Introduction to Geographic Information Systems Fall 2013 (INF 385T-28620) Dr. David Arctur

# Introduction to Geographic Information Systems Fall 2013 (INF 385T-28620) Dr. David Arctur

## Introduction to Geographic Information Systems Fall 2013 (INF 385T-28620) Dr. David Arctur

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##### Presentation Transcript

1. Introduction to Geographic Information Systems Fall 2013 (INF 385T-28620) Dr. David Arctur Research Fellow, Adjunct Faculty University of Texas at Austin Lecture 2 Sept 5, 2013 MapDesign

2. Outline INF385T(28620) – Fall 2013 – Lecture 2 Choropleth maps Colors Vector GIS display GIS queries Map layers and scale thresholds Hyperlinks and map tips

3. Lecture 2 Choropleth maps

4. Choropleth maps INF385T(28620) – Fall 2013 – Lecture 2 Color-coded polygon maps Use monochromatic scales or saturated colors Represent numeric values (e.g. population, number of housing units, percentage of vacancies)

5. Choropleth map example INF385T(28620) – Fall 2013 – Lecture 2 Percentage of vacant housing units by county

6. Classifying data Break points INF385T(28620) – Fall 2013 – Lecture 2 Process of placing data into groups (classes or bins) that have a similar characteristic or value • Break points • Breaks the total attribute range up into these intervals • Keep the number of intervals as small as possible (5-7) • Use a mathematical progression or formula instead of picking arbitrary values

7. Classifications INF385T(28620) – Fall 2013 – Lecture 2 • Natural breaks (Jenks) • Picks breaks that best group similar values together naturally and maximizes the differences between classes • Generally, there are relatively large jumps in value between classes and classes are uneven • Based on a subjective decision and is the best choice for combining similar values • Class ranges specific to the individual dataset, thus it is difficult to compare a map with another map

8. Classifications INF385T(28620) – Fall 2013 – Lecture 2 • Quantiles • Places the same number of data values in each class • Will never have empty classes or classes with too few or too many values • Attractive in that this method produces distinct map patterns • Analysts use because they provide information about the shape of the distribution. • Example: 0–25%, 25%–50%, 50%–75%,75%–100%

9. Classifications INF385T(28620) – Fall 2013 – Lecture 2 • Equal intervals • Divides a set of attribute values into groups that contain an equal range of values • Best communicates with continuous set of data • Easy to accomplish and read • Not good for clustered data • Produces map with many features in one or two classes and some classes with no features

10. Classifications INF385T(28620) – Fall 2013 – Lecture 2 Use mathematical formulas when possible. • Exponential scales • Popular method of increasing intervals • Use break values that are powers such as 2n or 3n • Generally start out with zero as an additional class if that value appears in your data • Example: 0, 1–2, 3–4, 5–8, 9–16, and so forth

11. Classifications INF385T(28620) – Fall 2013 – Lecture 2 Use mathematical formulas when possible • Increasing interval widths • Long-tailed distributions • Data distributions deviate from a bell-shaped curve and most often are skewed to the right with the right tail elongated • Example: Keep doubling the interval of each category, 0–5, 5–15, 15–35, 35–75 have interval widths of 5, 10, 20, and 40.

12. Original map (natural breaks) U.S. population by state, 2000 INF385T(28620) – Fall 2013 – Lecture 2

13. Equal interval scale INF385T(28620) – Fall 2013 – Lecture 2 Not good because too many values fall into low classes

14. Quantile scale INF385T(28620) – Fall 2013 – Lecture 2 Shows that an increasing width (geometric) scale is needed

15. Custom geometric scale INF385T(28620) – Fall 2013 – Lecture 2 Experiment with exponential scales with powers of 2 or 3.

16. Beware empty statisticshttp://xkcd.com/1138 INF385T(28620) – Fall 2013 – Lecture 2

17. Normalizing data Divides one numeric attribute by another in order to minimize differences in values based on the size of areas or number of features in each area Examples: • Dividing the number of vacant housing units by the total number of housing units yields the percentage of vacant units • Dividing the population by area of the feature yields a population density INF385T(28620) – Fall 2013 – Lecture 2

18. Nonnormalized data INF385T(28620) – Fall 2013 – Lecture 2 Number of vacant housing units by state, 2000

19. Normalized data INF385T(28620) – Fall 2013 – Lecture 2 Percentage vacant housing units by state, 2000

20. Nonnormalized data California population by county, 2007 INF385T(28620) – Fall 2013 – Lecture 2

21. Normalized data California population density, 2007 INF385T(28620) – Fall 2013 – Lecture 2

22. Lecture 2 colors INF385T(28620) – Fall 2013 – Lecture 2

23. Color overview • Hue is the basic color • Value is the amount of white or black in the color • Saturation refers to a color scale that ranges from a pure hue to gray or black INF385T(28620) – Fall 2013 – Lecture 2

24. Color wheel Device that provides guidance in choosing colors • Use opposite colors to differentiate graphic features • Three or four colors equally spaced around the wheel are good choices for differentiating graphic features • Use adjacent colors for harmony, such as blue, blue green, and green or red, red orange, and orange INF385T(28620) – Fall 2013 – Lecture 2

25. Light vs. dark colors • Light colors associated with low values • Dark colors associated with high values • Human eye is drawn to dark colors INF385T(28620) – Fall 2013 – Lecture 2

26. Contrast The greater the difference in value between an object and its background, the greater the contrast INF385T(28620) – Fall 2013 – Lecture 2

27. Monochromatic color scale INF385T(28620) – Fall 2013 – Lecture 2 • Series of colors of the same hue with color value varied from low to high • Common for choropleth maps • The darker the color in a monochromatic scale, the more important the graphic feature • Use more light shades of a hue than dark shades in monochromatic scales • The human eye can better differentiate among light shades than dark shades

28. Monochromatic map INF385T(28620) – Fall 2013 – Lecture 2 Values too similar

29. Monochromatic map INF385T(28620) – Fall 2013 – Lecture 2 A better map, more contrast

30. Dichromatic color scale • An exception to the typical monochromatic scale used in most choropleth maps • Two monochromatic scales joined together with a low color value in the center, with color value increasing toward both ends • Uses a natural middle point of a scale, such as 0 for some quantities (profits and losses, increases and decreases) INF385T(28620) – Fall 2013 – Lecture 2

31. Dichromatic map • Symmetric break points centered on 0 make it easy to interpret the map INF385T(28620) – Fall 2013 – Lecture 2

32. Color tips INF385T(28620) – Fall 2013 – Lecture 2 • Colors have meaning • Political and cultural • Cool colors • Calming • Appear smaller • Recede • Warm colors • Exciting • Overpower cool colors

33. Color tips • Do not use all of the colors of the color spectrum, as seen from a prism or in a rainbow, for color coding • If you have relatively few points in a point layer, or if a user will normally be zoomed in to view parts of your map, use size instead of color value to symbolize a numeric attribute INF385T(28620) – Fall 2013 – Lecture 2

34. Graphics for colorblind users Two-meter air temperature anomalies (i.e., differences from the 1971–2000 mean) for January 1998 (during a recent El Niño): INF385T(28620) – Fall 2013 – Lecture 2

35. Graphics for colorblind users Two-meter air temperature anomalies (i.e., differences from the 1971–2000 mean) for January 1998 (during a recent El Niño): INF385T(28620) – Fall 2013 – Lecture 2

36. Lecture 2 Vector & Raster Data INF385T(28620) – Fall 2013 – Lecture 2

37. Points, lines, polygons INF385T(28620) – Fall 2013 – Lecture 2 • Point • x,y coordinates • Line • starting and ending point and may have additional shape vertices (points) • Polygon • three or more lines joined to form a closed area

38. Feature attribute tables INF385T(28620) – Fall 2013 – Lecture 2 Store characteristics for vector features Layers can be displayed using attributes

39. Displaying points INF385T(28620) – Fall 2013 – Lecture 2 Single symbols All CAD calls

40. Displaying points INF385T(28620) – Fall 2013 – Lecture 2 Same features, different points Based on attributes

41. Displaying points INF385T(28620) – Fall 2013 – Lecture 2 Industry specific (e.g. crime analysis) Good for large scale (zoomed in) maps

42. Displaying points INF385T(28620) – Fall 2013 – Lecture 2 • Industry specific (e.g. schools) • Not good for multiple features at smaller scales • Simple points better for analysis

43. Displaying points INF385T(28620) – Fall 2013 – Lecture 2 • Quantities • Use exaggerated sizes

44. Displaying lines INF385T(28620) – Fall 2013 – Lecture 2 For analytical maps, most lines are ground features and should be light shades (e.g. gray or light brown)

45. Displaying lines INF385T(28620) – Fall 2013 – Lecture 2 Consider using dashed lines to signify less important line features and solid lines for the important ones

46. Displaying polygons INF385T(28620) – Fall 2013 – Lecture 2 Consider using no outline or dark gray for boundaries of most polygons • Dark gray makes the polygons prominent enough, but not so much that they compete for attention with more important graphic features

47. Displaying polygons INF385T(28620) – Fall 2013 – Lecture 2 Consider using texture for black and white copies

48. Graphic hierarchy • Assign bright colors (red, orange, yellow, green, blue) to important graphic elements • Features are known as figure All features in figure INF385T(28620) – Fall 2013 – Lecture 2

49. Graphic hierarchy • Assign drab colors to the graphic elements that provide orientation or context, especially shades of gray • Features known as ground Circles in figure, squares and lines in ground INF385T(28620) – Fall 2013 – Lecture 2

50. Graphic hierarchy • Place a strong boundary, such as a heavy black line, around polygons that are important to increase figure • Use a coarse, heavy cross-hatch or pattern to make some polygons important, placing them in figure INF385T(28620) – Fall 2013 – Lecture 2