Week 3: Color, Spatial Data Tamara Munzner Department of Computer - - PowerPoint PPT Presentation
Week 3: Color, Spatial Data Tamara Munzner Department of Computer Science University of British Columbia JRNL 520H, Special Topics in Contemporary Journalism: Data Visualization Week 3: 27 September 2016
http://www.cs.ubc.ca/~tmm/courses/journ16
JRNL 520H, Special Topics in Contemporary Journalism: Data Visualization Week 3: 27 September 2016
– don’t expect email answers until she returns; email Tamara instead!
–at Stanford Fri/Sat to give keynote at the Computation & Journalism symposium http://journalism.stanford.edu/cj2016/ –will still be answering email –no office hours in Sing Tao this week
– email tmm@cs.ubc.ca to arrange (late afternoon today or Wed are only possible times)
–in Portland Fri/Sat to give another keynote, will still be answering email –short office hours in Sing Tao next week: 12:30-1:30pm
2
–max 97, min 73, avg 86 –major sources of analysis problems:
–email went out in three rounds - did everybody receive it? –thanks to Curtis and Emi for reporting bug to us!
–interleave foundations & demos
3
4
5
Encode Arrange Express Separate Order Align
–simple analytics: totals –more disaggregation practice –Show Me
–beyond simple bars –challenges of missing data
6
–visual encoding practice –more filters practice –dual axes
–outlier removal for subsequent data analysis
–don’t be a jerk at sporting events!
7
–we didn’t have time to do together in class –straw poll: how many of you did this already?
–more practice with changing visual encodings –highlighting individual items
–different patterns result in different insights
8
9
10
Why? How? What?
Encode Arrange Express Separate Order Align Use
Map Color Motion Size, Angle, Curvature, ...
Hue Saturation Luminance
Shape
Direction, Rate, Frequency, ...
from categorical and ordered attributes
11
[Seriously Colorful: Advanced Color Principles & Practices. Stone.Tableau Customer Conference 2014.]
–identity for categorical
–magnitude for ordered
–lightness ≠ luminance
12
Saturation Luminance values Hue
Corners of the RGB color cube L from HLS All the same Luminance values
13
Wavelength (nm) IR UV Visible Spectrum
Small but important separation
–one achromatic luminance channel L –edge detection through luminance contrast –two chroma channels, R-G and Y-B axis
–8% of men are red/green color deficient –blue/yellow is rare
14
Lightness information Color information
[Seriously Colorful: Advanced Color Principles & Practices. Stone.Tableau Customer Conference 2014.]
15
Deuteranope Protanope Tritanope Normal vision
[Seriously Colorful: Advanced Color Principles & Practices. Stone.Tableau Customer Conference 2014.]
– vary luminance – change shape
16
Deuteranope simulation
[Seriously Colorful: Advanced Color Principles & Practices. Stone.Tableau Customer Conference 2014.]
17
Normal Deuteranope Tritanope Protanope
[Seriously Colorful: Advanced Color Principles & Practices. Stone.Tableau Customer Conference 2014.]
18
[Seriously Colorful: Advanced Color Principles & Practices. Stone.Tableau Customer Conference 2014.]
19
Image courtesy of John McCann
20
Image courtesy of John McCann
21
[Seriously Colorful: Advanced Color Principles & Practices. Stone.Tableau Customer Conference 2014.]
22
after [Color Use Guidelines for Mapping and
http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html]
Categorical Ordered Sequential Bivariate Diverging
Binary Diverging Categorical Sequential Categorical Categorical
23
after [Color Use Guidelines for Mapping and
http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html]
Categorical Ordered Sequential Bivariate Diverging
Binary Diverging Categorical Sequential Categorical Categorical
24
after [Color Use Guidelines for Mapping and
http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html]
Categorical Ordered Sequential Bivariate Diverging
Binary Diverging Categorical Sequential Categorical Categorical
25
–size heavily affects salience
–saturation & luminance: 3-4 bins max
after [Color Use Guidelines for Mapping and
http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html]
Categorical Ordered Sequential Bivariate Diverging
Binary Diverging Categorical Sequential Categorical Categorical
26
27
[Cinteny: flexible analysis and visualization of synteny and genome rearrangements in multiple organisms. Sinha and Meller. BMC Bioinformatics, 8:82, 2007.]
–perceptually unordered –perceptually nonlinear
–fine-grained structure visible and nameable
28 [Transfer Functions in Direct Volume Rendering: Design, Interface, Interaction. Kindlmann. SIGGRAPH 2002 Course Notes] [A Rule-based Tool for Assisting Colormap Selection. Bergman,. Rogowitz, and.
Visualization (Vis), pp. 118–125, 1995.] [Why Should Engineers Be Worried About Color? Treinish and Rogowitz 1998. http://www.research.ibm.com/people/l/lloydt/color/color.HTM]
–perceptually unordered –perceptually nonlinear
–fine-grained structure visible and nameable
–large-scale structure: fewer hues
29 [Transfer Functions in Direct Volume Rendering: Design, Interface, Interaction. Kindlmann. SIGGRAPH 2002 Course Notes] [A Rule-based Tool for Assisting Colormap Selection. Bergman,. Rogowitz, and.
Visualization (Vis), pp. 118–125, 1995.] [Why Should Engineers Be Worried About Color? Treinish and Rogowitz 1998. http://www.research.ibm.com/people/l/lloydt/color/color.HTM]
–perceptually unordered –perceptually nonlinear
–fine-grained structure visible and nameable
–large-scale structure: fewer hues –fine structure: multiple hues with monotonically increasing luminance [eg viridis R/python]
30 [Transfer Functions in Direct Volume Rendering: Design, Interface, Interaction. Kindlmann. SIGGRAPH 2002 Course Notes] [A Rule-based Tool for Assisting Colormap Selection. Bergman,. Rogowitz, and.
Visualization (Vis), pp. 118–125, 1995.] [Why Should Engineers Be Worried About Color? Treinish and Rogowitz 1998. http://www.research.ibm.com/people/l/lloydt/color/color.HTM]
31
–perceptually unordered –perceptually nonlinear
–fine-grained structure visible and nameable
–large-scale structure: fewer hues –fine structure: multiple hues with monotonically increasing luminance [eg viridis R/python] –segmented rainbows for binned
32 [Transfer Functions in Direct Volume Rendering: Design, Interface, Interaction. Kindlmann. SIGGRAPH 2002 Course Notes] [A Rule-based Tool for Assisting Colormap Selection. Bergman,. Rogowitz, and.
Visualization (Vis), pp. 118–125, 1995.] [Why Should Engineers Be Worried About Color? Treinish and Rogowitz 1998. http://www.research.ibm.com/people/l/lloydt/color/color.HTM]
33
–length accurate, 2D area ok, 3D volume poor
–nonlinear accuracy
–complex combination of lower-level primitives –many bins
–highly separable against static
–use with care to avoid irritation
Motion
Direction, Rate, Frequency, ...
Length Angle Curvature Area Volume
Size, Angle, Curvature, ... Shape Motion
34
Visualization Series, CRC Press, 2014
–Chap 10: Map Color and Other Channels
–http://www.colorbrewer2.org
Vis Course Notes, 2006. –http://www.stonesc.com/Vis06
and Applications 27:2 (2007), 14–17.
Visualization: Perception for Design, 3rd edition. Ware. Morgan Kaufmann / Academic Press, 2004.
35
–designer of Tableau color defaults –also author of A Field Guide to Digital Color –credits: following color slides excerpted from Seriously Colorful: Advanced Color Principles & Practices
36
–designer of Tableau color defaults, author of A Field Guide to Digital Color –workbook from Tableau Customer Conference 2014 talk Seriously Colorful: Advanced Color Principles & Practices
–more visual encoding practice –color palettes, univariate & bivariate –discrete (categorical) vs continuous (quantitative)
–Tableau has many built-in features to get color right, but care still needed
37
38
39
Use Given Geometry
Geographic Other Derived
Spatial Fields
Scalar Fields (one value per cell) Isocontours Direct Volume Rendering Vector and Tensor Fields (many values per cell) Flow Glyphs (local) Geometric (sparse seeds) Textures (dense seeds) Features (globally derived)
–when central task is understanding spatial relationships
–geographic geometry –table with 1 quant attribute per region
–use given geometry for area mark boundaries –sequential segmented colormap
–small regions are less visually salient
40
http://bl.ocks.org/mbostock/4060606
ben.jones#!/vizhome/PopVsFin/PopVsFin Are Maps of Financial Variables just Population Maps?
(relative) numbers
41
[ https://xkcd.com/1138 ]
–geographic geometry –scalar spatial field
–isoline geometry
specific levels of scalar values
42
Land Information New Zealand Data Service
–scalar spatial field
–isosurface geometry
specific levels of scalar values
–spatial relationships
43
[Interactive Volume Rendering
–handling spatial data –multiple data sources –paths on maps –more on handling missing data: filtering
–integrating visual encoding design choices with given spatial data
44
45