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Data Visualizations Decoded 
Julie Rodriguez
The Problem 
No definitions 
No central repository 
No use case based approach
Visualization Taxonomies (220 years back) 
The Commercial & 
Political Atlas 
Layout 
Line, Bar, Pie 
Chart 
PLAYFAIR 
Semiology of 
Graphics 
Data Type, 
Layout 
Diagrams, 
Networks, 
Maps 
BERTIN 
The Eyes Have It: A Task by 
Data Type Taxonomy for 
Information Visualization 
The Structure of the 
Information Visualization 
Data Type 
1D,2D,3D, Temporal, Multi-dimensional, 
Tree, Network 
Task 
Overview, Zoom, Filter, 
Details, Relate, History, 
Extract 
SHNEIDERMAN 
Design Space 
Domain, 
Layout 
Scientific, GIS, 
Multi-dimensional, 
Information 
Landscapes, 
Nodes & Links, 
Trees, Text 
Transforms 
CARD 
Rethinking Visualization: A 
High-level Taxonomy 
Syntactic Structures 
in Graphics 
Layout 
Metric, 
topological, 
grouping, 
composite 
space 
ENGELHARDT 
Algorithm 
Discrete or 
Continuous 
TROY 
A taxonomy of visualization 
techniques using the data 
state reference model 
Domain, Data 
Type, Layout 
Scientific, GIS, 
2D, Multi-dimensional 
Plots, 
Information 
Landscapes, 
Trees, Network, 
Text, Web 
Visualization, 
Visualization 
Spreadsheets 
CHI 
1786 1967 1996 1997 2000 
2003 2004
Visualization Taxonomies (and counting) 
The business 
and web 
community 
have built 
soſtware 
solutions 
reflecting: 
 Layout 
 Data type 
Periodic Table for 
Management 
Data Type 
Data, 
Information, 
Concept, 
Strategy, 
Metaphor, 
Compound 
EPPLER 
Infodesign 
patterns 
Task 
Quantities, 
Proportions, 
Flows, 
Hierarchies, 
Networks, 
Spatial, 
Correlations, 
Navigation, 
Filtering, 
Arrangement, 
etc. 
BEHRENS 
What Makes a 
Visualization Memorable 
Data Type, 
Layout, Task 
Area, Bar, Circle, 
Diagram, 
Distribution, Grid& 
Matrix, Line, Map, 
Point, Table, Text, 
Trees & Network 
BORKIN 
2007 2008 2013 
Visualization Software 
Tableau 
Qlik 
Technical Computing 
MATLAB 
Wolfram 
Online Web Apps 
ManyEyes 
Plotly 
RAW 
Frameworks 
D3 
Highcharts 
Reviewing Data 
Visualization: an Analytical 
Taxonomical Study 
Layout, Task 
Spatialization, 
Shape, Color, 
Prospective 
Interaction 
RODRIGUES 
2006
5 
Changing the Question 
‘I want to see the scatter plot 
view of this data’ with ‘I want to 
see what the correlations are with 
this data’.
Collect & Organize
Collect & Organize & Discover 
Attributes 
Grouping Ranking 
Calculations 
Drill 
Down 
Time Correlations 
Flow Data 
Mapping Nodes Pattern Recognition
Publish 
SOURCE: http://www.sapient.com/content/dam/sapient/sapientglobalmarkets/pdf/thought-leadership/crossings-fall2012.pdf
Patterns of Use 
Comparisons: Attributes, Time, Rank 
Connections: Drill Down, Flow, Grouping, Networks 
Conclusions: Calculations, Correlations, Predictive
10 
Comparisons 
Attributes, Time, Rank
Defining the Question 
…..‘I want to 
see the 
attributes of 
this fund’. 
SOURCE: http://www.blackrockinternational.com/intermediaries/en-zz/funds-information/holdings/bgf-global-allocation-a2-usd
Attributes Understanding the 
characteristics of an object. 
SOURCE (image): Visualizing Financial Data, Rodriguez & Kaczmarek
Defining the Question 
…..‘I need to see what has 
occurred’.
Time 
SOURCE (image): Visualizing Financial Data, Rodriguez & Kaczmarek 
Tracking events as they 
unfold over time.
Defining the Question 
…..‘Within 1,000s of data 
points, I need to see who’s 
landed on top’.
Rank Establishing relationships between two 
or more items to introduce greater 
than, less than or equal to. 
SOURCE (image): Visualizing Financial Data, Rodriguez & Kaczmarek
Connections 
Flows, Drill Down, Groups, Networks
Defining the Question 
…..‘I need to 
see both 
aggregates 
& details’. 
SOURCE (image): Visualizing Financial Data, Rodriguez & Kaczmarek
Drill Down Shifting from summary to 
detail information. 
SOURCE (image): Visualizing Financial Data, Rodriguez & Kaczmarek
Defining the Question 
…..‘I need to 
see the 
influence 
and impact’. 
SOURCE: Smith College Annual Report 2013
Flows Transforming data from 
one stage to another. 
SOURCE (image): Visualizing Financial Data, Rodriguez & Kaczmarek
Defining the Question 
…..‘I need to see categorical 
presence’.
Groups 
SOURCE: Bl.ocks.com 
Creating categories from 
a data set.
Defining the Question 
…..‘I need to see 
connections and links’.
Networks Connecting the dots between 
discrete locations. 
SOURCE: Mappa Mundi
26 
Conclusions 
Calculations, Correlations, Predictive
Defining the Question 
…..‘I want to see the outcomes of this 
distance calculation’. 
SOURCE: Wikipedia
Calculations Translating equations to 
be visually deciphered. 
SOURCE: Wikipedia
Defining the Question 
…..‘I need to 
see the level 
of 
correlation’. 
SOURCE (image): Visualizing Financial Data, 
Rodriguez & Kaczmarek
Correlations Discovering congruency 
between data sets. 
SOURCE (image): Visualizing Financial Data, Rodriguez & Kaczmarek
Defining the Question 
…..‘I need to foresee the 
possibilities’.
Predictive Predicting outputs based 
on learned inputs. 
SOURCE (image): Visualizing Financial Data, Rodriguez & Kaczmarek
Collect & Organize & Discover… 
Attributes 
Grouping Ranking 
Calculations 
Drill 
Down 
Time Correlations 
Flow Data 
Mapping Nodes Pattern Recognition
www.vizipedia.com 
Browse 
Reference 
Contribute
35 
Questions/Comments 
Tweet: juliargentinaG 
Email: juliargentina@gmail.com

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Vizipedia prez

  • 1. Data Visualizations Decoded Julie Rodriguez
  • 2. The Problem No definitions No central repository No use case based approach
  • 3. Visualization Taxonomies (220 years back) The Commercial & Political Atlas Layout Line, Bar, Pie Chart PLAYFAIR Semiology of Graphics Data Type, Layout Diagrams, Networks, Maps BERTIN The Eyes Have It: A Task by Data Type Taxonomy for Information Visualization The Structure of the Information Visualization Data Type 1D,2D,3D, Temporal, Multi-dimensional, Tree, Network Task Overview, Zoom, Filter, Details, Relate, History, Extract SHNEIDERMAN Design Space Domain, Layout Scientific, GIS, Multi-dimensional, Information Landscapes, Nodes & Links, Trees, Text Transforms CARD Rethinking Visualization: A High-level Taxonomy Syntactic Structures in Graphics Layout Metric, topological, grouping, composite space ENGELHARDT Algorithm Discrete or Continuous TROY A taxonomy of visualization techniques using the data state reference model Domain, Data Type, Layout Scientific, GIS, 2D, Multi-dimensional Plots, Information Landscapes, Trees, Network, Text, Web Visualization, Visualization Spreadsheets CHI 1786 1967 1996 1997 2000 2003 2004
  • 4. Visualization Taxonomies (and counting) The business and web community have built soſtware solutions reflecting:  Layout  Data type Periodic Table for Management Data Type Data, Information, Concept, Strategy, Metaphor, Compound EPPLER Infodesign patterns Task Quantities, Proportions, Flows, Hierarchies, Networks, Spatial, Correlations, Navigation, Filtering, Arrangement, etc. BEHRENS What Makes a Visualization Memorable Data Type, Layout, Task Area, Bar, Circle, Diagram, Distribution, Grid& Matrix, Line, Map, Point, Table, Text, Trees & Network BORKIN 2007 2008 2013 Visualization Software Tableau Qlik Technical Computing MATLAB Wolfram Online Web Apps ManyEyes Plotly RAW Frameworks D3 Highcharts Reviewing Data Visualization: an Analytical Taxonomical Study Layout, Task Spatialization, Shape, Color, Prospective Interaction RODRIGUES 2006
  • 5. 5 Changing the Question ‘I want to see the scatter plot view of this data’ with ‘I want to see what the correlations are with this data’.
  • 7. Collect & Organize & Discover Attributes Grouping Ranking Calculations Drill Down Time Correlations Flow Data Mapping Nodes Pattern Recognition
  • 9. Patterns of Use Comparisons: Attributes, Time, Rank Connections: Drill Down, Flow, Grouping, Networks Conclusions: Calculations, Correlations, Predictive
  • 11. Defining the Question …..‘I want to see the attributes of this fund’. SOURCE: http://www.blackrockinternational.com/intermediaries/en-zz/funds-information/holdings/bgf-global-allocation-a2-usd
  • 12. Attributes Understanding the characteristics of an object. SOURCE (image): Visualizing Financial Data, Rodriguez & Kaczmarek
  • 13. Defining the Question …..‘I need to see what has occurred’.
  • 14. Time SOURCE (image): Visualizing Financial Data, Rodriguez & Kaczmarek Tracking events as they unfold over time.
  • 15. Defining the Question …..‘Within 1,000s of data points, I need to see who’s landed on top’.
  • 16. Rank Establishing relationships between two or more items to introduce greater than, less than or equal to. SOURCE (image): Visualizing Financial Data, Rodriguez & Kaczmarek
  • 17. Connections Flows, Drill Down, Groups, Networks
  • 18. Defining the Question …..‘I need to see both aggregates & details’. SOURCE (image): Visualizing Financial Data, Rodriguez & Kaczmarek
  • 19. Drill Down Shifting from summary to detail information. SOURCE (image): Visualizing Financial Data, Rodriguez & Kaczmarek
  • 20. Defining the Question …..‘I need to see the influence and impact’. SOURCE: Smith College Annual Report 2013
  • 21. Flows Transforming data from one stage to another. SOURCE (image): Visualizing Financial Data, Rodriguez & Kaczmarek
  • 22. Defining the Question …..‘I need to see categorical presence’.
  • 23. Groups SOURCE: Bl.ocks.com Creating categories from a data set.
  • 24. Defining the Question …..‘I need to see connections and links’.
  • 25. Networks Connecting the dots between discrete locations. SOURCE: Mappa Mundi
  • 26. 26 Conclusions Calculations, Correlations, Predictive
  • 27. Defining the Question …..‘I want to see the outcomes of this distance calculation’. SOURCE: Wikipedia
  • 28. Calculations Translating equations to be visually deciphered. SOURCE: Wikipedia
  • 29. Defining the Question …..‘I need to see the level of correlation’. SOURCE (image): Visualizing Financial Data, Rodriguez & Kaczmarek
  • 30. Correlations Discovering congruency between data sets. SOURCE (image): Visualizing Financial Data, Rodriguez & Kaczmarek
  • 31. Defining the Question …..‘I need to foresee the possibilities’.
  • 32. Predictive Predicting outputs based on learned inputs. SOURCE (image): Visualizing Financial Data, Rodriguez & Kaczmarek
  • 33. Collect & Organize & Discover… Attributes Grouping Ranking Calculations Drill Down Time Correlations Flow Data Mapping Nodes Pattern Recognition
  • 35. 35 Questions/Comments Tweet: juliargentinaG Email: juliargentina@gmail.com