CS 725/825 - Information Visualization
Fall 2013: Tues/Thurs 1:30-2:45pm, E&CS 2120

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Tableau's data visualization software is provided through the Tableau for Teaching program.

IEEE VIS 2013 Notes


LineUp: Visual Analysis of Multi-Attribute Rankings

  • Samuel Gratzl, Alexander Lex, Nils Gehlenborg, Hanspeter Pfister, Marc Streit
  • http://vimeo.com/74501347 - 30 sec video
  • http://www.youtube.com/watch?v=iFqCBI4T8ks - 5 min video
  • http://www.jku.at/cg/content/e152197/... (paper)
  • Problem: While the visualization of a ranking itself is straightforward, its interpretation is not, because the rank of an item represents only a summary of a potentially complicated relationship between its attributes and those of the other items. It is also common that alternative rankings exist which need to be compared and analyzed to gain insight into how multiple heterogeneous attributes affect the rankings.
  • What: LineUp - a novel and scalable visualization technique that uses bar charts. This interactive technique supports the ranking of items based on multiple heterogeneous attributes with different scales and semantics. LineUp can also be used to compare multiple alternative rankings on the same set of items, for example, over time or across different attribute combinations.
  • Fig 2 shows different ranking visualization techniques

What Makes a Visualization Memorable?

  • Michelle A. Borkin, Azalea A. Vo, Zoya Bylinskii, Phillip Isola, Shashank Sunkavalli, Aude Oliva, Hanspeter Pfister
  • http://vimeo.com/74413611
  • http://cvcl.mit.edu/papers/Borkin_etal_MemorableVisualization_TVCG2013.pdf
  • What: Ran the largest scale visualization study to date using 2,070 single-panel visualizations, categorized with visualization type (e.g., bar chart, line graph, etc.), collected from news media sites, government reports, scientific journals, and infographic sources. Each visualization was annotated with additional attributes, including ratings for data-ink ratios and visual densities. Using Amazon¬ís Mechanical Turk, we collected memorability scores for hundreds of these visualizations.
  • Findings: Discovered that observers are consistent in which visualizations they find memorable and forgettable. We find intuitive results (e.g., attributes like color and the inclusion of a human recognizable object enhance memorability) and less intuitive results (e.g., common graphs are less memorable than unique visualization types).
  • Fig 1 - most memorable, most memorable w/o pics, least memorable
  • Fig 2 - breakdown of visualization categories by visualization sources

Visual Sedimentation

SoccerStories: A Kick-off for Visual Soccer Analysis

  • Charles Perin, Romain Vuillemot, Jean-Daniel Fekete
  • http://vimeo.com/74415805
  • http://hal.inria.fr/docs/00/84/67/18/PDF/soccerstories.pdf
  • Problem: However, soccer analysts we collaborated with consider that quantitative analysis alone does not convey the right picture of the game
  • What: Our system provides an overview+detail interface of game phases, and their aggregation into a series of connected visualizations, each visualization being tailored for actions such as a series of passes or a goal attempt
  • Fig 10 shows user interface

SketchStory: Telling More Engaging Stories with Data through Freeform Sketching


Visual Traffic Jam Analysis Based on Trajectory Data

  • Zuchao Wang, Min Lu, Xiaoru Yuan, Junping Zhang, Huub van de Wetering
  • http://vimeo.com/74411645
  • http://vis.pku.edu.cn/research/publication/vast13_trafficjam.pdf
  • What: An interactive system for visual analysis of urban traffic congestion based on GPS trajectories, extract and derive traffic jam information from trajectories. Traffic speed on each road segment is computed and traffic jam events are automatically detected. Spatially and temporally related events are concatenated in traffic jam propagation graphs
  • How: Figure 2 shows work flow of the system
  • SHOW FIG 5 in graph. Why is this a particularly good colormap for this data?

Temporal Event Sequence Simplification

  • Megan Monroe, Rongjian Lan, Hanseung Lee, Catherine Plaisant, Ben Shneiderman
  • http://vimeo.com/74412630 - 30 sec video
  • http://vimeo.com/39536311 - 2:41 video (EventFlow) SHOW EVENT FLOW FIRST
  • http://hcil2.cs.umd.edu/trs/2013-11/2013-11.pdf
  • http://www.cs.umd.edu/hcil/eventflow/
  • Problem: The difficulty in using electronic health records (EHRs) for research purposes is that both patient selection and record analysis must be conducted across very large, and typically very noisy datasets.
  • What: Extension of EventFlow, a visualization tool that transforms an entire dataset of temporal event records into an aggregated display, allowing researchers to analyze population-level patterns and trends. This work presents a series of user-driven data simplifications that allow researchers to pare event records down to their core elements. Present a novel metric for measuring visual complexity.
  • Section 8 - Basketball Play-by-Play - Figs 9-13 - UMd vs. UNC

HierarchicalTopics: Visually Exploring Large Text Collections Using Topic Hierarchies

  • Wenwen Dou, Li Yu, Xiaoyu Wang, Zhiqiang Ma, William Ribarsky
  • http://vimeo.com/74410866
  • http://viscenter.uncc.edu/sites/viscenter.uncc.edu/files/hierarchicaltopics.pdf
  • Topic-based text summarization methods coupled with interactive visualizations
  • Problem: It is difficult for most of current topic-based visualizations to represent large number of topics without being cluttered or illegible
  • Goal: Facilitate the representation and navigation of a large number of topics
  • How: HT integrates a computational algorithm, Topic Rose Tree, with an interactive visual interface. The Topic Rose Tree constructs a topic hierarchy based on a list of topics. The interactive visual interface is designed to present the topic content as well as temporal evolution of topics in a hierarchical fashion.
  • Uses ThemeRiver as part of the vis

Visual Analysis of Topic Competition on Social Media