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Flashcards

Data Visualization

Simon Lauridsen Data Visualization English 35 cards

Data Visualization

Cards in this deck (35)

  1. 1THE VISUALIZATION PIPELINETHE VISUALIZATION PIPELINE
  2. 2WHAT IS DATA VISUALIZATION?Both a product and a process
  3. 3What is the composition of color?Hue = Wavelength
    Saturation = Intensity from gray to pure, vivid color
    Brightness = Lightness from black to colorful to white    What is the composition of color?
  4. 4What is LuminanceVisually perceived brightness, not uniformed definedWhat is Luminance
  5. 5Name three GESTALT PRINCIPLES OF GROUPINGName three GESTALT PRINCIPLES OF GROUPING
  6. 6What are the four data typesWhat are the four data types
  7. 7What is EXPRESSIVENESS?Showing the data and only the data
  8. 8How to consider EXPRESSIVENESS?A visualization is effective if it exploits the ccapabilities of the output
    medium and the human visual system    How to consider EXPRESSIVENESS?
  9. 9What does Appropriateness mean for a data viz.A visualization is appropriate if it balances the tradeoff between efforts
    required for creating it and the benefits yielded by it.
  10. 10What are the two main ways we can design the interface for the viz?OVERVIEW + DETAIL and FOCUS + CONTEXTWhat are the two main ways we can design the interface for the viz?
  11. 11When and how should we use multiple coordinated views ?Baldonado rules:
    Rule of Diversity: Use MCV in case of many attributes, purposes, users,…
    • Rule of Complementarity: Use MCV to show different aspects of the data
    • Rule of Decomposition: Use MCV to deconstruct complex data into manageable / understandable parts
    • Rule of Parsimony: Don’t use MCV when a single view suffices
    • Rule of Space/Time Resource Optimization: The resources saved by MCV should outweigh their costs.
    • Rule of Self-Evidence: Relations between views should be clear.
    • Rule of Consistency: Use consistent interfaces and encodings across MCV
    • Rule of Attention Management: Guide the user focus to the right view at
    the right time.
  12. 12What is DATA CONTEXT and DATA CONTENTThe data context give us the identity of the data we are looking at, while the data content is the part of the data there is measured.
  13. 13What is a task?A task can be seen as a mid-level abstraction between the goal of the user and the action they want to take. IT can be sometinh like a overview or Zoom+Filter. There are three catagories, exploration, insight and meta.
  14. 14What is the difference between s Elementary task and a synoptic tasksElementary tasks are tasks that ask/answer questions
    about individual data items and their relations, while synoptic tasks are tasks that ask/answer questions
    about sets of data items and their relations
  15. 15What is the DESIGN ACTIVITY FRAMEWORK made up of ?Understand: Motivation: to gather and research the needs of the use.
    Outcome: visualization requirements
    Ideate: Motivation: to generate good ideas for supporting the requirements
    Motivation: to generate good ideas for supporting the requirements
    Make:
    Outcome: a testable hi-fi prototype
    Deploy:
    Motivation: to bring a prototype into effective action in a real-world setting
    Outcome: a testable hi-fi prototype
  16. 16What is The 5 Design Sheet Methodology?IT is a method to come up with vis ideas.
    It contains five sheet. THe first sheet is used to come up wiht many samll ideas and the combine then into a few big ones.
    Sheet 2 to 4 is then used to fill out three different ideas and sheet 5 is used to clarify which one we pick with more details. .
  17. 17What are the GUIDING DESIGN PRINCIPLES ?• Functional Design:
    -> First create something useful, then make it more beautiful.
    • Deliberate Design:
    -> Don’t include any visual feature without justification (purpose or reason).
    • Intuitive Design:
    -> The visualization should not be more complex than the underlying data.
    • Ethical Design:
    -> Do not visualize to deceive the reader/viewer
  18. 18What is MUNZNER’S NESTED MODElWhat is MUNZNER’S NESTED MODEl
  19. 19What is the three stages of Data PreprocessingData Profiling, Data Wrangling and Data transformation
  20. 20How can we handle missing data?Using a placeholder value (bad), data imputation , data amputation and data imputation with data imputation.
  21. 21What is common parts of data transformation?DATA REDUCTION by random sampling or some other sampling
    AGGREGATING DATA by clustering using different linkage metrics
  22. 22How can we deal with multivariate data with points?• Triliniar Plots = Scatterplots for 3 variables that add up to a given total
    • Scatterplot Matrices (SPLOMs) = One Scatterplot per variable
    combination in a matrix layou
    • RadViz = each attribute is assigned
    an anchor on the circumference
    of a circular drawing area
  23. 23How can we deal with multivariate data with lines?PARALLEL COORDINATE Plots(PCS)
    RADAR CHART
  24. 24How can we deal with multivariate data with Nesting?Trellis Displays = make a grid where each entry coves some combination of variables.
    Mosaic Plots    How can we deal with multivariate data with Nesting?
  25. 25When mapping time to visulas which are the most effective choices?Position, lenght and angle.
    Lines with. brightness and connection can also be used.
    Color can be fine if other option have been taken
  26. 26What graph is good at comparing multiple timelines?Horizon Graphs:What graph is good at comparing multiple timelines?
  27. 27Why can it be hard to make good GEOVISUALIZATION?We cannot use positions
    and the Modifiable areal unit problem which comes from aggregating point-based data into areas
  28. 28What do we want from a map projection?Preserving angles: mercator projection
    Preserving areas:
    Mollweide projection
    Preserving distances:
    Werner projection
    We will need to chooce
  29. 29How can we add data to a map?Points: Making either a dotmap or a heatmap
    Lines: FLow map, edge-nundled map
    Areas: Choroplth map, isoplet map
    Multivar: Glyphs, Trellis plot    How can we add data to a map?
  30. 30How can we adjust the map to fit the data?Area/ Continuous Cartograms
    Dorling Cartogram
    Demers Cartogram
    The RecMap Algorithm
    Gridded / Mosaic Cartograms
  31. 31WHAT MAKES A GOOD NODE-LINK-DIAG.?• Draw edges straight, polyline,
    orthogonally,…
    • Minimize Edge Crossings
    • Minimize Edge Bends
    • Minimize Aspect Ratio
    • Minimize Drawing Area
    • Maximize Symmetries
    • Maintain Uniform Edge Length    WHAT MAKES A GOOD NODE-LINK-DIAG.?
  32. 32What makes up the GRAPH VISUALIZATION PIPELINE?• Node Positioning (Graph Layout)
    • Graph pre-processing
    • Pre-layout
    • Layout
    • Post-layout
    • Edge Routing
    • Edge Bundling
    • Attributes -> encode
    • Labels
    • Colors
  33. 33WHY USE MATRIX VISUALIZATIONS?1) It allow for a compact representation of dense subgraphs
    2) For egde-centric displays    WHY USE MATRIX VISUALIZATIONS?
  34. 34What is VISUAL ANALYTICS?Visual Analytics denotes the seamlessly
    integrated data analysis using
    automated, computational methods and interactibe graphical methods    What is VISUAL ANALYTICS?
  35. 35What is the design space?What is the design space?

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Data Visualization Flashcards on Data Visualization, created by Simon Lauridsen on 05/01/2022.
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Simon Lauridsen
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