Presentation of data mining applications in web based geovisual analytical environment: Example of COVID-19 vaccine tweets

Mekânsal görsel analitik, mekânsal bilgilerin etkileşimli görsel ara yüzlerle ele alındığı analitik akılyürütme bilimidir. Mekânsal görsel analitik sistemleri sayesinde, Twitter gibi sosyal medyaplatformlarındaki büyük veri setlerinden bir konu hakkında elde edilen veriler son kullanıcıya etkileşimliharitalama sistemleriyle sunulabilir. 11 Mart 2020’de Dünya Sağlık Örgütü’nün COVID-19 salgınınıduyurmasının ardından Twitter veri trafiğinde de ciddi bir artış görülmüştür. Bu çalışmada, COVID-19salgını döneminin önemli tartışmalarından biri olan COVID-19 aşıları hakkındaki tweet trafiğininzamansal ve mekânsal gelişimi veri madenciliği teknikleriyle incelenmiş ve görsel analitik ortamdasunulmuştur. Bu çalışma ile twitter gibi sosyal medya platformlarının sahip olduğu büyük veri olarakkabul edilen veri setlerinin veri madenciliği yöntemleriyle analiz edilerek afet ve kriz yönetimi açısındanönemli çıkarımlar yapılabileceği ortaya konmuştur.Spatial visual analytics is the science of analytical reasoning in which spatial information is handled with interactive visual interfaces. Thanks to spatial visual analytics systems, data obtained from large data sets on social media platforms such as Twitter can be presented to the end user with interactive mapping systems. After the World Health Organization announced the COVID-19 outbreak on March 11, 2020, there has been a significant increase in Twitter data traffic. In this study, the temporal and spatial development of tweet traffic about COVID-19 vaccines, which is one of the important discussions of the COVID-19 epidemic period, was examined with data mining techniques and presented in a visual analytical environment. With this study, it has been revealed that important inferences can be made in terms of disaster and crisis management by analyzing the data sets, which are accepted as big data, of social media platforms such as twitter with data mining methods

Subsurface Characterization by Means of Geovisual Analytics

This Thesis is concerned with one of the major problems in subsurface characterizations emerging from ever-increasing loads of data in the last decades: What kind of technologies suit well for extracting novel, valid and useful knowledge from persistent data repositories for the characterization of subsurface regions and how can such technologies be implemented in an integrated, community-open software platform? In order to address those questions, an interactive, open-source software platform for geoscientific knowledge discovery has been developed, which enables domain experts to generate, optimize and validate prognostic models of the subsurface domain. Such a free tool has been missing in the geoscientific community so far. The extensible software platform GeoReVi (Geological Reservoir Virtualization) implements selected aspects of geovisual analytics with special attention being paid to an implementation of the knowledge discovery in databases process. With GeoReVi the human expert can model and visualize static and dynamic systems in the subsurface in a feedback cycle. The created models can be analyzed and parameterized by means of modern approaches from geostatistics and data mining. Hence, knowledge that is useful to both the assessment of subsurface potentials and to support decision-making during the utilization process of the subsurface regions can be extracted and exchanged in a formalized manner. The modular software application is composed of both integrated and centralized databases, a graphical user interface and a business logic. In order to fulfill the needs of low computing time in accordance with high computational complexity of spatial problems, the software system makes intense use of parallelism and asynchronous programming. The competitiveness of industry branches, which are aimed at utilizing the subsurface in unknown regions, such as the geothermal energy production or carbon capture and storage, are especially dependent on the quality of spatial forecasts for relevant rock and fluid properties. Thus, the focus of this work has been laid upon the implementation of algorithms, which enhance the predictability of properties in space under consideration of uncertainty. The software system was therefore evaluated in ample real-world scenarios by solving problems from scientific, educational and industrial projects. The implemented software system shows an excellent suitability to generically address spatial problems such as interpolation or stochastic simulation under consideration of numerical uncertainty. In this context, GeoReVi served as a tool for discovering new knowledge with special regard to investigating the heterogeneity of rock media on multiple scales of investigation. Among others, it could be demonstrated that the three-dimensional scalar fields of different petrophysical and geochemical properties in sandstone media may diverge significantly at small-scales. In fact, if the small-scale variability is not considered in field-scale projects, in which the sampling density is usually low, statistical correlations and thus empirical relationships might be feigned. Furthermore, it could be demonstrated that the simple kriging variance, which is used to simulate the natural variability in sequential simulations, systematically underestimates the intrinsic variability of the investigated sandstone media. If the small-scale variability can be determined by high-resolution sampling, it can be used to enhance conditional simulations at the scale of depositional environments

An exploration of water poverty in Lao People’s Democratic Republic

The United Nations recently revised and published new Sustainable Development Goals for the next 15 years. At least six of the 17 goals are directly linked to water, and several others are indirectly affected by water issues. Water is central to achieving the goals and water-related issues have been identified by the World Economic Forum as some of the biggest risks the world is facing in the future. Efficient measures to address the issues require integrated approaches, such as the Water Poverty Index (WPI). WPI is a holistic tool to assess water resources in an integrated way, combining water resource availability, social dimensions of access and capacity to manage water resource as well as the environmental requirements for utilization of water. This thesis examines the spatio-temporal distribution and the causes of water poverty in Lao PDR through WPI. Laos is located in Monsoon Asia with extreme seasonal differences in water availability. Due to this seasonality, WPI is developed in a manner that allows computing dry and wet season WPI separately and comparing them in a meaningful way. Exploratory (spatial) data analysis as well as spatial data mining methods are employed to investigate the distribution and causes of water poverty. The research is based on freely available data, and R code used in the analyses are openly published at http://markokallio.fi/waterpoverty/. Significant spatial and temporal differences are found. Water poverty is high in the rural areas and in the mountains, while the low-lying lands near the Mekong river exhibit relatively low water poverty. Three provinces; Xekong, Oudomxai and Phongsaly are very poor, while the area around Vientiane Capital show least water poverty. Major difference is found also between seasons with WPI increasing in the water-rich more than in the water-poor areas as the wet season starts. In addition, it was found that in some locations, water poverty is higher during the wet season than in the dry season. The main causes driving water poverty are found to be Use and Access related, and in relative terms, Capacity related (especially village road access). Resource availability is problematic mainly in the western and northwestern provinces during the dry season

A decision support tool to improve binational water quality planning and management in the lower Rio Grande/Río Bravo

This dissertation describes the development of a decision support tool designed to facilitate and enhance collaborative binational decision making associated with integrated transboundary water quality planning and management in the Lower Rio Grande/Río Bravo. The Lower Rio Grande Water Quality Initiative Decision Support System (LRGWQIDSS) is the result of a multidisciplinary effort to integrate the results of qualitative social science research and traditional and novel engineering and geographic information systems (GIS) methods associated with the modeling, analysis, and visualization of watershed, water quality and natural resources data. The LRGWQIDSS incorporates information currently used by urban planning and natural resource management organizations working along the Texas-Mexico border area and provides a means to analyze and display the information in a way that is useful to institutional and noninstitutional actors involved in transboundary water quality planning efforts. The analysis of the institutional arrangements currently in place to protect water quality in the Lower Rio Grande/Río Bravo played an important role in the design and development of the LRGWQIDSS and its successful application. The tool’s development represents a case study in the importance of the role of institutional analysis in the successful development of decision support systems for transboundary water quality management.Public Polic