Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Examples for leverage points


Published on

Published in: Technology
  • Login to see the comments

  • Be the first to like this

Examples for leverage points

  1. 1. Grinstein lecture/book visualizations to liven/explain theory paper
  2. 2. 3.1. Preattentive Processing Connection 2 Source unknown
  3. 3. 3.2. Theories of Preattentive Processing Feature Integration Theory (a) a boundary defined by a unique feature hue is preattentively classified as horizontal; 3 (b) a boundary defined by a conjunction of features cannot be preattentively classified as vertical
  4. 4. 4 Roland Rensink. “The Need for Attention to See Change.”∼rensink/flicker, March 2, 2003.
  5. 5. 5 Roland Rensink. “The Need for Attention to See Change.”∼rensink/flicker, March 2, 2003.
  6. 6. 4. Perception in Visualization Examples of perceptually motivated multidimensional visualizations: (a) visualization of intelligent agents competing in simulated e-commerce auctions; (b) visualization of a CT scan of an abdominal aortic aneurism; (c) a painter-like visualization of weather conditions over the Rocky Mountains 6
  7. 7. 3.3. Feature Hierarchy Example: Line Width 7 Source unknown
  8. 8. Bar Chart Bar length better than area size (actually only area height was used!) Solution from Stephen Few‘s Perceptual Edge 8
  9. 9. Improved Bar Chart Solution from Stephen Few‘s Perceptual Edge 9
  10. 10. 3.1. Position Example visualizations: (left) using position to convey information. Displayed here is the minimum price versus the maximum price for cars with a 1993 model year. The spread of points appears to indicate a linear relationship between minimum and maximum price; (right) another visualization using a different set of variables. This figure compares minimum price with engine size for the 1993 cars data set. Unlike (left), there does not appear to be a strong relationship between these two variables. 10
  11. 11. 3.2. Mark This visualization uses shapes to distinguish between different car types in a plot comparing highway MPG and horsepower. Clusters are clearly visible, as well as some outliers. 11
  12. 12. 3.3. Size (Length, Area and Volume) This is a visualization of the 1993 car models data set, showing engine size versus fuel tank capacity. Size is mapped to maximum price charged. 12
  13. 13. 3.4. Brightness Another visualization of the 1993 car models data set, this time illustrating the use of brightness to convey car width (the darker the points, the wider the vehicle). 13
  14. 14. 3.5. Color A visualization of the 1993 car models, showing the use of color to display the car’s length. Here length is also associated with the yaxis and is plotted against wheelbase. In this figure, blue indicates a shorter length, while yellow indicates a longer length. 14
  15. 15. 3.6. Orientation Sample visualization of the 1993 car models data set depicting using highway milesper-gallon versus fuel tank capacity (position) with the additional data variable, midrange price, used to adjust mark orientation. 15
  16. 16. 3.7. Texture Example visualization using texture to provide additional information about the 1993 car models data set, showing the relationship between wheelbase versus horsepower (position) as related to car types, depicted by different textures. 16
  17. 17. 4.9. Senay and Ignatius (1994) VISTA VISTA’s composition rules Hikmet Senay and Eve Ignatius. “A Knowledge-Based System for Visualization Design.” IEEE Comput. Graph. Appl. 14:6 (1994), 36–47. 17
  18. 18. 2. Two-Dimensional Data A cityscape showing the density of air traffic over the United States at a particular time period. 18
  19. 19. Landscapes Example: News articles visualized as a landscape • visualization of the data as perspective landscape • the data needs to be transformed into a (possibly artificial) 2D spatial representation which preserves the characteristics of the data
  20. 20. High-Dimensional Data Parallel Coordinates
  21. 21. Parallel Coordinates
  22. 22. Parallel Coordinates (Example) Baseball League Database (1996)
  23. 23. Chernoff-Faces Do you spot any trend?
  24. 24. Stick Figures 5-dim. Image data from the great lake region
  25. 25. 4.3. Visualization Techniques OpenDX ( A storm cloud visualization containing glyphs showing wind direction and strength. 25
  26. 26. 4.3. Visualization Techniques OpenDX ( Flow data visualized using ribbons, with vorticity mapped to twist. 26
  27. 27. 4.3. Visualization Techniques OpenDX ( Corresponding points from several time slices can be joined to form streaklines. 27
  28. 28. 1.1. Space-Filling Methods Jing Yang, Matthew O.Ward, Elke A. Rundensteiner, and Anilkumar Patro. “InterRing: A Visual Interface for Navigating and Manipulating Hierarchies.” Information Visualization 2:1 (2003), 16–30. A sample hierarchy and the corresponding treemap display. 28
  29. 29. 1.1 Cushion Treemap Idea: Use shading to construct a surface which shape encodes the tree structure. The human visual system is trained to interpret variations in shade as illuminated surfaces . see: H. van de Wetering and J. van Wijk. Cushion treemaps: Visualization of hierarchical information.In Proceedings of the IEEE Symposium on Information Visualization (InfoVis), 2005. 29
  30. 30. 1.1 Newsmap 30
  31. 31. 1.1 Treemap Bederson, B.B., PhotoMesa: a zoomable image browser using quantum treemaps and bubblemaps, Proceedings of the 14th annual ACM symposium on User interface software and technology, pp 71-80, 2001, ACM 31
  32. 32. 1.1. Space-Filling Methods Jing Yang, Matthew O.Ward, Elke A. Rundensteiner, and Anilkumar Patro. “InterRing: A Visual Interface for Navigating and Manipulating Hierarchies.” Information Visualization 2:1 (2003), 16–30. A sample hierarchy and the corresponding sunburst display. 32
  33. 33. 2.1. Graphs-Drawing Conventions Edge oriented Clustering oriented Orthogona l Hierarchica l Circular Hierarchy oriented Node oriented ForcePictures from: Directed 33
  34. 34. Hierarchical Edge Bundling 34
  35. 35. Hierarchical Edge Bundling More details in the paper: • Bundling Strength • Alpha blending Danny Holten, Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data, IEEE TVCG, Vol 12, No 5, 2006 (Best Paper InfoVis 2006) 35
  36. 36. 3.2. Tabular Displays Inxight Table Lens ( An example of Inxight Table Lens showing the cars data set sorted first by car origin and then by MPG. 36
  37. 37. 5.2. Hybrid Approaches Example: XMDV Tool XMDV allows to dynamically link and brush scatterplot matrices, star icons, parallel coordinates, and dimensional stacking (combination of geometric, icon-based, hierarchical and dynamic techniques). Matthew O. Ward, "Linking and Brushing.", Encyclopedia of Database Systems 2009: 1623-1626. 37
  38. 38. 5.2. Guidelines for Using Multiple Views • Rule of Complementary: Use multiple views when different views bring out correlations and/or disparities. 38
  39. 39. Georges Grinstein, UMass Lowell – Daniel
  40. 40. 1. Visualizing Spatial Data • Type of map depends on the properties of the data, for example: Dot maps Line diagrams Land use maps[2] Isoline maps[3] Chloropleth maps Surface maps[1] [1] K. Crane, Spin transformations of discrete surfaces, 2011 [2] C. Power, Hierarchical fuzzy pattern matching for the regional comparison of land use maps, 2001 [3] I. Solis, Isolines: energy-efficient mapping in sensor networks, 2005 42
  41. 41. 8.3.1 Dot Map A simple dot map of commercial wireless antennas in the USA 43
  42. 42. 2.1. Pixel Maps 0:00 am (EST) 6:00 am (EST) 10:00 pm (EST) 6:00 pm (EST) The figures display U.S. Telephone Call Volume at four different times during one day. The idea is to place the first data items at their correct position and position overlapping data points at nearby unoccupied positions. Overlap-free visualization! Daniel A. Keim, Christian Panse, and Mike Sips. “Visual Data Mining of Large Spatial Data Sets.” In Databases in Networked Information Systems, Lecture Notes in Computer Science, 2822, Lecture Notes in Computer Science, 2822, pp. 201–215. Berlin: Springer, 2003. 44
  43. 43. 3.2. Flow Maps and Edge Bundling The visualization of traffic flows of the United States to other countries suffers under visual clutter. Arc maps try to avoid overlapping by mapping 2D lines into 3D arcs. Partially translucent arcs avoid overplotting. K.C. Cox. 3D geographic network displays. ACM Sigmod Record, 1996 45
  44. 44. 3.2. Flow Maps and Edge Bundling Flow maps are used to show the movement of objects from one location to another. They avoid overlapping by merging edges by, for example, clustering. (a) Minard’s 1864 flow map of wine exports from France [20] (b) Tobler’s computer generated flow map of migration from California from 1995 - 2000. [18; 19] (c) A flow map produced by our system that shows the same migration data. D. Pahn et al. Flow map layout. Information Visualization, 2005. 46
  45. 45. 3.2. Flow Maps and Edge Bundling The visualizations show IP flow traffic from external nodes on the outside to internal nodes, visualized as treemaps on the inside. The edge bundling visualization (right side) significantly reduces the visual clutter compared to the straight line visualization (left side). Fabian Fischer, Florian Mansmann, Daniel A. Keim, Stephan Pietzko, and Marcel Waldvogel. “Large-Scale Network Monitoring for Visual Analysis of Attacks.” In Visualization for Computer Security: 5th International Workshop, VizSec 2008, Cambridge, MA, USA, September 15, 2008, Proceedings, Lecture Notes in Computer Science, 5210, pp. 111–118. Berlin: Springer- Verlag, 2008. 47
  46. 46. Flowstrates: Exploration of Temporal Origin-Destination Data Ilya Boyandin, Enrico Bertini, Peter Bak, Denis Lalanne. Flowstrates: An Approach for Visual Exploration of Temporal Origin-Destination Data, EuroVis 2011 48
  47. 47. Applied “Force-Directed Edge Bundling”, Holten 2009 49
  48. 48. 10.2 Visualization techniques for serial data Making a visualization time-dependent Every visualization can be made time dependent by providing several visualizations for several time points… … in parallel … as a sequence (Animation) 1980 1990 2000
  49. 49. 10.2 Visualization techniques for serial data Time-Series Plot One Parameter Several Parameters
  50. 50. 10.2 Visualization techniques for serial data Gantt Chart
  51. 51. 10.2 Visualization techniques for serial data LifeLines LifeLines for medical records. Consultations, manifestations, documents, hospitalizations and treatments are shown in this record. Each doctor has a unique color. Line thickness shows severity and dosage.
  52. 52. 10.2 Visualization techniques for serial data History Flow a u t h o rs Text of page Editing history of the wikipedia „Microsoft“ page History flow visualization
  53. 53. 10.2 Visualization techniques for serial data ThemeRiver ThemeRiver depicts thematic variations over time within a large collection of documents • • horizontal distance between two points  time interval • total vertical distance  collective strength of the selected themes • Data: Collection of patents from one company directed flow from left to right  movement through time colored currents  individual themes
  54. 54. 10.2 Visualization techniques for serial data Histogram vs. ThemeRiver • Discrete values • Exact values • Hard to follow a single current • Continuous flow • Interpolation, approximation • Easy to follow a single current (curving continuous lines)
  55. 55. 10.2 Visualization techniques for serial data Importance-Driven Visualization Goal: Display large numbers of time series such that • relative importance and hierarchy relations can be quickly perceived • the time series can easily be compared (by arranging them in a regular layout)
  56. 56. 10.2 Visualization techniques for serial data Importance-Driven Visualization 80 time series from 9 different S&P500 Industries i-measure: volatility of stocks color: normalized stock open price from green (low) through yellow (medium) to red (high)
  57. 57. 10.2 Visualization techniques for serial data Space-Time Cube The space-time cube: I. An example of the author’s travels on an average Thursday in Enschede, the Netherlands. II. The space-time cube’s basics: a Space-Time Path and its footprint. The vertical line in the path represents the time a person remains at the same location, called station. III. A Space-Time-Prism (STP) indicates the locations that can be reached in a particular time interval (the Potential Path Space (PPS)). The projection of the PPS on the map results in the Potential Path Area (PPA).
  58. 58. Seesoft color = statistic of interest, here: code age
  59. 59. Seesoft Color is mapped to code age. Three representations of code in the window: - text - line representation - pixel representation
  60. 60. ThemeScape Document Visualization
  61. 61. ThemeScape Document Visualization A themescape representation of 700 articles related to the financial industry
  62. 62. Newsmap (Germany)
  63. 63. Text and Geo (1) Chae et al. 2012 Seasonal Trend Decomposition WS 2011 / 12 Computational Methods for Document Analysis, Prof. Dr. D. A. Keim 65
  64. 64. Word Clouds – 4 years of GK publications at the University of Konstanz (size of term corresponds to the frequency of the term within the publications)
  65. 65. Hyperbolic Browser A hyperbolic browser representation of hierarchically ordered collection of documents
  66. 66. 1.2. Selection Operators - techniques for selecting and highlighting objects and groups of objects point is selected  highlighted  and can be dragged - often to identify the set of objects that will be the argument to some action 68
  67. 67. 1.3. Filtering Operators Dynamic Queries = visual means of specifying conjunctions e.g.: FilmFinder by C. Ahlberg and B. Shneiderman - sliders or radio buttons to select value ranges for variables in the Data Table - cases for which all the variables fall into the specified ranges are displayed 69
  68. 68. 1.3. Filtering Operators XmdvTool ( Filtering rows and columns of the grades data set using XmdvTool. 70
  69. 69. 1.6. Connection Operators interactive changes made in one visualization are automatically reflected in the other visualizations cases that are selected in one view… … are automatically also selected in all the other views Screenshots of XMDV-Tool 71
  70. 70. Overview & Detail Detail Overview 72
  71. 71. 1. Screen Space Perspective Wall • The data outside the focal area are perspectively reduced in size • The perspective wall is a variant of the bifocal lense display which horizontally compresses the sides of the workspace by direct scaling Documents arranged on a Perspective Wall 73
  72. 72. 1. Screen Space - Fisheye  original graph and fisheye view of the graph  shows an area of interest quite large and with detail and the other areas successively smaller and in less detail  graph visualization using a fisheye perspective 74
  73. 73. 5. Data Structure Space Wei Peng, Matthew O. Ward, and Elke A. Rundensteiner. “Clutter Reduction in Multi Dimensional Data Visualization Using Dimension Reordering.” In INFOVIS ’04: Proceedings of the IEEE Symposium on Information Visualization, pp. 89–96. Washington, DC: IEEE Computer Society, 2004. Example of shape simplification via dimension reordering. The left image shows the original order, while the right image shows the results of reordering to reduce concavities and increase the percentage of symmetric shapes. 75
  74. 74. 6. Visualization Structure Space – TableLens TableLens with distortion (expansion) to show names Visualization of a baseball database with a few rows being selected in full detail 76
  75. 75. 7. Animating Transformations Example of a velocity curve corresponding to the position curve, with ease-in, ease-out movement. Example of an acceleration curve corresponding to the position curve, with ease-in, ease-out movement. 77
  76. 76. 3. System Performance - Use Case (1)  Practice Fusion Medical Research Data 15,000 de-identified health records, 7 different tables (patients, diagnosis, medications, etc.)  Data handling and visualization functionality evaluation Task: visualize the distribution of women’s pregnancy age
  77. 77. 3. System Performance - Use case (2)  VAST challenge 2011    1,023,057 geo-tagged microblogging messages with time stamps map information for the artificial “Vastopolis” metropolitan area Geo-spatial-temporal data analysis functionality evaluation Spotfire Tableau Qlikview JMP Task: visualize the geo-referenced disease outbreaks over the given time span 79
  78. 78. VAST 2013 Examples
  79. 79. Purdue University SPRING RAIN 81
  80. 80. INRIA (Perin)
  81. 81. Arizona State University (Lu)
  82. 82. University of Konstanz (el Assady) 2
  83. 83. University of Konstanz (Schreck) 2
  84. 84. University of Stuttgart (Kruger) 3
  85. 85. Middlesex University (O’Connor-Read)
  86. 86. Central South University (Zhao)
  87. 87. Middlesex University (Choudhury) 92
  88. 88. Purdue University 93
  89. 89. SAS
  90. 90. Central South University
  91. 91. Peking University and Universität Stuttgart
  92. 92. University of Konstanz
  93. 93. University of North Carolina Charlotte