Data Analaytics.04. Data visualization

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Data Analytics process in Learning and Academic Analytics projects. Day 4: Data visualization

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Data Analaytics.04. Data visualization

  1. 1. Data Analytics process in Learning and Academic Analytics projects Day 4: Data visualization Alex Rayón Jerez alex.rayon@deusto.es DeustoTech Learning – Deusto Institute of Technology – University of Deusto Avda. Universidades 24, 48007 Bilbao, Spain www.deusto.es
  2. 2. “Perfection is achieved not when there is nothing more to add, but when there is nothing left to take away” Antoine de Saint-Exupery
  3. 3. Narrative + Design + Statistics
  4. 4. “[...] people almost universally use story narratives to represent, reason about, and make sense of contexts involving multiple interacting agents, using motivations and goals to explain both observed and possible future actions. With regard to learning analytics, I’m seeing this as how it can contribute to the retrospective understanding and sharing of what transpired within the operational contexts” [Zachary2013]
  5. 5. Objectives ● Know the foundations ○ Learn the principles of information visualization ● Learn about existing techniques and systems ○ Effectiveness ○ Develop the knowledge to select appropriate visualization techniques for particular tasks ● Build ○ Build your own visualizations ○ Apply theoretical foundations
  6. 6. Table of contents ● Introduction ● History ● Concept ● Process ● Mistakes in visualization ● Tools ● Designing a Dashboard
  7. 7. Table of contents ● Introduction ● History ● Concept ● Process ● Mistakes in visualization ● Tools ● Designing a Dashboard
  8. 8. Introduction ● Danger of getting lost in data, which may be: ○ Irrelevant to the current task in hand ○ Processed in an inappropriate way ○ Presented in an inappropriate way Source: http://www.planetminecraft.com/server/padlens-maze/
  9. 9. Introduction (II)
  10. 10. Introduction (III) ● Good graphics…. ○ Point relationships, trends or patterns ○ Explore data to infer new things ○ To make something easy to understand ○ To observe a reality from different viewpoints ○ To achieve an idea to be memorized
  11. 11. Introduction (IV) ● It is a way of expressing ○ Like maths, music, drawing or writing ● So, it has some rules to respect Source: http://powerlisting.wikia.com/wiki/Mathematics_Manipulation
  12. 12. Table of contents ● Introduction ● History ● Concept ● Process ● Mistakes in visualization ● Tools ● Designing a Dashboard
  13. 13. History Definition and characteristics 18th Century 19th Century 20th Century Joseph Priestley William Playfair John Snow Charles J. Minard F. Nightingale Jacques Bertin John Tukey Edward Tufte Leland Wilkinson
  14. 14. History 18th Century: Joseph Priestley Source: http://en.wikipedia.org/wiki/A_New_Chart_of_History#mediaviewer/File:A_New_Chart_of_History_color.jpg
  15. 15. History 18th Century: Joseph Priestley (II) ● Lectures on History and General Policy (1788) ○ A Chart of Biography (1765) ○ A New Chart of History (1769) ● Beautiful metaphors of an inaccurate and abstract dimension (time) translated to a concrete one (space) ○ Time thinking consumes cognitive resources
  16. 16. History 18th Century: William Playfair Source: http://en.wikipedia.org/wiki/William_Playfair
  17. 17. History 19th Century: John Snow Source: http://en.wikipedia.org/wiki/1854_Broad_Street_cholera_outbreak
  18. 18. History 19th Century: Charles J. Minard Source: http://en.wikipedia.org/wiki/Charles_Joseph_Minard
  19. 19. History 19th Century: Florence Nightingale Source: http://en.wikipedia.org/wiki/Florence_Nightingale
  20. 20. History 20th Century: Jacques Bertin Source: http://www.amazon.com/Semiology-Graphics-Diagrams-Networks-Maps/dp/1589482611
  21. 21. History 20th Century: John W. Tukey Source: http://books.google.es/books/about/Exploratory_Data_Analysis.html?id=UT9dAAAAIAAJ&redir_esc=y
  22. 22. History 20th Century: Edward R. Tufte Source: http://www.edwardtufte.com/tufte/books_vdqi
  23. 23. History 20th Century: Leland Wilkinson Source: http://www.amazon.com/Grammar-Graphics-Statistics-Computing/dp/0387245448
  24. 24. History 20th Century: Leland Wilkinson Source: http://www.amazon.com/Grammar-Graphics-Statistics-Computing/dp/0387245448
  25. 25. Table of contents ● History ● Concept ● Process ● Mistakes in visualization ● Tools ● Designing a Dashboard
  26. 26. Concepts Introduction ● Data Visualization ● Information visualization ● GeoVisualization ● Visual Analytics ● Information Design ● Infographic
  27. 27. Concepts Introduction (II) ● Cognitive tools: extending human perception and learning ○ Were invented and developed by our ancestors for making sense of the world and acting more effectively within it ■ Stories that helped people to remember things by making knowledge more engaging ■ Metaphors that enabled people to understand one thing by seeing it in terms of another ■ Binary oppositions like good/bad that helped people to organize and categorize knowledge
  28. 28. Concepts Introduction (III) Source: http://ierg.net/about/briefguide.html#cogtools
  29. 29. Concepts Introduction (IV) Source:http://en.wikipedia.org/wiki/Cognitive_ergonomics
  30. 30. Concepts Data visualization The use of computer-supported, interactive, visual representations of abstract elements to amplify cognition [Card1999]
  31. 31. Concepts Information visualization ● Also known as InfoVis ● Focuses on visualizing non-physical, abstract data such as financial data, business information, document collections and abstract conceptions ● However, inadequately supported decision making [AmarStasko2004] ○ Limited affordances ○ Predetermined representations ○ Decline of determinism in decision-making
  32. 32. Concepts Geovisualization ● Geo-spatial data is special since it describes objects or phenomena that are related to a specific location in the real world Source: http://www.boostlabs.com/why-geovisualization-geographic-visualization-works/
  33. 33. Concepts Visual Analytics The science of analytical reasoning facilitated by interactive visual interfaces [ThomasCook2005]
  34. 34. Concepts Visual Analytics (II) [Keim2006]
  35. 35. Concepts Visual Analytics (III) [Keim2006] “Visual analytics is more than just visualization and can rather be seen as an integrated approach combining visualization, human factors and data analysis. [...]integrates methodology from information analytics, geospatial analytics, and scientific analytics. Especially human factors (e.g., interaction, cognition, perception, collaboration, presentation, and dissemination) play a key role in the communication between human and computer, as well as in the decisionmaking process.”
  36. 36. Concepts Visual Analytics (IV) ● [Shneiderman2002] suggests combining computational analysis approaches such as data mining with information visualization ● People use visual analytics tools and techniques to ○ Synthesize information and derive insight from massive, dynamic, ambiguous and often conflicting data ○ Detect the expected and discover the unexpected ○ Provide timely, defensible, and understandable assessments ○ Communicate assessment effectively for action
  37. 37. Concepts Visual Analytics (V) Interactive visualization Computational analysis Analytical reasoning
  38. 38. Concepts Visual Analytics (VI) ● Combine strengths of both human and electronic data processing [Keim2008] ○ Gives a semi-automated analytical process ○ Use strengths from each
  39. 39. Concepts Visual Analytics (VII) [Verbert2014]
  40. 40. Concepts Information design The practice of presenting information in a way that fosters efficient and effective understanding of it
  41. 41. Concepts Information design (II) Source: http://www.nytimes.com/imagepages/2007/03/17/nyregion/nyregionspecial2/20070318_TRAIN_GRAPHIC.html
  42. 42. Concepts Infographics The graphic visual representations of data, information or knowledge intended to present complex information quickly and clearly
  43. 43. Concepts Infographics (II) Source: http://blog.crazyegg.com/2012/02/22/infographics-how-to-strike-the-elusive-balance-between-data-and-visualization/
  44. 44. Concepts Infographics (III) Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef016760ebbbcd970b-550wi
  45. 45. Concepts Comparison Source: http://www.slideshare.net/SookyoungSong/hci-tutorial0212
  46. 46. Table of contents ● Introduction ● History ● Concept ● Process ● Mistakes in visualization ● Tools ● Designing a Dashboard
  47. 47. Process Introduction The purpose of analytical displays of evidence is to assist thinking. Consequently, in constructing displays of evidence, the first question is, “What are the thinking tasks that these displays are supposed to serve?” The central claim of the book is that effective analytic designs entail turning thinking principles into seeing principles. So, if the thinking task is to understand causality, the task calls for a design principle: “Show causality.” If a thinking task is to answer a question and compare it with alternatives, the design principle is: “Show comparisons.” The point is that analytical designs are not to be decided on their convenience to the user or necessarily their readability or what psychologists or decorators think about them; rather, design architectures should be decided on how the architecture assists analytical thinking about evidence. Edward T. Tufte in an interview
  48. 48. Process Data Visualization Reference Model [Chi2000]
  49. 49. Process 1) Data transformation ● Encoding of value ○ Univariate data ○ Bivariate data ○ Multivariate data ● Encoding of relation ○ Lines ○ Maps and diagrams
  50. 50. Process 1) Data transformation (II) ● Encoding of value ○ Univariate data ○ Bivariate data ○ Multivariate data ● Encoding of relation ○ Lines ○ Maps and diagrams
  51. 51. Process 1) Data transformation (III) [Shneiderman1996]
  52. 52. Process 1) Data transformation (IV) Data Visualization [Jarvainen2013] Univariate data
  53. 53. Process 1) Data transformation (V) Data Visualization [Jarvainen2013] Bivariate data
  54. 54. Process 1) Data transformation (VI) Anscombe's quartet Source: http://en.wikipedia.org/wiki/Anscombe's_quartet
  55. 55. Process 1) Data transformation (VII) Data Visualization [Jarvainen2013] Multivariate data
  56. 56. Process 1) Data transformation (VIII) ● Encoding of value ○ Univariate data ○ Bivariate data ○ Multivariate data ● Encoding of relation ○ Lines ○ Maps and diagrams
  57. 57. Process 1) Data transformation (IX) ● Relation ○ A logical or natural association between two or more things ○ Relevance of one to another ○ Connection
  58. 58. Process 1) Data transformation (X) Source: http://www.digitaltrainingacademy.com/socialmedia/2009/06/social_networking_map.php Social network Lines indicate relationship
  59. 59. Process 1) Data transformation (XI)
  60. 60. Process 1) Data transformation (XII) Source: http://www.d3noob.org/2013/02/formatting-data-for-sankey-diagrams-in.html Sankey Diagram
  61. 61. Process 1) Data transformation (XIII) Source: http://en.wikipedia.org/wiki/Harry_Beck
  62. 62. Process 1) Data transformation (XIV) A Tour Through the Visualization Zoo Source: http://homes.cs.washington.edu/~jheer//files/zoo/
  63. 63. Process 1) Data transformation (XV)
  64. 64. Process 2) Visual mapping Ranking of elementary perceptual tasks [ClevelandMcGill1985]
  65. 65. Process 2) Visual mapping (II) ● Two researchers of the AT&T Bell Labs, William S. Cleveland y Robert McGill, published a core article in the Journal of the American Statistical Association ● The title was: “Graphical perception: theory, experimentation, and application to the development of graphical methods” ● It proposes a guide the most suitable visual representation depending on the objective of each graph
  66. 66. Process 2) Visual mapping (III) “A graphical form that involves elementary perceptual tasks that lead to more accurate judgements than another graphical form (with the same quantitative information) will result in a better organization and increase the chances of a correct perception of patterns and behavior.”
  67. 67. Process 2) Visual mapping (IV) Source: http://www.businessinsider.com/pie-charts-are-the-worst-2013-6 “Save the pies for dessert” (Stephen Few)
  68. 68. Process 2) Visual mapping (V) Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef0167631df6f7970b-550wi
  69. 69. Process 2) Visual mapping (VI) Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef016302299aa9970d- 550wi In some representations, the accuracy is not the objective, but the perception of general patterns, concentrations, aggregations, trends, etc. The shapes in the low part of the list could be quite useful
  70. 70. Process 2) Visual mapping (VII)
  71. 71. Process 2) Visual mapping (VIII) Depictive graphics Symbolic graphics Source: http://www.dnr.mo.gov/regions/regions.htm Source: http://trevorcairney.blogspot.com.es/2010_04_01_archive.html Source: http://pubs.usgs.gov/of/2005/1231/sumstat.htm
  72. 72. Process 2) Visual mapping (IX) ● Maria Kozhevnikov, states that not everybody understands statistical graphs easily ○ It depends on some activation patterns within the brain ● In one of her studies, she exposed how artists, architects and scientifics interpret graphs in different ways ○ The same happens with regular readers
  73. 73. Process 2) Visual mapping (X) Ranking of perceptual tasks [ClevelandMcGill1985]
  74. 74. Process 2) Visual mapping (XI) Remembering what Tufte said: “What are the thinking tasks that these displays are supposed to serve?”
  75. 75. Process 2) Visual mapping (XII) Compare numbers? A bar chart (Source: http://en.wikipedia.org/wiki/Bar_chart)
  76. 76. Process 2) Visual mapping (XIII) Compare numbers? Source: http://www.improving-visualisation.org/img_uploads/2009-03-09_Mon/200939171254.jpg ?
  77. 77. Process 2) Visual mapping (XIV) Temporal variance of a magnitude? A line chart (Source: http://en.wikipedia.org/wiki/Line_graph)
  78. 78. Process 2) Visual mapping (XV) Correlation among two variables? A scatter plot (Source: http://en.wikipedia.org/wiki/Scatter_plot)
  79. 79. Process 2) Visual mapping (XVI) Difference between two variables? As Cleveland and McGill states, our brain has problems comparing angles, curves and directions → if we want to show the difference, we must represent directly the difference or
  80. 80. Process 2) Visual mapping (XVII) Source: http://www.excelcharts.com/blog/uncommon-knowledge-about-pie-charts/#prettyPhoto[gallery]/0/
  81. 81. Process 2) Visual mapping (XVIII) The best strategy? Represent the same data in different ways
  82. 82. Process 2) Visual mapping (XIX) Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef0153903da6ba970b-550wi A map Graphics Numeric table
  83. 83. Process 2) Visual mapping (XX) Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef0153903da6ba970b-550wi Different visualization configurations Filters (zoom, search tool, select data by continent and size) Depth search (click in the bubbles and show more data, etc.)
  84. 84. Process 2) Visual mapping (XXI) Source: http://www.stonesc.com/Vis08_Workshop/DVD/Reijner_submission.pdf
  85. 85. Process 2) Visual mapping (XXII) Source: http://apandre.wordpress.com/dataviews/choiceofchart/
  86. 86. Process 2) Visual mapping (XXIII) Source: http://apandre.wordpress.com/dataviews/choiceofchart/
  87. 87. Process 2) Visual mapping (XXIV) Source: http://www.visual-literacy.org/periodic_table/periodic_table.html
  88. 88. Process 3) View Transformations Classification of Visual Data Exploration Techniques [Keim2002]
  89. 89. Process Principles ● Summary of Tufte’s principles ○ Tell the truth ■ Graphical integrity ○ Do it effectively with clarity, precision, etc. ■ Design aesthetics “The success of a visualization is based on deep knowledge and care about the substance, and the quality, relevance and integrity of the content” [Tufte1983]
  90. 90. Process Principles (II) ● Design aesthetics: five principles ○ Above all else show the data ○ Maximize the data-ink ratio, within reason ○ Erase non-data ink, within reason ○ Erase redundant data-ink ○ Revise and edit
  91. 91. Process Principles (III) ● Preattentive attributes ○ Color ○ Size ○ Orientation ○ Placement on page or Source: http://www.storytellingwithdata.com/2011/10/google-example-preattentive-attributes.html
  92. 92. Table of contents ● History ● Concept ● Process ● Mistakes in visualization ● Tools ● Designing a Dashboard
  93. 93. Mistakes in visualization Introduction
  94. 94. Mistakes in visualization Some mistakes Problems?
  95. 95. Mistakes in visualization Some mistakes (II) ● Multidimensionality ● Lack of context and understanding ○ Are the numbers relevant? ○ What do they mean? ○ How do they affect to me? An onion with just one layer
  96. 96. Mistakes in visualization Some mistakes (III) Problems? Try to identify: 1) The biggest donor in 2008 2) The smallest donor in 2009 3) The variation between 2008 and 2009 4) Which region received the biggest amount of moneySource: http://blogs.elpais.com/.a/6a00d8341bfb1653ef0153903125d9970b-550wi
  97. 97. Mistakes in visualization Some mistakes (IV) ● A map is not the best way to represent that data ● If I want to answer previously stated questions I must search for the relevant figures, memorize them and then compare Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef0153903125d9970b-550wi
  98. 98. Mistakes in visualization Some mistakes (V) Problems? The graph tries to reveal the size of UK’s deficit (the black box in the right side) Does the graph helps in the contextualization? Can we analyze data deeper? How can we compare? Know the differences? Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef015390a96894970b-550wi
  99. 99. Mistakes in visualization Some mistakes (VI) Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef015390a98d8a970b-550wi Solution
  100. 100. Mistakes in visualization Some mistakes (VII) Problems? Bar values should start at zero Source: http://www.qualitydigest.com/inside/quality-insider-article/asci-customer-satisfaction-airlines-remains-low.html
  101. 101. Table of contents ● History ● Concept ● Process ● Mistakes in visualization ● Tools ● Designing a Dashboard
  102. 102. Tools Pentaho Reporting
  103. 103. Tools Many Eyes
  104. 104. Tools Tableau Public
  105. 105. Tools Tableau Public (II) ● Free to use ● 1 GB of storage ● Easy to embed in webpage ● Tableau Public Premium ○ Price based on page views
  106. 106. Tools d3.js
  107. 107. Tools Highcharts
  108. 108. Tools R Studio
  109. 109. Tools ggplot2 in R An implementation of the Grammar of Graphics by Leland Wilkinson “In brief, the grammar tells us that a statistical graphic is a mapping from data to aesthetic attributes (color, shape, size) of geometric objects (points, lines, bars). The plot may also contain statistical transformations of the data and is drawn on a specific coordinate system”
  110. 110. Tools ggplot2 in R (II)
  111. 111. Tools Google Charts
  112. 112. Tools Google Charts (II)
  113. 113. Tools Google Fusion Tables
  114. 114. Tools Google Fusion Tables (II)
  115. 115. Tools Simile Widgets
  116. 116. Tools Processing.js
  117. 117. Tools NodeXL
  118. 118. Tools Spotfire
  119. 119. Tools Advizor Analyst
  120. 120. Tools Datawatch
  121. 121. Tools QlikView
  122. 122. Tools Prefuse
  123. 123. Tools Protovis
  124. 124. Table of contents ● History ● Concept ● Process ● Mistakes in visualization ● Tools ● Designing a Dashboard
  125. 125. Dashboard Introduction Fundamentals Perception Vision Color Principles Techniques Representation Presentation Interaction Applications Dashboards Visual Analytics
  126. 126. Dashboard Introduction (II) “Most information dashboards that are used in business today fall far short of their potential” Stephen Few
  127. 127. Dashboard Definition “A dashboard is a visual display of the most important information needed to achieve one or more objectives; consolidated and arranged on a single screen so the information can be monitored at a glance” [Few2007]
  128. 128. Dashboard Characteristics ● Visual displays ● Display information needed to achieve specific objectives ● Fits on a single computer screen ● Are used to monitor information at a glance ● Have small, concise, clear, intuitive display mechanisms ● Are customized
  129. 129. Dashboard Categories Role Strategic, Operational, Analytical Type of data Quantitative, Non-quantitative Data domain Sales, Finance, Marketing, Manufacturing, Human Resources, Learning, etc. Type of measures Balanced Scored Cards, Six Sigma, Non-performance Span of data Enterprise wide, Departmental, Individual Update frequency Monthly, Weekly, Daily, Hourly, Real-time Interactivity Static display, Interactive display Mechanisms of display Primarily graphical, Primarily text, Integration of graphics and text Portal functionality Conduit to additional data. No portal functionality
  130. 130. Dashboard Common mistakes 1) Exceeding the boundaries of a single screen ● Information that appears on dashboards is often fragmented in one of two ways: ○ Separated into discrete screens to which one must navigate ○ Separated into different instances of a single screen that are accesses through same form of interaction
  131. 131. Dashboard Common mistakes (II) 2) Supplying inadequate context for the data ● Fail to provide adequate context to make the measures meaningful 3) Displaying excessive detail or precision ● Show unnecessary detail 4) Choosing a deficient measure ● Use of measures that fail to directly express the intended message
  132. 132. Dashboard Common mistakes (III) 5) Choosing inappropiate display media ● Common problem with pie charts ;-) 6) Introducing meaningless variety ● Exhibit unnecessary variety of display media
  133. 133. Dashboard Common mistakes (IV) 7) Using poorly designed display media ● A legend was used to label and assign values to the slices of the pie. This forces our eyes to bounce back and forth between the graph and the legend to glean meaning, which is a waste of time and effort when the slices could have been labeled directly. ● The order of the slices and the corresponding labels appears random. Ordering them by size would have provided useful information that could have been assimilated instantly. ● The bright colors of the pie slices produce sensory overkill. Bright colors ought to be reserved for specific data that should stand out from the rest.
  134. 134. Dashboard Common mistakes (V) 8) Encoding quantitative data inaccurately 9) Arranging the data poorly ● The most important data ought to be prominent ● Data that require immediate attention ought to stand out ● Data that should be compared ought to be arranged and visually designed to encourage comparisons
  135. 135. Dashboard Common mistakes (VI) 10) Highlighting important data ineffectively or not at all ● Fail to differentiate data by its importance ○ Giving relatively equal prominence to everything on the screen 11) Cluttering the display with useless decoration ● Try to look something that is not ● It results in useless and distracting decoration
  136. 136. Dashboard Common mistakes (VII) 12) Misusing or overusing color ● Too much color undermines its power 13) Designing an unattractive visual display ● The fundamental challenge of dashboard design is to effectively display a great deal of often disparate data in a small amount of space
  137. 137. Dashboard Buzz words ● Dashboards ○ Presents information in a way that is easy to read and interpret ● Key Performance Indicator ○ Success or steps leading to the success of a goal
  138. 138. Dashboard Exploratory Analytics Requirements ● The tool ideally exhibits the following characteristics: ○ Provides every analytical display, interaction, and function that might be needed by those who use it for their analytical tasks ○ Grounds the entire analytical experience in a single, central workspace, with all displays, interactions, and functions within easy reach from there
  139. 139. Dashboard Exploratory Analytics Requirements (II) ● The tool ideally exhibits the following characteristics: ○ Supports efficient, seamless transitions from one step to the next of the analytical process, even though the sequence and nature of those steps cannot be anticipated ○ Doesn’t require a lot of fiddling with things to whip them into shape to support your analytical needs (such as having to take time to carefully position and size graphs on the screen)
  140. 140. Dashboard Exploratory Analytics Requirements (III) Source: http://www.perceptualedge.com/articles/visual_business_intelligence/differences_in_analytical_tools.pdf
  141. 141. Dashboard Presentation ● Present: to offer to view; display ● Space limitations ○ Scrolling ○ Overview + detail ○ Distortion ○ Supression ○ Zoom and pan ● Time limitations ○ Rapid serial visual presentation ○ Eye-gaze
  142. 142. Dashboard Interactive data visualizations Graphic design Static visualization Data analysis
  143. 143. Dashboard Interactive data visualizations (II) Graphic design Data analysis Interactive design Exploratory Data analysis Interactive visualization User interface design Static visualization
  144. 144. Dashboard Interactive data visualizations (III) ● When is static representation not enough? ○ Scale ■ Too many data points ■ Too many different dimensions ○ Storytelling ○ Exploration ○ Learning
  145. 145. Dashboard Interactive data visualizations (IV) ● Select ● Explore ● Reconfigure ● Encode ● Abstract/Elaborate ● Filter ● Connect
  146. 146. Dashboard Interactive data visualizations (V) ● Select ● Explore ● Reconfigure ● Encode ● Abstract/Elaborate ● Filter ● Connect
  147. 147. Dashboard Interactive data visualizations (VI) Pick a detail from a larger dataset to keep track of it Source: http://en.wikipedia.org/wiki/Closest_pair_of_points_problem
  148. 148. Dashboard Interactive data visualizations (VII) ● Select ● Explore ● Reconfigure ● Encode ● Abstract/Elaborate ● Filter ● Connect
  149. 149. Dashboard Interactive data visualizations (VIII) ● Overcome limitations of display size ● Most common technique: panning
  150. 150. Dashboard Interactive data visualizations (IX) ● Select ● Explore ● Reconfigure ● Encode ● Abstract/Elaborate ● Filter ● Connect
  151. 151. Dashboard Interactive data visualizations (X) ● Show a different arrangement
  152. 152. Dashboard Interactive data visualizations (XI) ● Select ● Explore ● Reconfigure ● Encode ● Abstract/Elaborate ● Filter ● Connect
  153. 153. Dashboard Interactive data visualizations (XII) ● Change visual variables: colors, sizes, orientation, font, shape
  154. 154. Dashboard Interactive data visualizations (XIII) ● Select ● Explore ● Reconfigure ● Encode ● Abstract/Elaborate ● Filter ● Connect
  155. 155. Dashboard Interactive data visualizations (XIV) Show more or less detail: focus + context
  156. 156. Dashboard Interactive data visualizations (XV) ● Select ● Explore ● Reconfigure ● Encode ● Abstract/Elaborate ● Filter ● Connect
  157. 157. Dashboard Interactive data visualizations (XVI) Filter: Show something conditionally
  158. 158. Dashboard Interactive data visualizations (XVII) ● Select ● Explore ● Reconfigure ● Encode ● Abstract/Elaborate ● Filter ● Connect
  159. 159. Dashboard Interactive data visualizations (XVIII) Show related items: brushing and linking
  160. 160. Dashboard Interaction framework ● Continuous interaction ● Stopped interaction ● Passive interaction ● Composite interaction
  161. 161. Dashboard Interaction framework (II) Continuous interaction
  162. 162. Dashboard Interaction framework (III) Stopped interaction
  163. 163. Dashboard Interaction framework (IV) Passive interaction Two important aspects of passive interaction: 1)  During typical use of a visualization tool, most of the user’s time is spent on passive interaction – often involving eye movement 2)  Passive interaction does not imply a static representation
  164. 164. Dashboard Interaction framework (V) Passive interaction
  165. 165. Dashboard Interaction framework (VI) Composite interaction Source: http://vis.berkeley.edu/papers/generalized_selection/
  166. 166. Dashboard Steps Source: http://www.tableausoftware.com/es- es/trial/tableau-software 1. Choose metrics that matter 2. Keep it visual 3. Make it interactive 4. Keep it current or don’t bother 5. Make it simple to access and use
  167. 167. References [AmarStasko2005] Amar, R. A., & Stasko, J. T. (2005). Knowledge precepts for design and evaluation of information visualizations. Visualization and Computer Graphics, IEEE Transactions on, 11(4), 432-442. [Cairo] Alberto Cairo [Online]. URL: https://twitter.com/albertocairo [Chi2000] Chi, Ed H. "A taxonomy of visualization techniques using the data state reference model." Information Visualization, 2000. InfoVis 2000. IEEE Symposium on. IEEE, 2000. [ClevelandMcGill1985] Cleveland, William S., and Robert McGill. "Graphical perception and graphical methods for analyzing scientific data." Science 229.4716 (1985): 828-833. [Few2004] Few, Stephen. "Show me the numbers." Analytics Pres (2004). [Few2007] Few, Stephen. "Dashboard confusion revisited." Perceptual Edge (2007). [Fry] Ben Fry [Online]. URL: http://benfry.com/ [Jarvinen2013] Data visualization [Online]. URL: http://lib.tkk.fi/Lic/2013/urn100763.pdf [Keim2006] Keim, D.A.; Mansmann, F. and Schneidewind, J. and Ziegler, H., Challenges in Visual Data Analysis, Proceedings of Information Visualization (IV 2006), IEEE, p. 9-16, 2006. [Kosslyn] Kosslyn Laboratory [Online]. URL: http://isites.harvard.edu/icb/icb.do?keyword=kosslynlab&pageid=icb.page250946 [Malamed] Visual Language for Designers: Principles for Creating Graphics that People Understand [Online]. URL: http://www.amazon.com/Visual- Language-Designers-Principles-Understand/dp/1592535151 [Shneiderman1996] Shneiderman, Ben. "The eyes have it: A task by data type taxonomy for information visualizations." Visual Languages, 1996. Proceedings., IEEE Symposium on. IEEE, 1996. [Shneiderman2002] Shneiderman, B. (2002) Inventing discovery tools: combining information visualization with data mining1. Information visualization, 1(1), 5-12. [ThomasCook2005] J.J. Thomas and K.A. Cook, "A Visual Analytics Agenda," IEEE Computer Graphics & Applications, vol. 26, pp. 10-13, 2006. [Verbert2014a] Visual Analytics [Online]. URL: http://www.slideshare.net/kverbert/in-34471961 [Yau] Nathan Yau [Online]. URL: http://flowingdata.com/about-nathan/ [Zachary2013] Zachary, W., Rosoff, A., Miller, L. C., & Read, S. J. (2013). Context as a Cognitive Process: An Integrative Framework for Supporting Decision Making. Paper presented at the STIDS.
  168. 168. Courses KU Leuven [Online]. URL: http://ariadne.cs.kuleuven.be/wiki/index.php/MM-Course1314 Berkeley [Online]. URL: http://blogs.ischool.berkeley.edu/i247s13/ Columbia university [Online]. URL: http://columbiadataviz.wordpress.com/student-work/ Information Visualization MOOC [Online]. URL: http://ivmooc.cns.iu.edu/
  169. 169. Additional resources http://infosthetics.com/ http://visualizing.org http://www.visualcomplexity.com/vc/ http://visual.ly/ http://flowingdata.com http://www.infovis-wiki.net
  170. 170. Data Analytics process in Learning and Academic Analytics projects Day 4: Data visualization Alex Rayón Jerez alex.rayon@deusto.es DeustoTech Learning – Deusto Institute of Technology – University of Deusto Avda. Universidades 24, 48007 Bilbao, Spain www.deusto.es

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