Visualisation - techniques, interaction dynamics, big data
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The document discusses the significance of data visualization to enhance human cognition through interactive visual representations. It covers various visualization techniques, tools, and methods for effectively representing data structures, statistics, and hierarchical relationships. The emphasis is on the importance of interactivity in understanding complex datasets for insightful analysis.
![Information Visualisation is the use
of interactive visual
representations to amplify
cognition [Card. et. al]
Definition](https://image.slidesharecdn.com/cursusivpvgentdeel2-150605074951-lva1-app6892/85/Visualisation-techniques-interaction-dynamics-big-data-14-320.jpg)
Visualisation - techniques, interaction dynamics, big data
- 1. Post-‐academic course Big Data Post-‐academic course Big Data Joris Klerkx Research Expert, PhD. joris.klerkx@cs.kuleuven.be @jkofmsk Erik Duval Professor erik.duval@cs.kuleuven.be @erikduval Visualisatie - deel 2 Big Data - module 3 IVPV - Instituut voor PermanenteVorming 28-05-2015
- 2. To research, design, create and evaluate useful tools that augment the human intellect By ‘augmen+ng human intellect’ we mean increasing the capability of a man to approach a complex problem situa+on, to gain comprehension to suit his particular needs, and to derive solu+ons to problems (Douglas Engelbart, 1962). Augment group - HCI research lab Dept. Computerwetenschappen KU Leuven https://augmenthuman.wordpress.com Music Technology Enhanced Learning e-health Research 2.0 Health Media (Consumption) Technology Enhanced Learning Science 2.0
- 5. Recap
- 6. Humans have advanced perceptual abilities Humans have little short term memory Externalize data by using interactive, visual encodings Our brains makes us extremely good at recognizing visual patterns Our brains remember relatively little of what we perceive
- 7. Before visualisation… Data - structure time, hierarchy, network, 1D, 2D, nD, … - questions where, when, how often, … - audience domain & visualisation expertise, …
- 8. Audience vs purpose Explore Explain Lay person Domain experts
- 9. Visual mapping Encode data characteristics into visual form Each mark (point, line, area,…) represents a data element Think about relationships between elements (position) “Simplicity is the ultimate sophistication.” Leonardo daVinci
- 10. J. Mackinlay. Automating the design of graphical presentations of relational information. ACM Transactions On Graphics, 5(2):110–141, 1986.
- 11. Many ways to visualize simple dataset
- 12. Some ways are more effective than others, depending on your question at hand
- 14. Information Visualisation is the use of interactive visual representations to amplify cognition [Card. et. al] Definition
- 15. Information visualization significantly improves insight generations and user productivity Accelerates time to insight
- 16. Today • Tour through the visualisation zoo • Interactive Dynamics • Is there too much data to visualize? • Tools?
- 17. A tour through the visualization zoo Heer, J., Bostock, M., & Ogievetsjy, V. (2010, May). A Tour through the VisualizaMon Zoo -‐ A survey of powerful visualisaMon techniques, from the obvious to the obscure. ACM Graphics , 8 (5), hTps://queue.acm.org/detail.cfm?id=1805128
- 18. Time-series data Sets of values changing over time
- 19. Relative changes in time-series data An index chart is an interactive line chart that shows percentage changes for a collection of time-series data based on a selected index point. http://homes.cs.washington.edu/~jheer//files/zoo/ex/time/index-chart.html
- 20. Aggregated time-series data A stream graph visually summates time-series values http://hci.stanford.edu/jheer/files/zoo/ex/time/stack.html
- 21. Time-series data
- 22. Time-series data Small multiples: showing each series in its own chart. http://homes.cs.washington.edu/~jheer//files/zoo/ex/time/multiples.html
- 23. https://augmenthuman.wordpress.com/portfolio/visualizing-gaming-trends-on-steam/
- 26. The horizon graph is a technique for increasing the data density of a time-series view while preserving resolution. Sizing the Horizon: The Effects of Chart Size and Layering on the Graphical Perception of Time Series Visualizations Jeffrey Heer, Nicholas Kong, Maneesh Agrawala ACM Human Factors in Computing Systems (CHI), 2009. pp. 1303 - 1312. Best Paper Award PDF (442K)
- 28. http://www.perceptualedge.com/blog/?p=390 A year’s worth of stock prices for 50 separate stocks
- 30. Statistical Distributions Reveal how a set of numbers is distributed and thus help an analyst better understand the statistical properties of the data
- 31. Histograms shows the prevalence of values grouped into bins Histogram sliders
- 32. A stem-and-leaf plot bins numbers according to the first significant digit, and then stacks the values within each bin by the second significant digit. http://homes.cs.washington.edu/~jheer//files/zoo/ex/stats/stem-and-leaf.html
- 34. Box-and-whisker plot, which can convey statistical features such as the mean, median, quartile boundaries, or extreme outliers. http://admin-apps.webofknowledge.com/JCR/help/h_boxplot.html
- 35. Statistical distribution of data The Q-Q plot compares two probability distributions by graphing their quantiles against each other. http://hci.stanford.edu/jheer/files/zoo/ex/stats/qqplot.html
- 36. Representing relationships/ correlations among multiple variables. A scatter plot matrix (SPLOM) uses small multiples of scatter plots showing a set of pairwise relations among variables http://homes.cs.washington.edu/~jheer//files/zoo/ex/stats/splom.html graphing every pair of variables in two dimensions
- 38. Maps Mostly based upon a cartographic projection: a mathematical function that maps the three-dimensional geometry of the Earth to a two-dimensional image Other maps knowingly distort or abstract geographic features to tell a richer story or highlight specific data. http://geoawesomeness.com/topics/web-maps/ http://unfoldingmaps.org http://ffffound.com/home/tillnm/found/
- 39. • Google Maps - Well rounded, established mapping solution, especially for non-developers to get a basic map on the web, along with all the powers that Google is (in)famous for. • OpenLayers - For situations when other mapping frameworks can’t solve your spatial analysis problems. • Leaflet - Currently, easily the best mapping framework for general mapping purposes, especially if you don’t need the additional services that MapBox or CartoDB provide. • MapBox - Fast growing and market changing mapping solution for when you want more control over map styling or have a need for services that others are not providing, such as detailed satellite images, geocoding or directions. • Unfolding - to create interactive maps and geovisualizations in Processing and Java http://www.toptal.com/web/the-roadmap-to-roadmaps-a-survey-of-the-best-online-mapping-tools Typical Mapping Tools
- 41. A flow map depicts the movement of a quantity in space and (implicitly) in time.
- 42. Charles Joseph Minard, 1781-1870 Napoleon’s Russian Campaign
- 43. https://lirias.kuleuven.be/bitstream/123456789/461689/1/paper234_camera-ready.pdf
- 44. http://homes.cs.washington.edu/~jheer//files/zoo/ex/maps/choropleth.html Choropleth Maps use color encoding to show aggregated data by geographical areas
- 45. Choropleth maps using 2 categories http://www.colorado.edu/geography/gcraft/notes/cartocom/section6.html
- 46. Choropleth maps using 5 categories http://www.colorado.edu/geography/gcraft/notes/cartocom/section6.html
- 47. Choropleth maps using 9 categories http://www.colorado.edu/geography/gcraft/notes/cartocom/section6.html
- 48. Choropleth maps using different ranging methods Equal Range http://www.colorado.edu/geography/gcraft/notes/cartocom/section6.html
- 50. Quantile Range http://www.colorado.edu/geography/gcraft/notes/cartocom/section6.html
- 52. Graduated Symbol Maps places symbols/glyphs over an underlying map http://homes.cs.washington.edu/~jheer//files/zoo/ex/maps/symbol.html Graduated Symbol Maps places symbols over an underlying map
- 53. A cartogram distorts the shape of geographic regions so that the area directly encodes a data variable http://homes.cs.washington.edu/~jheer//files/zoo/ex/maps/cartogram.html
- 57. There is no perfect map How could you actually compare sizes of different continents and countries? https://www.youtube.com/watch?v=KUF_Ckv8HbE
- 58. Hierachies Most data can be organised into natural hierarchies Special visualization techniques exist to leverage hierarchical structure, allowing rapid multiscale inferences: micro-observations of individual elements and macro-observations of large groups
- 59. A node-link diagram with Reingold-Tilford algorithm http://hci.stanford.edu/jheer/files/zoo/ex/hierarchies/tree.html
- 60. The dendrogram (or cluster) algorithm places leaf nodes of the tree at the same level Polar coordinates instead of cartesian coordinates http://homes.cs.washington.edu/~jheer//files/zoo/ex/hierarchies/cluster-radial.html
- 62. The adjacency diagram is a space-filling variant of the node-link diagram; rather than drawing a link between parent and child in the hierarchy, nodes are drawn as solid areas (either arcs or bars), and their placement relative to adjacent nodes reveals their position in the hierarchy http://homes.cs.washington.edu/~jheer//files/zoo/ex/hierarchies/icicle.html
- 63. The sunburst layout, shown in figure 4E, is equivalent to the icicle layout, but in polar coordinates. http://homes.cs.washington.edu/~jheer//files/zoo/ex/hierarchies/sunburst.html
- 66. Enclosure diagrams use containment rather than adjacency to represent the hierarchy Squarified Treemaps - space filling http://homes.cs.washington.edu/~jheer//files/zoo/ex/hierarchies/treemap.html
- 68. Circle-packing layout Non - space filling http://hci.stanford.edu/jheer/files/zoo/ex/hierarchies/pack.html
- 69. Networks Non-hierarchical relationships between data points
- 70. https://queue.acm.org/detail.cfm?id=1805128 A force-directed layout where nodes are charged particles that repel each other, and links are dampened springs that pull related nodes together. http://hci.stanford.edu/jheer/files/zoo/ex/networks/force.html
- 71. http://homes.cs.washington.edu/~jheer//files/zoo/ex/networks/arc.html An arc diagram uses a one-dimensional layout of nodes, with circular arcs to represent links.
- 72. http://www.youtube.com/watch?v=wQpTM7ASc-w T. Nagel, M. Maitan, E. Duval,A.Vande Moere, J. Klerkx, K. Kloeckl, and C. Ratti.Touching transport - a case study on visualizing metropolitan public transit on interactive tabletops. In AVI2014: 12th ACM International Working Conference on AdvancedVisual Interfaces, pages 281–288, 2014.
- 73. http://homes.cs.washington.edu/~jheer//files/zoo/ex/networks/matrix.html Adjacency matrix: each value in row i and column j in the matrix corresponds to the link from node i to node j.
- 74. Chord diagrams show directed relationships among a group of entities. Relationship can be quantitative or binary http://bl.ocks.org/mbostock/4062006 Ye L, Amberg J, Chapman D et al. 2013 Fish gut microbiota analysis differentiates physiology and behavior of invasive Asian carp and indigenous American fish The ISME journal
- 76. Choices of representation (e.g., matrix- diagram) and interactive parameterization (e.g., default sort order) can be critical to unearthing data quality issues that can otherwise undermine accurate analysis.
- 80. Many(!!) more techniques are out there e.g. check the visual index at https://github.com/mbostock/d3/wiki/Gallery
- 82. When“static”visualizations are not enough Too many datapoints or variables True understanding and insight generation require interaction Data is exploratory in nature
- 83. Interaction Should contribute to successful analytic dialogues! Support exploration at the rate of human thought!
- 84. Interactive Dynamics enables analysts to explore large data sets (Task types) Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416 • Data & View Specification controls • View Manipulation • Process & Provenance
- 86. Custom programming of a specific visualization component Specific tools that use a chart typology (eg excel) Data-flow graphs deconstruct the visualization process into fine-grained set of operators (data import, transformation, layout, coloring, etc Visualization construction using formal grammars (eg ggplot2, R, Prototvis, etc) Data & View Specification controls serve to visually & interactively encode data
- 88. google spreadsheets Tools that use a chart typology (eg excel)
- 89. Excel for mac 2015 Tools that use a chart typology (eg excel)
- 91. Cfr. http://gravyanecdote.com/visual-analytics/which-chart-should-you-use-to-show- this-data/ Data-flow graphs deconstruct the visualization process into fine-grained set of operators (data import, transformation, layout, coloring, etc
- 93. Let me know how this works for you ;)
- 95. • Categorical/ordinal data • radio buttons, checkboxes, scrollable lists, hierachies, search boxes (with autocomplete) • Ordinal, quantitative, and temporal data • a standard slider (for a single threshold value) or a range slider (for specifying multiple endpoints). Filtering allows rapid and reversible exploration of data subsets
- 96. Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
- 99. http://www.trulia.com/vis/tru247/ Query controls can be further augmented with visualizations of their own
- 100. Query controls can be further augmented with visualizations of their own
- 101. Sorting enables popping up of trends, clusters,… • Choices in a toolbar • Clicks on the header in a table • Can be complicated in the case of multiple view displays
- 104. Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
- 105. Select items to hightlight, filter or manipulate them • Mouse clicks, free-form lassos, area cursors (‘brushes’), mouse hovering, etc • depends on the device • Various expressive power • selections of a collection of items • selections as queries over the data (eg drawing rectangle -> range query)
- 108. Select by slope and tolerance Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
- 109. Mapping mouse gestures to query patterns Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
- 110. Navigate to examine high- mede patterns & low-level detail • Overview first, zoom & filter, then details-on-demand • Start with what you know, then grow • Search, show context, expand on demand. • Focus + Context • Semantic Zooming • Magical lenses
- 111. $ f con- cation n this of the zoom what isible, e user on the more rectly 3, for ber of dmark Figure 4: Setting of the evaluation. B. Vandeputte, E. Duval, and J. Klerkx. Interactive sensemaking in authorship networks. Proceedings of the ACM International Conference on Interactive Tabletops and Surfaces, ITS11, pp. 246–247, 2011. Overview first, zoom and filter, details on demand
- 112. B. Vandeputte, E. Duval, and J. Klerkx. Applying design principles in authorship networks-a case study. In CHI EA’12: Proceedings of the 2012 ACM annual conference extended abstracts on Human Factors in Computing Systems, pages 741– 744, 2012. (https://www.youtube.com/watch?v=R5CeTEejdBA) Start with what you know, then grow Search, show context, expand on demand
- 113. http://sanfrancisco.crimespotting.org Overview first, zoom and filter, details on demand Viewport on the data: zooming & panning controls
- 115. Focus + Context Semantic Zooming Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
- 116. Magical Lenses C. Tominski, S. Gladisch, U. Kister, R. Dachselt, and H. Schumann. A Survey on Interactive Lenses in Visualization. EuroVis State-of-the-Art Reports, Swansea, UK, Eurographics Association, 2014.
- 117. Fisheye
- 118. C. Tominski, S. Gladisch, U. Kister, R. Dachselt, and H. Schumann. A Survey on Interactive Lenses in Visualization. EuroVis State-of-the-Art Reports, Swansea, UK, Eurographics Association, 2014.
- 119. Coordinate views for linked, multi- dimensional exploration Enables seeing data from different perspectives Multiple views can facilitate comparison
- 122. Small multiples https://queue.acm.org/detail.cfm?id=2146416 Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
- 123. Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
- 125. Organize multiple windows & workspaces • Tiled approaches (different widgets) allows to see all information and selectors at once, minimizing distracting scrolling or window operations, while enabling analysts to concentrate on extracting and reporting insights. • Layout organization tools will become decisive factors in creating effective user experience
- 126. Orchestrate attention and mentally integrate patterns among views Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
- 127. Orchestrating visualizations over various devices
- 128. In-class display
- 129. Process & Provenance
- 130. Record analysis histories for revisitation, review and sharing
- 131. Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
- 132. Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
- 133. Annotate patterns to document findings Record, organize, and communicate insights gained during visual exploration
- 134. Freeform graphical annotations without explicit tie to the underlying data Data-aware annotations Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
- 136. Share views and annotations to enable collaboration Real-world analysis is very much a social process that may involve multiple interpretations, discussion, and dissemination of results.
- 138. Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
- 140. Sharing datasets!
- 141. Guide users through analysis tasks or stories • Incorporate guided analytics to lead analysts through workflows for common tasks. • Narrative visualization
- 142. Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
- 144. Interval 1 Interval 2 Interval 3 Interval 4
- 145. Interactive Dynamics: Summary Heer, J., & Shneiderman, B. (2012, February). InteracMve Dynamics for Visual Analysis. Magazine Queue -‐ Microprocessors , 10 (2), p. 30. hTp://queue.acm.org/detail.cfm?id=2146416
- 146. Further notes
- 147. How much “big” data can we visualise? Petabytes? Exabytes? Yottabytes?! Direct visualisation of big data ‘in the raw’ is probably not so effective
- 148. Is there too much data to visualize? You only have so many pixels on a screen Each pixel, one data point E.g Typical hdtv screen contains 1920 * 1080 = 2.073.600 pixels prysm.com
- 150. What is big data?
- 151. Multiple data sources with varied data types “Diverse” data I talk geoJSON i talk custom xml i talk apache logs
- 152. millions of records “Tall” data
- 153. Cluttered displays Heer, J. & Kandel, S. (2012), Interactive Analysis of Big Data, XRDS, 19 (1)
- 154. Cluttered displays Binned density scatterplot Hexagonal instead of rectangular Heer, J. & Kandel, S. (2012), Interactive Analysis of Big Data, XRDS, 19 (1)
- 155. Perceptual scalability of a display should be limited by the chosen resolution of the data, not the numbers of records (Heer & Kandel, 2012) Heer, J. & Kandel, S. (2012), Interactive Analysis of Big Data, XRDS, 19 (1)
- 156. Multi-variate data with 100s to 1000s of variables “Wide” data
- 157. Visualizations might help reveal multidimensional patterns Use the power of the machine to find a proxy in the data that predicts the selected variables Depending on their specific questions, domain experts might select a subset of variables they are interested in
- 158. http://www.perceptualedge.com/blog/?p=2046 In this day of so-called Big Data, organizations are scrambling to implement new software and hardware to increase the amount of data that they collect and store. In so doing they are unwittingly making it harder to find the needles of useful information in the rapidly growing mounds of hay. If you don’t know how to differentiate signals from noise, adding more noise only makes matters worse. Monday, June 1st, 2015
- 159. When we rely on data for decision making, how do we tell what qualifies as a signal and what is merely noise? In and of itself, data is neither. It is merely a collection of facts. When a fact is true, useful, and deserves a response, only then is it a signal. When it isn’t, it’s noise. It’s that simple (Few, http://www.perceptualedge.com/blog/?p=2046, 2015)
- 160. Avoid the All-You-Can-Eat buffet! (Ben Fry)
- 161. Visual-information Seeking Mantra Visual Analytics Mantra Overview First, Zoom and Filter, Details-on-Demand Analyze First, Show the Important, Zoom and Analyse Further, Details-on-Demand Ben Shneiderman Daniel Keim
- 162. Interactive analysis tools can help quell “big data” by augmenting our ability to manipulate and reason about it (Heer & Kandel, 2012) Heer, J. & Kandel, S. (2012), Interactive Analysis of Big Data, XRDS, 19 (1)
- 163. In the face of a data deluge, what remains relatively constant is our own cognitive ability to make sense of the data and reach reliable, informed decisions. Big data is of little help when decoupled from sound judgment. J. Heer Heer, J. & Kandel, S. (2012), Interactive Analysis of Big Data, XRDS, 19 (1)
- 164. Large data is a wild beast and you’d better treat it with the right tools. Visualization is a great tool to convey what automatic data analysis algorithms discover. And often it is a very challenging task! What the algorithms spit is exciting new complex data that requires creativity and knowledge as well. E. Bertini http://fellinlovewithdata.com/guides/how-do-you-visualize-too-much-data
- 165. ▪ Interactive visualization of a million items J.D. Fekete and C. Plaisant. ▪ Random Sampling as a Clutter Reduction Technique to Facilitate Interactive Visualisation of Large Datasets G. Ellis (part of it in collab. with yours truly). ▪ A Sampling Approach to Deal with Cluttered Information Visualizations E. Bertini (my phd thesis). ▪ TreeJuxtaposer: Scalable Tree Comparison using Focus+Context with Guaranteed Visibility T. Munzner, F. Guimbretiere, S. Tasiran, L. Zhang, and Y. Zhou. ▪ Beyond visual acuity: the perceptual scalability of information visualizations for large displays B. Yost, Y. Haciahmetoglu, and C. North. ▪ Extreme visualization: squeezing a billion records into a million pixels B. Shneiderman. ▪ Measuring Data Abstraction Quality in Multiresolution Visualization Q. Cui, M. O. Ward, E. A. Rundensteiner, and J. Yang. • imMens: Real-time Visual Querying of Big Data Zhicheng Liu, Biye Jiang, Jeffrey Heer Some papers about big data visualisation http://fellinlovewithdata.com/guides/how-do-you-visualize-too-much-data
- 169. Don’t start from the tool Start from your data and the questions about your data
- 171. The need for multidisciplinary teams http://holisticsofa.com/ The Data Visualization Pyramid
- 172. POINTERS • http://wearecolorblind.com/articles/quick-tips/ • http://infosthetics.com • http://www.visualcomplexity.com/vc/ • http://bestario.org/research/remap • ... (a lot more online! )
- 173. BOOKS • “Readings in InformationVisualization: UsingVision toThink”, Card, S et al. • “Signals”,“Now i see”,“Show Me the Numbers”, Few, S. • “Beautiful Evidence”,Tufte, E. • “InformationVisualization. Perception for design”,Ware, C. • BeautifulVisualization: Looking at Data through the Eyes of Experts (Theory in Practice): Julie Steele, Noah Iliinsky
- 174. ? Joris Klerkx Research Expert, PhD. joris.klerkx@cs.kuleuven.be @jkofmsk https://augmenthuman.wordpress.com