A Tutorial on Color Symbolization and Data Classification for Mapping and Visualization

Transcription

1 A Tutorial on Color Symbolization and Data Classification for Mapping and Visualization Cynthia Brewer, Penn State Geography Prepared for STIS conference sponsored by BioMedware, January 9-10, 2003, in Ann Arbor

2 Color Symbolization

3 with Mark Harrower Digital Government Quality Graphics NSF Grant No

4 Full view - solution

5 Color scheme types Sequential: light-to-dark; low-to-high data Diverging: dark-light-dark, two hues; emphasize critical midrange in ordered data Qualitative: different hues, similar lightness; categorical data

6 From Mapping Census 2000 atlas Example maps Sequential Diverging Qualitative

7 Use perceptual system Figure from such as Munsell (HVC)

8 Break for Munsell chip organization task to be sure you understand the perceptual dimensions of color

9 Hue and lightness, sequential schemes from ColorBrewer

10 Sequential scheme paths hue and value graph

11 Sequential paths collapsed

12 Example chroma & value paths for sequential schemes

13 Diverging paths collapsed

14 Example diverging schemes

15 Qualitative schemes

16 Summary Each scheme type has a characteristic path through perceptual color space

17 Classification

18 Classification for map comparison is key issue in multi-map contexts

19 Stroke, White Male Time Series Matched legends

20 Classification literature Brewer & Pickle (Dec Annals of AAG) Reviews: Jenks, Coulson, Evans, Paslawski, Slocum On Comparison (most in 70s): Monmonier, Lloyd & Steinke, Olson, Muller 1990s: Cromley

21 Experiment Classification 7 map series, 6 questions each, 58 subjects Matched Legends 2 map series one with matched legends, 48 subjects Questions about polygons, regions, whole maps; Within map and comparison questions

22 Classification types Tested: - Quantile (percentile) - Min. boundary error - Jenks optimization, Natural breaks - Equal interval with class for extremes -Mean andst. deviation -Shared area - Box plot based Others: - Arithmetic, Geometric -Nested means - Significance based - Equal area - Min. difference from class midpoints or medians - Critical values

23 Map series Lung cancer for WM, WF, BM, BF HIV, unintentional injuries for WM, BM All causes, heart, cancer, stroke (WF) Motor vehicle, suicide, homicide (WM) and % urban Breast cancer (WF), income, education, urban Heart disease, 4 time periods (WM) Stroke, lung cancer, 2 time periods each (WM) Liver disease, COPD for WM, WF Stroke, 4 time periods (WM)

24 Quantile example map Stroke, White Female

25 Hybrid equal interval example Stroke, White Female

26 Quantile data classing

27 Hybrid equal interval

28 Results Graph

29 Conclusions Classifications suited for choropleth maps in series intended for a wide range of map reading tasks: Quantile Minimum boundary error Natural breaks (Jenks)

30 Use same legends Matched legends aid map comparison 28% improvement!

31 Difficult tasks - interpreting broader map patterns - comparing patterns between maps - questions requiring map legend reading

32 Classing strategies for series - share class breaks between maps -use meaningful breaks: national rate, median, zero, threshold - round aggressively

33 One race or One or more including Two change maps

34 Example comparison One race, AIAN One or more AIAN 26.4% 110.3%

35 Classing series - class aggregate of all data for series - use many classes/colors - map with subset of classes for each map - limit to true max and min within each map

36 Classing series examples Same colors each map? OR?

37 Same map pair with and without STIS matched 2003 / BioMedware legends

38 Color and classes reveal your data; use them smartly