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School of Computer Science The craft of Information Visualization NCRM Research Methods Festival 2008 Jonathan C. Roberts School of Computer Science Bangor.

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Presentation on theme: "School of Computer Science The craft of Information Visualization NCRM Research Methods Festival 2008 Jonathan C. Roberts School of Computer Science Bangor."— Presentation transcript:

1 School of Computer Science The craft of Information Visualization NCRM Research Methods Festival 2008 Jonathan C. Roberts School of Computer Science Bangor University

2 2 J.C.Roberts Minard’s plot z http://www.math.yorku.ca/SCS/Gallery/re-minard.html The French engineer, Charles Minard (1781-1870), illustrated the disastrous result of Napoleon's failed Russian campaign of 1812.

3 3 J.C.Roberts The 1854 London Cholera Epidemic. One of the first uses of a map to display epidemiological data was this dot chart (from Tufte, 1983, p. 24) by Dr. John Snow (1855) showing deaths from cholera (dots) in relation to the locations of public water pumps.Dr. John Snow Tufte says, "Snow observed that cholera occurred almost entirely among those who lived near (and drank from) the Broad Street water pump. He had the handle of the contaminated pump removed, ending the neighborhood epidemic which had taken more than 500 lives."

4 4 J.C.Roberts Advantages of Information Visualization Visualization provides: 1. The ability to comprehend huge amounts of information 2. The perception of emergent properties that were not anticipated 3. problems with the data to be made apparent (e.g. errors or artefacts of the data) 4. Large/Small scale features can be seen 5. facilitation of hypothesis formation

5 5 J.C.Roberts Schematic of the visualization process Data Pre-processing And transformation Graphics Engine z Human Physical Env. Social Env. Data gathering Data manipulation

6 6 J.C.Roberts Things to consider… Six important aspects of an Information Visualization: Data Visual Structures Multiple Views Interaction & Exploration Tasks (& Management of tasks) Level & organization

7 7 J.C.Roberts 1. Data & Visual Structures.. maps interesting data items to graphics objects Bertin methodology maps the CONTENT (information to be transmitted - filtered data) to the CONTAINER (the properties of the display/graphic system) using a COMPONENT analysis.

8 8 J.C.Roberts Bertin COMPONENT analysis Bertin’s component analysis invariant and variational components number of Components length of Components organisation of Components

9 9 J.C.Roberts Bertin CONTAINER - graphic system properties Representation Styles diagrams, networks, maps, symbols Retinal Variables Level of organisation –point, line, area, volume Main retinal Variables: Position Size Colour (Hue, saturation, value) Orientation Shape Texture Additional retinal variables Motion – velocity Motion – direction Flicker – frequency Flicker – phase

10 10 J.C.Roberts Different Mappings 2 variables Independent and Dependent When an experiment is conducted, some variables are manipulated by the experimenter (these are called “independent variables”) and others are measured from the subjects (these are “dependent variables” or “dependent measures.” independent dependent

11 11 J.C.Roberts Different Mappings 3..4 variables independent dependent The values are extra dependent values on the same independent parameter.

12 12 J.C.Roberts The data table… (spreadsheet) This is ok when there is only one independent variable. But what if we have multiple independents? independent dependent

13 13 J.C.Roberts 2D.. 3D

14 14 J.C.Roberts Multivariate, Car Variable Car1 Car2 MPG3243 Weight1000kg1100kg Top Speed 130140 0-6045 Cylinders86

15 15 J.C.Roberts Scatter Plot Matrices Reorderable matrix Scatter Plot Matrices

16 16 J.C.Roberts Parallel-coordinates (PC or ||-coords) Parallel coordinates yield graphical representations of multi- dimensional relations rather than just finite points sets. Place the axis parallel and join the dots Euclidean 3d geometry. X,y,z coordinates –Point in space is given by extents along the axis ||-coordinates. Point is a line

17 17 J.C.Roberts So what is a point… A n-d point is equivalent to a line in ||-coordinates http://catt.bus.okstate.edu/jones98/parallel.html

18 18 J.C.Roberts Point line duality Line in Euclidean The line is represented by the crossing l

19 19 J.C.Roberts Cubes.. Parallel coordinates provides a very simple representation of high dimensional objects such as hypercubes. Consider the Parallel coordinate plot of the four corners of a two- dimensional square:

20 20 J.C.Roberts Interacting with ||- Coordinates http://software.fujitsu.com/en/symfoware/visualminer/vmpcddemo.pdf

21 21 J.C.Roberts Selecting a range of records

22 22 J.C.Roberts Selecting records

23 23 J.C.Roberts Verifying a hypothesis

24 24 J.C.Roberts Highlighting relationships

25 25 J.C.Roberts Separating different record groups

26 26 J.C.Roberts Another observation

27 27 J.C.Roberts Visual Structures - Techniques Graphical properties: placing appropriate marks Substitute different properties with different marks Aligning data on different axes composing data Overlaying data on top

28 28 J.C.Roberts Multiple Views Display different information in different views [Waltz, Roberts] cdv - Cartographic Visualization for Enumerated Data [Dykes] Same color

29 29 J.C.Roberts Dual views – focus+context Dual views [Roberts] Table Lens

30 30 J.C.Roberts Multiple View Techniques Different views may be better at displaying that information Correlations between views can be highlighted Through brushing or zooming One view can be for Focus another for context (focus+context) One view can be for Overview another for detail (overview+detail) Distortion can be used to (say) place more information in a small area

31 31 J.C.Roberts 4. Interaction & Exploration Allow the user to change their mind and explore the data To provide sliders/buttons/menus to choose how the data is to be viewed To select a subset of the information (zoom into this…) E.g. Brushing –a collection of techniques to dynamically query and directly select elements on the visual display. –Usually in dual views (or more) –Such interaction allows the user to explore the visualization to interactively select a subset of points and see how these changes are updated in other related views.

32 32 J.C.Roberts Zoom To focus, Select (or highlight) a feature set of information –Zoom: telephoto-lens, reduced field of view –3D clipping –Semantic zoom Alternate Representations [Roberts, Ryan]

33 33 J.C.Roberts Dynamic queries Instant update –Direct manipulation –Sliders/buttons Example of a dynamic queries environment created with IVEE Measurements of heavy metals in Sweden FilmFinder: Ahlberg, Shneiderman

34 34 J.C.Roberts Interaction Techniques Dynamic Queries (indirect manipulation) Direct Manipulation Overlays (e.g. magic lens) Coordination of views which are coordinated? how are they coordinated?

35 35 J.C.Roberts Filter & Extract Visual extraction –constant quantity of information –brush and highlight visually altered to stand out (colour, size...) –sliders (1 < highlight < 4...) Subset (filter) of the data –extract portions of the dataset –Specialize semi-automatic/manual (seed-point, selection) neighborhood / global operations 1 1 1 1 1 1 2 3 2 1 1 3 9 3 1 1 2 3 2 1 1 1 1 1 1 1 2 3 2 1 1 3 9 3 1 1 2 3 2 1 1 1 1 1 1 1 2 3 2 1 1 3 9 3 1 1 2 3 2 1 1 1 1 1 1

36 36 J.C.Roberts Filter & Extract Visual extraction –constant quantity of information –brush and highlight visually altered to stand out (colour, size...) –sliders (1 < highlight < 4...) Subset (filter) of the data –extract portions of the dataset –Specialize semi-automatic/manual (seed-point, selection) neighborhood / global operations 1 1 1 1 1 1 2 3 2 1 1 3 9 3 1 1 2 3 2 1 1 1 1 1 1 1 2 3 2 1 1 3 9 3 1 1 2 3 2 1 1 1 1 1 1 1 2 3 2 1 1 3 9 3 1 1 2 3 2 1 1 1 1 1 1

37 37 J.C.Roberts Filter & Extract Visual extraction –constant quantity of information –brush and highlight visually altered to stand out (colour, size...) –sliders (1 < highlight < 4...) Subset (filter) of the data –extract portions of the dataset (isolate) –Specialize/Generalize semi-automatic/manual (seed-point, selection) neighborhood / global operations 1 1 1 1 1 1 2 3 2 1 1 3 9 3 1 1 2 3 2 1 1 1 1 1 1 1 2 3 2 1 1 3 9 3 1 1 2 3 2 1 1 1 1 1 1 3 3

38 38 J.C.Roberts 5. Tasks (& Management of tasks) Foraging for data Solving problems and investigating hypothesis Searching for some data (or the lack of data) Making quantitative/qualitative analysis Querying and finding evidence for decision making

39 39 J.C.Roberts Techniques to perform the Task Overview Zoom Filter Details on demand Browse Search Read (facts or patterns) Compare Manipulate Explore Create Disseminate and present From. Readings in information visualization - Card/Mackinlay

40 40 J.C.Roberts 6. Level & organization What is the right level-of-detail? –Are there too many points on display (abstract/summarize/bin/aggregate) How is the information organized? Think what is close and what is near –Near objects are easier to compare –E.g. re-order the axes on a ||-coord plot

41 41 J.C.Roberts Techniques for Level Delete Re-order Cluster Class Promote Average Abstract/Summarize Instantiate Extract Compose Organize

42 42 J.C.Roberts Things to remember… Six important aspects of an Information Visualization: Data Visual Structures Multiple Views Interaction & Exploration Tasks (& Management of tasks) Level & organization

43 School of Computer Science The craft of Information Visualization Jonathan C. Roberts School of Computer Science Bangor University END


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