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

Leo N. Geppert (), Katja Ickstadt (), Fabian Karl (), Jonas Münch () and Michael Steinbrecher ()
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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

A chapter in Artificial Intelligence, Big Data and Data Science in Statistics, 2022, pp 301-319 from Springer

Abstract: 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.

Keywords: Visual analysis loop; Tracking data; Data representation; Derived information; Limitations of data; Static vs. interactive visualisation (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-07155-3_13

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DOI: 10.1007/978-3-031-07155-3_13

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