuclides, it is also considered as a potential radiation hazard. In this study, based on the surface radiation measurements and relevant ancillary data, expected radiation risk zones were identified with regard to the human population residing near the Thermal Power Plant. With population density as the risk determining criteria, about 20% of the study area was at ‘High’ risk and another 20% of the study area was at ‘Low’ risk zone. The remaining 60% was under medium risk zone. Based on the findings remedial measures which may be adopted have been suggested.

Sediment accumulation in a dam reservoir is a common happening environmental problem throughout the world. Topographic conditions, land use land cover change, the intensity of rainfall, and the soil characteristics are the major driving factors for sedimentation to occur. The effect of sedimentation in a dam reservoir is very visible in the watershed as a result of hilly topographic conditions, high rainfall intensity, thin land cover, and less soil infiltration capacity. In this paper, an integrated RUSLE and GIS technique was implemented to estimate a mean annual sediment yield based on spatial and temporal variations in Nashe dam reservoir situated in Fincha catchment, Abaya River basin, Ethiopia. Spatial and temporal estimation of mean annual sediment yield was estimated using the Revised Universal Soil Loss Equation (RUSLE) model and GIS. Historical 6-year (2014-2019) rainfall for the temporal variations and other physical factors such as soil erodibility, slope and length steepness, management and land used land cover, and support practice for spatial variations were used as sediment driving factors. The mean annual sediment yield ranges from 0 to 2712.65 t ha-1 year-1 was seen. Spatially, Very high, high, moderate, low, and very low sediment yield severity with total area coverage with 25%, 10%, 30%, 15%, and 20% in 2017, 2015, 2019, 2014, and 2018 respectively. The information about the spatial and temporal variations of the severity of sediment yield in RUSLE model has a paramount role to control the entry of sediment into the dam reservoir in this watershed. The results of the RUSLE model can also be further considered along with the watershed for planning strategies for dam reservoirs in the catchment.

In recent years, rainfall-induced waterlogging has become a common hazard in the highly urbanized coastal city of Chattogram, Bangladesh resulting in high magnitude of property damage and economic loss. Therefore, the primary objective of this research is to prepare a waterlogging inventory map and understand the spatial variation of the risk by means of hazard intensity, exposure, and vulnerability of waterlogging. In this research, the inventory map and factors influencing waterlogging hazard were determined from a participatory survey and other spatial data including land elevation, population, and structural data were collected from secondary sources. Analytical Hierarchy Process was applied to measure the hazard intensity and the exposure and vulnerability were estimated by overlaying the spatial data onto the hazard intensity map. A total of 58 locations in 22 wards have been identified as waterlogging affected, which covers ~8.42% of the city area. Obtained waterlogging vulnerability index map suggests that ward no. 5, 6, 16, 17, and 33 are greatly vulnerable to waterlogging in terms of their social, infrastructure, critical facilities, economic and environmental vulnerability. We show that ~2.71% of the study area is at very high risk, while the risk score is considerably higher for ward no. 5, 8, 17, 19, and 33.

As the amount of sensor data made available online increases, it becomes more difficult for users to identify useful datasets. Semantic web technologies improve discovery with meaningful ontologies, but the decision of suitability remains with the users. The GEO label provides a visual summary of the standardised metadata to aid users in this process. This work presents novel rules for deriving the information for the GEO label's multiple facets, such as user feedback or quality information, based on the Semantic Sensor Network Ontology and related ontologies. It enhances an existing implementation of the GEO label API to generate labels for resources of the Semantic Sensor Web. The prototype is deployed to serverless cloud infrastructures. We find that serverless GEO label generation is capable of handling two evaluation scenarios for concurrent users and burst generation. More real-world semantic sensor descriptions and an integration into large scale discovery platforms are needed to develop the presented solutions further.

Telehealth is the delivery of many health care services and technologies to individuals at different geographical areas and is categorized as asynchronously or synchronously. The coronavirus disease 2019 (COVID-19) pandemic has caused major disruptions in health care delivery to breast cancer (BCa) patients and there is increasing demand for telehealth services. Globally, telehealth has become an essential means of communication between patient and health care provider. The application of telehealth to the treatment of BCa patients is evolving and increasingly research has demonstrated its feasibility and effectiveness in improving clinical, psychological and social outcomes. Two areas of telehealth that have significantly grown in the past decade and particularly since the beginning of the COVID-19 pandemic are telerehabilitation and teleoncology. There two technological systems provides opportunities at every stage of the cancer care continuum for BCa patients. We conducted a systematic literature review that examined the use of telehealth services via its various modes of delivery among BCa patients particularly in areas of screening, diagnosis, treatment modalities, as well as satisfaction among patients and health care professionals. The advantages of telehealth models of service and delivery challenges in delivery to patients in remote arears are discussed.

Currently, most middleware application developers have two choices when designing or implementing API services: They can either stick with REST or explore the emerging GraphQL technology. Although REST is widely considered the standard method for API development, GraphQL is believed to overcome the main drawbacks of REST, especially data fetching issues. Yet, doubts remain as there are no experimental studies with convincing results to evaluate the performance of the two services. In this paper, we propose a research methodology to evaluate the performance of REST and GraphQL API services, which includes two main contributions. The first contribution is that the performance evaluation of the two services is conducted in the real operation of a massive and intensively accessible management information system. The second contribution is the fair and independent performance evaluation results obtained for both API services. The performance evaluation was studied using basic measures of QoS, including response time, throughput, CPU load, and memory usage. The results show that REST is still faster in response time and throughput, while GraphQL is very efficient in resource utilization, i.e., CPU load and memory utilization. Therefore, GraphQL is the right choice when data requirements change frequently and resource utilization is the most important consideration. REST is used when some data is frequently accessed called by multiple requests.

Land suitability analysis is a basic premise for allocating specific land for specific purpose. The objective of this study was to predict the suitable sites for cultivating Moringa oleifera tree in Ethiopia using Spatial Analytic Hierarchy Process. Findings of this study will have paramount significance in supporting decision making in the agroforestry development sector. This study employs Spatial Analytic Hierarchy Process and Geographic Information System to generate valuable information in land allocation for moringa oleifera tree production. Climate, topography, soil type and land use parameters were evaluated for suitability analysis. The results of the study revealed that most of the central part of the country are categorized as moderately suitable for the production of moringa oleifera tree. Areas classified as highly suitable are distributed along the borders of southern and western part of the country. However, some of the central part was classified as not suitable for Moringa oleifera tree production. This paper tried to investigate analysis of spatial data to predict suitable site for moringa tree production at national level. At national level, highly suitable, moderately suitable, and not suitable class covers an area of 308,508.2, 1,628,930.8 and 59891.3 Square Kilometer respectively.

Sea-level rise is a problem increasingly affecting coastal areas worldwide. The existence 15 of Free and Open-Source Models to estimate the sea-level impact can contribute to better coastal 16 management. This study aims to develop and to validate two different models to predict the 17 sea-level rise impact supported by Google Earth Engine (GEE) – a cloud-based platform for plan-18 etary-scale environmental data analysis. The first model is a Bathtub Model based on the uncer-19 tainty of projections of the Sea-level Rise Impact Module of TerrSet - Geospatial Monitoring and 20 Modeling System software. The validation process performed in the Rio Grande do Sul coastal 21 plain (S Brazil) resulted in correlations from 0.75 to 1.00. The second model uses Bruun Rule for-22 mula implemented in GEE and is capable to determine the coastline retreat of a profile through the 23 creation of a simple vector line from topo-bathymetric data. The model shows a very high correla-24 tion (0.97) with a classical Bruun Rule study performed in Aveiro coast (NW Portugal). The GEE 25 platform seems to be an important tool for coastal management. The models developed have been 26 openly shared, enabling the continuous improvement of the code by the scientific community.

Over the years, researchers have been developing different ventilation strategies as a potential solution for controlling infectious airborne transmissions in healthcare facilities. The effects of ventilation systems on airflow characteristics have been extensively discussed among researchers since SARS-CoV-2 outbreak in 2019. Based on a bibliometric study of 154 publications from 1970 to 2021 retrieved from the Web of Science (WoS) database, this paper examines the research landscape on the effects of ventilation strategies on airflow analysis in healthcare facilities. Results show that China is the most productive country due to its significant contributions to the top prolific authors and funding agencies, which have promoted the research development of the topic. Several research hotspots were identified through keyword co-occurrence analysis. Cluster 1 highlights the ventilation studies involving ventilation layouts and ventilation rates in healthcare facilities. Cluster 2 underlines the computational analysis of ventilation performance for reducing the exposure risk of occupants, which promotes the development of infection control measures. Cluster 3 strengthens the knowledge of the emission and dispersion characteristics of respiratory droplets in healthcare facilities. Overall, this paper highlights the future direction of the current research field other than identifying strategic collaborators and suitable publication outlets for interested researchers.

Abstract Geographic Information System and Remote Sensing played an important role in analyzing environmental and socio-economic drivers that created favorable condition for malaria breeding as well as in identifying hazard and risk areas. This study gives great emphasis on mapping malaria hazard and risk areas in Gedio zone of SNNPs using geospatial technology. The study identifies two major drivers like Environmental (physical) factors: which provide for the endurance of mosquitoes and Socio-economic factors. The above data were presented and analyzed quantitatively. The content analysis shows that Malaria hazard prevalence areas were mapped based on the environmental factors which are potential of providing good environmental conditions for mosquito breeding. The hazard map was produced using elevation, slope, proximity to breeding sites, and soil type as the factors for breeding mosquitoes. The malaria hazard analysis of the Gedio zone revealed that from the total area, 9.83%, 35.29% is mapped as a very high and high-risk area, whereas, the remaining 38.73%, a 16.14%, and 0.01% were mapped as moderate, low, very low level of malaria hazard respectively. The total area of the study area more than 1/3rd of the area is identified as a very high and high malaria risk area while the rest 2/3rd of an area is considered as a moderate to very low hazard risk zone. Accordingly, very high malaria risk area is found around towns because of population density. Finally, I recommend that the concerned body should have to expand health center, creating awareness of society, especially around populated areas where the risk is high and environmental and individual sanitation can reduce the risk of malaria.

Land price sustainability issues have been addressed by many authors in the past. Most of these researchers used land prices (from land price maps) as the primary data source in their studies. Only a few papers analysed official land price maps, which are available very rarely. For this reason, we studied the spatial and temporal changes of land prices in the city of Olomouc based on an analysis of official land price maps from 1993 to 2017. We proposed several research hypotheses to confirm some general statements about land price development. We concluded that some macroeconomic indicators had a significant impact on changes in land prices. In the residential and commercial areas and historical centre, land prices are significantly higher than in other monitored aspects (land-use types). We also concluded that no link existed between land-use stability and land price stability. Surprisingly, no long-term stable areas were found in the area of interest. The analysis also confirmed that land price and its change over time varied in different spatial aspects. Surprisingly, the smallest influence was reflected in the economic aspect. Regarding natural events in recent decades, we observed a significant drop in land prices in the vicinity of watercourses threatened by flooding. These findings can assist in better understanding local development and changes in land price.

The scarcity of surface water resources in the dry season in the Kilinochchi district increases the demand for freshwater. Therefore, the existing groundwater resources should be managed to overcome the situation. Several authors worldwide have published studies on the delineation of potential groundwater zone. However, only a few studies addressed the delineation of potential groundwater zones in the Kilinochchi district. This study aims to delineate potential groundwater zones in Kilinochchi, Sri Lanka using integrated Remote Sensing, Geographic Information Systems, and Analytic Hierarchy Process techniques. Groundwater potential zones are demarcated for the Kilinochchi district by overlaying thematic layers: geology, geomorphology, land use/land cover, soil types, drainage density, slope, lineament, and rainfall. Saaty's scale was applied to the assigned weights of the chosen thematic layers and their features. The thematic layers were integrated into a Geographic Information System, and a weighted overlay analysis is carried out to delineate groundwater zones. Thus the resultant map is categorized into five different potential zones: very low, low, moderate, high, and very high. It was found that the very high groundwater potential zone is mainly found in the north-eastern part of the study area covering 111.26 km2. The upper north-western, middle, and eastern parts of the study area fall within the high groundwater potential zone covering about 507.74 km2. The moderate groundwater potential zones (309.89 km2) mainly occurred in the western part, and the extreme west part of the study area falls under low (207.78 km2) and very low (59.12 km2) zones. The groundwater potential map was validated with the existing seventy-nine wells, which indicated a good prediction accuracy of 81.8%. This research will help policymakers better manage the Kilinochchi district's groundwater resources and gives scope for further research into groundwater exploration in the area.

Many spatial decision support systems suffer from user adoption issues in practice due to lack of trust, technical expertise, and resources. Automated machine learning has recently allowed non-experts to explore and apply machine learning models in the industry without requiring abundant expert knowledge and resources. This paper reviews recent literature from 136 papers, and proposes a general framework for integrating spatial decision support systems with automated machine learning to lower major user adoption barriers. Challenges of data quality, model interpretability, and practical usefulness were discussed as general considerations for system implementation. Research opportunities related to spatially explicit models in AutoML, and resource-aware, collaborative/connected, and human-centered systems were also discussed to address these challenges. This paper argues that integrating spatial decision support systems with automated machine learning can not only encourage user adoption, but also mutually benefit research in both fields — bridging human-related and technical advancements for fostering future developments in spatial decision support systems and automated machine learning.

Drug design is used for different applications of bioinformatics tools analyze DNA, genome, and sequence target region of a small organic molecule in order to understand the molecules of disease. Bioinformatics tools are identified a newly wide research field and minimize future risks through web servers and data mining. Clinical sample test performed with the bioinformatics tools as the biomedical detective. A particular structure and configuration of protein obliging in Drug design concluded Bioinformatics. This review bioinformatics tools and webserver will discuss functions of small organic molecules according to clinical pharmacology.

The province of Macerata, Italy, is a topographically complex region which has been little studied in terms of its temperature and precipitation climatology. Temperature data from 81 weather stations and precipitation data from 55 rain gauges were obtained, and, following quality control procedures, were investigated on the basis of 3 standard periods: 1931-1960, 1961-1990 and 1991-2014. Spatial and temporal variations in precipitation and temperature were analysed on the basis of six topographic variable (altitude, distance from the sea, latitude, distance from the closest river, aspect, and distance from the crest line). Of these, the relationship with altitude showed the strongest correlation. Use of GIS software allowed investigation of the most accurate way to present interpolations of these data and assessment of the differences between the 3 investigated periods. The results of the analyses permit a thorough evaluation of climate change spatially over the last 60 years. Generally, the amount of precipitation is diminished while the temperature is increased across the whole study area, but with significant variations within it. Temperature increased by 2 to 3°C in the central part of the study area, while near the coast and in the mountains the change is between about 0 and 1°C, with small decreases focused in the Appennine and foothill belt (-1 to 0°C). For precipitation, the decrease is fairly uniform across the study area (between about 0-200 mm), but with some isolated areas of strong increase (200-300 mm) and only few parts of territory in which there is an increase of 0-200 mm, mainly in the southern part of the coast, to the south-west and inland immediately behind the coast. The monthly temperature trend is characterized by a constant growth, while for precipitation there is a strong decrease in the amount measured in January, February and October (between 25 and 35 mm on average).

A number of research projects and a rich literature have dealt with the theme of abandoned medieval villages in Sardinia since the end of the 60s of the last century. Some more or less precise catalogues and reviews of villages in limited territories have been published. Only recently, however, this subject is being addressed in an interdisciplinary manner, combining traditional historical research with the results of archaeological surveys and excavation campaigns, geo-archaeology, toponymy, paleoclimatology. This allows us to have a picture of the landscape and human settlement evolution with its historical changes, conditioned not only by institutional superstructures but also by human and natural traumatic events. Particular attention will be given to the sudden changes that occurred between the thirteenth and fourteenth centuries. To carry out this survey, it is possible to use some very powerful IT tools which, through the aggregation, organization, correlation and management of information, allow the geo-localization of abandoned villages as proven by the documentary evidence. Thus, on this documentation, existing and acquired in the future, is founded the construction of the related information system. The most easy and suitable tools for this purpose are the CMSs (Content Management Systems) which, in association with GIS (Geographic Information Systems) engines, allow spatial and contextual analysis of the settlements, as they were inserted in their territory. This type of tools aggregates different peculiarities of the object of study, supporting a multidisciplinary reading on the argument. The computerized tools, integrated as a system, offer the possibility to implement it, feeding it and correcting it continuously, basing on new acquisitions. In this study, we will examine a historical areal, for which we have a sufficient number of sources available: Sarrabus, Colostrai and Quirra, adding to the geographical visual information, the temporal visual evolution.