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Visualising Complex Data Within a Data Science Loop: A Spatio-Temporal Example from Football

In: Artificial Intelligence, Big Data and Data Science in Statistics

Author

Listed:
  • Leo N. Geppert

    (TU Dortmund University, Department of Statistics)

  • Katja Ickstadt

    (TU Dortmund University, Department of Statistics)

  • Fabian Karl

    (TU Dortmund University, Institute of Journalism)

  • Jonas Münch

    (TU Dortmund University, Department of Statistics)

  • Michael Steinbrecher

    (TU Dortmund University, Institute of Journalism)

Abstract

The cross-sectional research area of data visualisation plays an important role in data science. Graphical presentations provide an accessible way to understand distributions, outliers, processes, trends and patterns in data, and to separate signal from noise. Visualisation tools support the data scientist in representing and analysing Big Data and/or data streams. They are a central tool in all steps of the data science loop. In this contribution we will point out some pitfalls when visualising complex data and will give recommendations on how to avoid them. We will go into more detail about different roles of visualisations, in particular, covering the roles of exploration and presentation and the role of the viewer (data scientist, practitioner, public). For demonstration, we will be using two example data sets from association football.

Suggested Citation

  • Leo N. Geppert & Katja Ickstadt & Fabian Karl & Jonas Münch & Michael Steinbrecher, 2022. "Visualising Complex Data Within a Data Science Loop: A Spatio-Temporal Example from Football," Springer Books, in: Ansgar Steland & Kwok-Leung Tsui (ed.), Artificial Intelligence, Big Data and Data Science in Statistics, pages 301-319, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-07155-3_13
    DOI: 10.1007/978-3-031-07155-3_13
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