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ESRM 250/CFR 520 Autumn 2009 Phil Hurvitz

Scale Issues Use of C o l o r. ESRM 250/CFR 520 Autumn 2009 Phil Hurvitz. with thanks to J. Lawler. Overview. Scale Issues Use of Color. What is map scale?. The scale of a map is the relationship between a unit of length on the map and a unit of length on the ground. Verbal scale

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ESRM 250/CFR 520 Autumn 2009 Phil Hurvitz

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  1. Scale Issues Use of Color ESRM 250/CFR 520Autumn 2009 Phil Hurvitz with thanks to J. Lawler 1 of 46

  2. Overview • Scale Issues • Use of Color 2 of 46

  3. What is map scale? • The scale of a map is the relationship between a unit of length on the map and a unit of length on the ground. • Verbal scale • “One cm represents 100 km” • Bar scales (graphical) • Representative fraction (RF) 1:24,000 3 of 46

  4. Scale: Map to Real World • Maps have built in imprecision • A 0.025 inch line on a 1:24,000 scale map represents 50 ft on the ground • A 0.025 inch line on a 1:200,000 scale map represents almost 417 ft on the ground • What is the difference between a line on a map and a “line” on the ground (e.g., a state border) 4 of 46

  5. Scale statements • Beware of scale statements • 1:200 (“one to two hundred”): does this mean unit-to-unit • one inch on the map equals 200 inches on the ground or not unit-to-unit • e.g., one inch on the map equals 200 feet on the ground • For this course, all scale statements will be assumed unit-to-unit 5 of 46

  6. Scale: large vs. small • What is “large” and what is “small” scale? • If you have things represented on a map: • 1:2,000,000 is “small scale” • Things on the map look relatively small • 1:200 is “large scale” • Things on the map look relatively small 6 of 46

  7. Importance of Scale • Scale of data plays an important role, and frequently causes problems • All spatial phenomena have scale dependent behavior/properties • Be aware of: • Data source scale • Mixing data from different source scales • Appropriateness of output scale 7 of 46

  8. Scale Issues: data sources • Data from different sources and scales can vary widely 1:100,000 scale data from USGS digital linegraph (DLG) 8 of 46

  9. Scale Issues: data sources 1:1,000,000 scale data from Digital Chartof the World (DCW) (Defense MappingAgency) 9 of 46

  10. Scale Issues: data sources 1:2,000,000 scale data from USGS DLG 10 of 46

  11. Overview • Scale Issues • Use of Color 11 of 46

  12. Displaying data: colors • What can color do? • Label • Measure • Represent or imitate reality • Beautify Wabern 1983 From Tufte 1990 12 of 46

  13. Displaying data: colors • The colors are nice, but what’s wrong with this map? Primary home heating fuel. U.S. Department of Commerce From Tufte 1990 13 of 46

  14. Displaying data: colors From Tufte 1990 14 of 46

  15. Displaying data: colors • Smooth transition of color: gradients From Tufte 1997 15 of 46

  16. Displaying data: colors • Contrast effects From Tufte 1997 16 of 46

  17. Displaying data: colors • Contrasting colors: differences From Tufte 1997 17 of 46

  18. Color Blindness • Do you see the frog? 18 of 46

  19. Color Blindness • What do colorblind people perceive? • Do these look different to you? http://jfly.iam.u-tokyo.ac.jp/color/ 19 of 46

  20. Color Blindness • Selecting colors that colorblind people can “see” requires careful planning http://jfly.iam.u-tokyo.ac.jp/color/ 20 of 46

  21. Color Blindness http://jfly.iam.u-tokyo.ac.jp/color/ 21 of 46

  22. Color Models: RGB • “Additive” colors can be created by a mix of red, green, and blue light (what primary colors are used for pigments?) 22 of 46

  23. Color Models:CMYK • “Subtractive” colors used in the printing (pigments) process: cyan, magenta, yellow, and black. 23 of 46

  24. Redundant Coding • To increase the legibility of figures, use different line types and colors (and labels) rather than just different colors http://jfly.iam.u-tokyo.ac.jp/color/ 25 of 46

  25. Redundant Coding • Use hatching and labeling http://jfly.iam.u-tokyo.ac.jp/color/ 26 of 46

  26. Color Schemes • Different color schemes indicate different conditions Dr. Cynthia Brewer / Department of Geography / The Pennsylvania State University http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html 28 of 46

  27. Color Schemes • Binary: two classes Dr. Cynthia Brewer / Department of Geography / The Pennsylvania State University http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html 29 of 46

  28. Color Schemes • Qualitative: nominal classes Dr. Cynthia Brewer / Department of Geography / The Pennsylvania State University http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html 30 of 46

  29. Color Schemes • Qualitative: nominal classes Dr. Cynthia Brewer / Department of Geography / The Pennsylvania State University http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html 31 of 46

  30. Color Schemes • Sequential: for numeric classes Dr. Cynthia Brewer / Department of Geography / The Pennsylvania State University http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html 32 of 46

  31. Color Schemes • Sequential: for numeric classes Dr. Cynthia Brewer / Department of Geography / The Pennsylvania State University http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html 33 of 46

  32. Color Schemes • Diverging: to show contrasts Dr. Cynthia Brewer / Department of Geography / The Pennsylvania State University http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html 34 of 46

  33. Color Schemes • Diverging: to show contrasts Dr. Cynthia Brewer / Department of Geography / The Pennsylvania State University http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html 35 of 46

  34. Color Schemes • Qualitative binary: sub-class differences Dr. Cynthia Brewer / Department of Geography / The Pennsylvania State University http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html 36 of 46

  35. Color Schemes • Qualitative binary: sub-class differences Dr. Cynthia Brewer / Department of Geography / The Pennsylvania State University http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html 37 of 46

  36. Color Schemes • Qualitative sequential: numeric and nominal Dr. Cynthia Brewer / Department of Geography / The Pennsylvania State University http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html 38 of 46

  37. Color Schemes • Qualitative sequential: numeric and nominal Dr. Cynthia Brewer / Department of Geography / The Pennsylvania State University http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html 39 of 46

  38. Color Schemes • Sequential sequential: two or more numeric classes Dr. Cynthia Brewer / Department of Geography / The Pennsylvania State University http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html 40 of 46

  39. Color Schemes • Sequential sequential: two or more numeric classes Dr. Cynthia Brewer / Department of Geography / The Pennsylvania State University http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html 41 of 46

  40. Color Schemes • Diverging diverging: multiple contrasts Dr. Cynthia Brewer / Department of Geography / The Pennsylvania State University http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html 42 of 46

  41. Color Schemes • Diverging diverging: multiple contrasts Dr. Cynthia Brewer / Department of Geography / The Pennsylvania State University http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html 43 of 46

  42. The Color Brewer Dr. Cynthia Brewer / Department of Geography / The Pennsylvania State University http://www.personal.psu.edu/faculty/c/a/cab38/ColorSch/Schemes.html 44 of 46

  43. Specifying Colors in ArcMap 45 of 46

  44. Specifying Colors in ArcMap 46 of 46

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