The document describes an exploratory study on data sketching for visual representation. Researchers conducted sessions where participants were asked to sketch representations of behavior-situation data on paper in any way they wished. Participants created a variety of representations ranging from numeric plots and matrices to more abstract pictorial designs. Analysis of the 35 sketches created a continuum from more numerically-focused and countable designs to more abstract visualizations. Common representation types included dot plots, matrices, bar charts, line graphs and parallel coordinates for more data-driven sketches and pictorial, graph-like and ranked list designs for more abstract sketches.
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An Exploratory Study of Data Sketching for Visual Representation - EuroVis 2015
1. AN EXPLORATORY STUDY
OF DATA SKETCHING
FOR VISUAL REPRESENTATION
Jagoda Walny, Samuel Huron, and Sheelagh Carpendale
InnoVis & Interactions Lab, University of Calgary
EuroVis 2015, Cagliari, Italy
32. 22
COUNTABLE DOT PLOTS &
MATRICES
BARCHARTS LINE GRAPH &
PARALLEL COORDS
GRAPH LIKE VENN PICTORIALRANKED LIST
NUMERIC ABSTRACT
Pictorial Representations
33. 23
COUNTABLE DOT PLOTS &
MATRICES
BARCHARTS LINE GRAPH &
PARALLEL COORDS
GRAPH LIKE VENN PICTORIALRANKED LIST
NUMERIC ABSTRACT
Pictorial Representations
34. 23
COUNTABLE DOT PLOTS &
MATRICES
BARCHARTS LINE GRAPH &
PARALLEL COORDS
GRAPH LIKE VENN PICTORIALRANKED LIST
NUMERIC ABSTRACT
Pictorial Representations
35. Spectrum of Data
Reports
“Please describe what you learned or
found interesting about this data during
the session.
(there are no wrong answers)”
36. A B C D E F
Information Intrinsic to Dataset
25
37. A B C D E F
Information Intrinsic to Dataset
• A. Individual values
• e.g. “fighting in church is inappropriate”
25
38. A B C D E F
Information Intrinsic to Dataset
• A. Individual values
• e.g. “fighting in church is inappropriate”
• B. Summarized rows or columns
• e.g. “there aren’t many behaviours appropriate in church”
25
39. A B C D E F
Information Intrinsic to Dataset
• A. Individual values
• e.g. “fighting in church is inappropriate”
• B. Summarized rows or columns
• e.g. “there aren’t many behaviours appropriate in church”
• C. Compared two rows or columns
• “Date and own room have the similar rating for ‘kiss’. Other ratings in
these two situations are close to each other.”
25
40. A B C D E F
Dataset-level Trends and Comparisons
26
41. A B C D E F
Dataset-level Trends and Comparisons
• Compare three or more rows/columns
26
42. A B C D E F
Dataset-level Trends and Comparisons
• Compare three or more rows/columns
• Group items by value
26
43. A B C D E F
Dataset-level Trends and Comparisons
• Compare three or more rows/columns
• Group items by value
• Make global comparisons
26
44. A B C D E F
Dataset-level Trends and Comparisons
• Compare three or more rows/columns
• Group items by value
• Make global comparisons
• E.g. “Several situations which have a similar rate for one specific behavior
tend to be similar for other behaviors.”
26
45. A B C D E F
Dataset-level Trends and Comparisons
• Compare three or more rows/columns
• Group items by value
• Make global comparisons
• E.g. “Several situations which have a similar rate for one specific behavior
tend to be similar for other behaviors.”
• E.g. Binning: “completely appropriate”, “somewhat appropriate”,
“highly inappropriate”
26
46. A B C D E F
Including Extrinsic Information
27
47. A B C D E F
Including Extrinsic Information
• Classify
• e.g. “comfortable”, “safe”, “aggressive”
27
48. A B C D E F
Including Extrinsic Information
• Classify
• e.g. “comfortable”, “safe”, “aggressive”
• Compare to expectations
• “mumbling + talking diverged more than expected.”
27
49. A B C D E F
Including Extrinsic Information
• Classify
• e.g. “comfortable”, “safe”, “aggressive”
• Compare to expectations
• “mumbling + talking diverged more than expected.”
• Explain in domain context
• “people care a lot in job interviews”
27
51. A B C D E F
Analytic Potential
• Hypotheses or conjectures about the reasons behind the
values in the dataset
28
52. A B C D E F
Analytic Potential
• Hypotheses or conjectures about the reasons behind the
values in the dataset
• E.g. “it appears the park might be the same as one's own
room... anonymity? “
28
53. A B C D E F
Analytic Potential
• Hypotheses or conjectures about the reasons behind the
values in the dataset
• E.g. “it appears the park might be the same as one's own
room... anonymity? “
• E.g. “I found out that there seem to be more women in
the dataset than men because most inappropriate
behaviours to men (i.e. Talking in the restroom) is still
above 5. ”
28
87. ➡ Viewing representations in terms of levels
of data description
➡ Understanding advantages of sketching
for representation and data understanding
49
88. COUNTABLE DOT PLOTS &
MATRICES
BARCHARTS LINE GRAPH &
PARALLEL COORDS
GRAPH LIKE VENN PICTORIALRANKED LIST
NUMERIC ABSTRACT
50
89. A B C D E F
COUNTABLE DOT PLOTS &
MATRICES
BARCHARTS LINE GRAPH &
PARALLEL COORDS
GRAPH LIKE VENN PICTORIALRANKED LIST
NUMERIC ABSTRACT
50
90. 1
2
3
4
A B C D E F
COUNTABLE DOT PLOTS &
MATRICES
BARCHARTS LINE GRAPH &
PARALLEL COORDS
GRAPH LIKE VENN PICTORIALRANKED LIST
NUMERIC ABSTRACT
50
91.
92. Thank you:
An Exploratory Study of Data Sketching for
Visual Representation
Jagoda Walny - jkwalny@ucalgary.ca
Samuel Huron - samuel.huron@cybunk.com
Sheelagh Carpendale - sheelagh@ucalgary.ca
PROJECT WEBSITE: j.mp/datasketching