Information Visualization
Karen E. Gutzman
March 2014
Road map
What’s a librarian to do?
2
Words to live by
Resources
Words to live by
• Edward Tufte’s seminar: Presenting
Data and Information
• Katy Börner’s course: Information
Visualization
3
Words to live by
4
…the task of the designer is to give visual access to the subtle
and the difficult …
-Edward Tufte (2001, p. 191)
Words to live by
5
Graphics reveal data
 greatest number of ideas
 shortest time
 least ink
 smallest space
Example 1. Brenner (2002)
Words to live by
6
Graphics reveal data
 greatest number of ideas
 shortest time
 least ink
 smallest space
Words to live by
7
Graphics reveal data
 greatest number of ideas
 shortest time
 least ink
 smallest space
Example 2. Tufte (n.d.)
Resources
• Data sources
• Visualization tools
8
Resources: data sources
9
Resources: visualization tools
10
Supports visualization of datasets
 Temporal
 Geospatial
 Topical
 Network analysis
What’s a
librarian to do?
• Dream up ideas
• Use the data
11
Temporal data
Types of visualizations
 Time-Series Plot
 Histogram
 Stacked graph
 Burst Detection (see visualization)
What to visualize
 The increase in the number of
citations for a specific publication
over time
 Number of new collaborators
participating in a specific program
 The amount of funding provided to
an organization or researcher (see
visualization)
12
Geospatial Data
Types of visualizations
 Proportional Symbol Map
 Choropleth Maps (see visualization)
What to visualize
 The location (state/country) of
researchers, experts, or collaborators
 The amount of funding on specific
topic by geographical area
 The location of authors who have
cited, viewed, or downloaded a
particular publication or work (see
visualization)
13
Topical Data
Types of visualizations
 Cross Maps
 Wordle
 Topic Bursts (see visualization)
What to visualize
 Word co-occurrence on a single
document, book or other text-
based research output combined
with burst detection (see
visualization)
14
Topical Data
Types of visualizations
 Cross Maps
 Wordle (see visualization)
 Topic Bursts
What to visualize
 Word co-occurrence on a single document,
book or other text-based research output
combined with burst detection (see
visualization)
15
Word bursts of top 50 terms in the abstracts of the top 10
most highly cited publications for Dr. Name Redacted.
(from wordle.net)
Network Data
Types of visualizations
 Radial Tree Graph
 Tree Map
 Social Networks (see visualization)
What to visualize
 The hierarchy of a network drive or organization
 Identify highly connected authors or papers
through collaborations or citations (see
visualization)
16
Dr. Name Redacted
Dr. Co-Author 2
Dr. Co-Author 1
Author Co-occurrence
(co-author) Network
Sci2 Team. (2009). Science of Science (Sci2) Tool. Indiana University and SciTech Strategies, https://sci2.cns.iu.edu.
Sources
17
Börner, K., Polley, D.E. (2014). Visual Insights. Cambridge, MA. Massachusetts Institute of
Technology
Brenner, H. (2002). Long-term survival rates of cancer patients achieved by the end of the
20th century: a period analysis. The Lancet, 360, pp. 1131-1135
Tufte, E. (2001). The Visual Display of Quantitative Information. 2nd Ed. Cheshire, CT.
Graphics Press.
Tufte, E. (n.d.) Cancer survival rates: tables, slopegraphs, barcharts. [online forum] Retrieved
from: http://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0000Jr
Sci2 Team. (2009). Science of Science (Sci2) Tool. Indiana University and SciTech Strategies,
https://sci2.cns.iu.edu
Information Visualization Portfolio: http://visualizinginformationportfolio.blogspot.com/
Thank you
18
This research was supported in part by an appointment to the NLM Associate Fellowship Program sponsored by
the National Library of Medicine and administered by the Oak Ridge Institute for Science and Education.

Information Visualization

  • 1.
  • 2.
    Road map What’s alibrarian to do? 2 Words to live by Resources
  • 3.
    Words to liveby • Edward Tufte’s seminar: Presenting Data and Information • Katy Börner’s course: Information Visualization 3
  • 4.
    Words to liveby 4 …the task of the designer is to give visual access to the subtle and the difficult … -Edward Tufte (2001, p. 191)
  • 5.
    Words to liveby 5 Graphics reveal data  greatest number of ideas  shortest time  least ink  smallest space Example 1. Brenner (2002)
  • 6.
    Words to liveby 6 Graphics reveal data  greatest number of ideas  shortest time  least ink  smallest space
  • 7.
    Words to liveby 7 Graphics reveal data  greatest number of ideas  shortest time  least ink  smallest space Example 2. Tufte (n.d.)
  • 8.
    Resources • Data sources •Visualization tools 8
  • 9.
  • 10.
    Resources: visualization tools 10 Supportsvisualization of datasets  Temporal  Geospatial  Topical  Network analysis
  • 11.
    What’s a librarian todo? • Dream up ideas • Use the data 11
  • 12.
    Temporal data Types ofvisualizations  Time-Series Plot  Histogram  Stacked graph  Burst Detection (see visualization) What to visualize  The increase in the number of citations for a specific publication over time  Number of new collaborators participating in a specific program  The amount of funding provided to an organization or researcher (see visualization) 12
  • 13.
    Geospatial Data Types ofvisualizations  Proportional Symbol Map  Choropleth Maps (see visualization) What to visualize  The location (state/country) of researchers, experts, or collaborators  The amount of funding on specific topic by geographical area  The location of authors who have cited, viewed, or downloaded a particular publication or work (see visualization) 13
  • 14.
    Topical Data Types ofvisualizations  Cross Maps  Wordle  Topic Bursts (see visualization) What to visualize  Word co-occurrence on a single document, book or other text- based research output combined with burst detection (see visualization) 14
  • 15.
    Topical Data Types ofvisualizations  Cross Maps  Wordle (see visualization)  Topic Bursts What to visualize  Word co-occurrence on a single document, book or other text-based research output combined with burst detection (see visualization) 15 Word bursts of top 50 terms in the abstracts of the top 10 most highly cited publications for Dr. Name Redacted. (from wordle.net)
  • 16.
    Network Data Types ofvisualizations  Radial Tree Graph  Tree Map  Social Networks (see visualization) What to visualize  The hierarchy of a network drive or organization  Identify highly connected authors or papers through collaborations or citations (see visualization) 16 Dr. Name Redacted Dr. Co-Author 2 Dr. Co-Author 1 Author Co-occurrence (co-author) Network Sci2 Team. (2009). Science of Science (Sci2) Tool. Indiana University and SciTech Strategies, https://sci2.cns.iu.edu.
  • 17.
    Sources 17 Börner, K., Polley,D.E. (2014). Visual Insights. Cambridge, MA. Massachusetts Institute of Technology Brenner, H. (2002). Long-term survival rates of cancer patients achieved by the end of the 20th century: a period analysis. The Lancet, 360, pp. 1131-1135 Tufte, E. (2001). The Visual Display of Quantitative Information. 2nd Ed. Cheshire, CT. Graphics Press. Tufte, E. (n.d.) Cancer survival rates: tables, slopegraphs, barcharts. [online forum] Retrieved from: http://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0000Jr Sci2 Team. (2009). Science of Science (Sci2) Tool. Indiana University and SciTech Strategies, https://sci2.cns.iu.edu Information Visualization Portfolio: http://visualizinginformationportfolio.blogspot.com/
  • 18.
    Thank you 18 This researchwas supported in part by an appointment to the NLM Associate Fellowship Program sponsored by the National Library of Medicine and administered by the Oak Ridge Institute for Science and Education.

Editor's Notes

  • #11 Sci2 Tool The Science of Science (Sci2) Tool is a modular toolset specifically designed for the study of science. It supports the temporal, geospatial, topical, and network analysis and visualization of scholarly datasets at the micro (individual), meso (local), and macro (global) levels. Other Colblindor (http://www.color-blindness.com/) Zoom.it (http://zoom.it) Gigapan (http://gigapan.com)
  • #12 Dream up ideas and use the data. This is a very circular process. You are looking for interesting data, not that boring data I talked about earlier. So this can time quite a bit of time.
  • #13 “WHEN” The time-series is the most frequently used form of graphic design. With one dimension marching along to the regular rhythm of seconds, minutes, hours, days, weeks, months, years…etc. Are at their best for big datasets with real variability. (not of house of representatives leaving VD pg. 37. --------------- Enhance the explanatory power by adding spatial dimensions, so that the data are moving over space as well as over time. (VD 40) Multiple time-series allow for comparison within each series over time (as do time-series plots), but also comparison between the three different sample radio bands… Stacked graph Shows trends over time…look for stability or cycles caused by seasons, etc. Spark lines are intense simple word-sized graphics (glucose, VD 171)
  • #14 “WHERE” visualizations use location information to identify their position or movement over geographic space. Geographical location Color Value
  • #15 WHAT The goal is to identify topics in unstructured texts.
  • #16 WHAT The goal is to identify topics in unstructured texts.
  • #17 “WITH WHOM” Tree datasets, such as directory structures or organizational hierarchies, classification hierarchies can be displayed using tree views, tree maps or tree graphics.