1. The Thought behind Creating a Visualization

2. “thinking” about visualizations
Thinking visually … our goal is to combine the components we’ve reviewed so far an balance them for the task, the audience, and the data:
Are the data univariate, bivariate, or multivariate?
Is there a trend in the data that suggest a design? For instance time-based data and bivariate data suggest chart with two axes, time on the x access, dimensions on the y axes.
But a history- or image-oriented one - suggests a richer visual- ladened chart. Compare …

3. Two charts of similar data, different orientations, and visual expressions

4. Compare these timelines of art
Same audience, task, and similar data …

5. A chart that, perhaps, has gone too far …
Besides reducing or clustering the data, what other options

6. InfoVis:
Aesthetic
elements
Data
Color,
Type
Composition

7. Data - what trend is being suggested by the data?
InfoVis:
Aesthetic
elements
Univariate,
Bivariate,
Multivariate
Data - what trend is being suggested by the data?
Do the symbols harmonize with the data? Are aesthetic elements that contribute to the visual appeal overtaking legibility?
Data
Color,
Type
Composition
Are the colors indexed to the data; suggest patterns, groups, differences? Does the choice of type support legibility, understanding scales and data values?
Does the composition encourage engagement? Situate the user to interpret the data?

8. Color-Oriented Data-Oriented
Check out these online guides about colors, their codes, and trends in color patterns
Color Chart: Reinventing color – 1950 to today
Soothsayer of Color
Color Hunt
Palletton
ColourLovers
Design Seeds
R (ggplot)
Tableau
Python with matplotlib, seaborne
and other libraries
Bokey
Spreadsheet charts
Data-Oriented

9. Approaching design Audience expectations
Types of interactivity with the end-user
Properties of the data
Domain or topic being studied
The task or purpose of the visualization
present | explain | explore | discover
Aesthetic design influences

10. Perspectives Models of approaching how to integrate the whole …
long-term engagement with the visualization based on the aesthetic qualities;
understanding the interplay between the data that are the foundation and their graphic representation’s placement, color, and similarities/differences;
interacting with the two bullet points above to arrive at a conclusion, or some new knowledge;
helping a viewer take further action (i.e., make a person aware of the phenomenon, knowledgeable, and hence “being informed”); and
providing a rationale for conclusion so that the viewer can articulate the reasons for his/her decisions
Models of approaching how to integrate the whole …
Data-centric
emphasizes the charting of the data
User-centric
tends to underscore affective states, user engagement
Visual-centric
leads more towards imagery but can overwhelm the data (information graphics)
Communications-centric
balancing the above with user-interactivity to stimulate sustained questioning to arrive at warranted decisions

18. Treemap

19. force-directed

20. time-series

21. choropleth

22. Case studies discussion
Shayne
Quincy Public Library
Bonnie
Bonnie