Visualising Data Patterns and Structure with Graphical Techniques
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This document discusses techniques for visual analysis and visualization. It explains that visualizations can reveal patterns and structure in data by mapping data variables to graphical dimensions. However, the viewer also plays a role in interpreting the visualization and developing a mental model to understand differences. Various techniques are described for visualizing different data types like time series, trees, and networks. Gestalt principles of visual perception are also referenced to explain how viewers perceive structure and patterns in visualizations.
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Visualising Data Patterns and Structure with Graphical Techniques
- 1. Visualising the difference: revealing pattern and structure through graphical techniques Tony Hirst Dept of Communication and Systems The Open University
- 11. Information required to generate a visualisationVSInformation revealed by a visualisation
- 15. Visualisations can make structure evident
- 19. Variable encoding:Data variable -> graphical dimension
- 20. BUT…
- 21. To what extent does the viewer use the visualisation to inform the creation of a model that they then interpret in order to spot the differences that make a difference in the visualisation?
- 22. Seeing Structure in Tabular Data
- 29. Trees: levels or containers?
- 32. When you get the structure wrong….Marimekko/mosaic charts vs flow chart http://bit.ly/qhZfbB http://junkcharts.typepad.com/junk_charts/2011/08/false-promises-of-equality-and-structure.html
- 33. 2 x 7 seven data types ( 1-, 2-, 3-dimensional data, temporal and multi-dimensional data, tree and network data ) seven tasks ( overview, zoom, filter, details-on-demand, relate, history, extract ) From: The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations Ben Schneiderman,IEEE Symposium on Visual Languages, 1996
- 35. Schneiderman’s “Visual Information Seeking Mantra” Overview first,zoom and filter,then details-on-demand From: The Eyes Have It:A Task by Data Type Taxonomy for Information Visualizations
- 42. Time Series Data
- 44. “Banking to 45 degrees” (Cleveland) “The aspect ratio is vital because it has a large impact on our ability to judge rate of change. A number of studies in visual perception have shown that our ability to judge the relative slopes of line segments on a graph is maximized when the absolute values of the orientations of the segments are centered on 45 degrees.”
- 47. Gestalt Theory of Visual Perception
- 50. Pragnanz
- 54. To what extent does the viewer use the visualisation to inform the creation of a model that they then interpret in order to spot the differences that make a difference in the visualisation?
- 55. Information required to generate a visualisationVSInformation revealed by a visualisation
- 56. (Probably no time for)QUESTIONS…? http://blog.ouseful.info @psychemedia “Mandelbrot Set Fractal - Milky Way” - dominicspics
- 57. "Every block of stone has a statue inside it and it is the task of the sculptor to discover it.”- Michelangelo
Hinweis der Redaktion
- 20 years ago, arrived at OU as a postgrad. During early stages of my research, was looking at dynamical systems models as a basis for adaptive agent behaviour. One of most beautiful things I’d ever seen was this diagram in a COGS tech report by Andy Wuensche. BUT: how could I draw such things programmatically? Never had really got into graphics, though in my electronics degree I had done my fair share of programming.Two classes of problem. In first case: 1) how to draw lines and nodes just anyway; 2) how to lay out lots of lines and nodes. I moved on to other things…. But recently, worth noting there has been a flowering of code libraries as well as applications that provide quite useable interfaces onto layout algorithms.Second class of problem: computing power. Faster machines means that it is now possible to process complex layout algorithms over large datasets in near real time; and if the process does run slow, it may be possible to turn this to some sort of advantage in user experience terms by providing an animation that shows how the algorithm is laying out a data set.Finally, an observation that others have made – screen size hasn’t really changed. (Then, when it comes to the difference that makes the difference, number of pixels per radian of visual angle that we are really concerned about…)
- My own introduction to visualisation came to the fore with the MPs expenses row. Data was being made available at the time, even before the Telegraph obtained details about actual expenditure, in the form of summary data relating to the total amount of expenses claimed under different expense areas. In particular, one dataset that intrigued me was travel expenses. I see to remembers that the data, as released, was a little bit scrappy to work with, but the newly formed Guardian datastore made it readily available via a Google spreadsheet.
- Possible to sort tables eg by column, but can also do sorting in the visual domain…
- The difference that makes the difference – right is Gold, not Bronze…
- So who won most bronze medals in Swimming? US or Australia?
- Possible to sort tables eg by column, but can also do sorting in the visual domain…
- The simplicity principleAlthough visual stimuli are fundamentally multi-interpretable, the human visual system usually has a clear preference for only one interpretation. To explain this preference, SIT introduced a formal coding model starting from the assumption that the perceptually preferred interpretation of a stimulus is the one with the simplest code. A simplest code is a code with minimum information load, that is, a code that enables a reconstruction of the stimulus using a minimum number of descriptive parameters. Such a code is obtained by capturing a maximum amount of visual regularity and yields a hierarchical organization of the stimulus in terms of wholes and parts.The assumption that the visual system prefers simplest interpretations is called the simplicity principle.[5] Historically, the simplicity principle is an information-theoretical descendant of the Gestalt law of Prägnanz,[6] which was based on the natural tendency of physical systems to settle into stable minimum-energy states. Furthermore, just as the later-proposed minimum description length principle in algorithmic information theory (AIT), it can be seen as a formalization of Occam's Razor in which the best hypothesis for a given set of data is the one that leads to the largest compression of the data.
- Dominicspics