Visual Analytics of Spatial Time Series Data

Information

• Publication Type: Master Thesis
• Workgroup(s)/Project(s):
  • Visualization Group
• Date: October 2021
• Date (Start): 5. January 2021
• Date (End): 12. October 2021
• Diploma Examination: 12. October 2021
• Open Access: yes
• First Supervisor: Kresimir Matkovic
• Pages: 60
• Keywords: visual analytics, interactive visual analysis, machine learning, linear regression, elastic net, K-Means, classification, spatial time series, economic recession, recession factors

Abstract

Regardless of what algorithms and technologies are developed, the human mind and logical reasoning remain important tools for analysing, modelling, and solving problems. Visual representation of data is considered the most e˙ective way to convey information to the human brain and promote analytical thinking. Visual analytics encompasses a set of techniques, methods, and tools that support analytical thinking through visual representations of various types of data. Due to their complexity and size, spatial time series data are suitable for implementation of such techniques, as their analysis remains challenging. Many environmental, social, and economic processes of modern civilization are represented by spatial time series, which emphasises the need for interactive visual representations for their more eÿcient analysis. One clear example of such complex processes is economic recession, a decline in economic activity for which there is no single formal definition. However, it is often described in terms of recession factors such as GDP, the Gini index, or inflation, all of which are examples of spatial time series data, and whose change can be a clear indicator of the state of the economy. As recession analysis is a very complex topic and it is not entirely clear which economic factors have the greatest impact, purely automated techniques are not appropriate and there is scope for advances in analytical approaches. This thesis proposes an application “Recession Explorer”: visual analytics of economic recession and its forecasting as an example of a holistic system that displays spatial time series data and explores patterns and insights in the data. Such a combination of approaches provides a unique perspective on economic recession studies by facilitating both high-level human reasoning and the use of advanced mathematical algorithms. The goal of the application is to demonstrate that the use of visual analytics is a beneficial approach to address the challenges of economic recession and, more generally, to assist users with interactive visualisations when dealing with and analysing spatial time series data.

Additional Files and Images

Weblinks

• Entry in reposiTUm (TU Wien Publication Database)
• DOI: 10.34726/hss.2021.73924

BibTeX

@mastersthesis{Priselac2021,
  title =      "Visual Analytics of Spatial Time Series Data",
  author =     "Marija Priselac",
  year =       "2021",
  abstract =   "Regardless of what algorithms and technologies are
               developed, the human mind and logical reasoning remain
               important tools for analysing, modelling, and solving
               problems. Visual representation of data is considered the
               most e˙ective way to convey information to the human brain
               and promote analytical thinking. Visual analytics
               encompasses a set of techniques, methods, and tools that
               support analytical thinking through visual representations
               of various types of data. Due to their complexity and size,
               spatial time series data are suitable for implementation of
               such techniques, as their analysis remains challenging. Many
               environmental, social, and economic processes of modern
               civilization are represented by spatial time series, which
               emphasises the need for interactive visual representations
               for their more eÿcient analysis. One clear example of such
               complex processes is economic recession, a decline in
               economic activity for which there is no single formal
               definition. However, it is often described in terms of
               recession factors such as GDP, the Gini index, or inflation,
               all of which are examples of spatial time series data, and
               whose change can be a clear indicator of the state of the
               economy. As recession analysis is a very complex topic and
               it is not entirely clear which economic factors have the
               greatest impact, purely automated techniques are not
               appropriate and there is scope for advances in analytical
               approaches. This thesis proposes an application “Recession
               Explorer”: visual analytics of economic recession and its
               forecasting as an example of a holistic system that displays
               spatial time series data and explores patterns and insights
               in the data. Such a combination of approaches provides a
               unique perspective on economic recession studies by
               facilitating both high-level human reasoning and the use of
               advanced mathematical algorithms. The goal of the
               application is to demonstrate that the use of visual
               analytics is a beneficial approach to address the challenges
               of economic recession and, more generally, to assist users
               with interactive visualisations when dealing with and
               analysing spatial time series data.",
  month =      oct,
  pages =      "60",
  address =    "Favoritenstrasse 9-11/E193-02, A-1040 Vienna, Austria",
  school =     "Research Unit of Computer Graphics, Institute of Visual
               Computing and Human-Centered Technology, Faculty of
               Informatics, TU Wien",
  keywords =   "visual analytics, interactive visual analysis, machine
               learning, linear regression, elastic net, K-Means,
               classification, spatial time series, economic recession,
               recession factors",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2021/Priselac2021/",
}