Lecture 2 Map design. Dr. Zhang Spring, 2017

Transcription

1 Lecture 2 Map design Dr. Zhang Spring, 2017

2 Model of the course Using and making maps Navigating GIS maps Map design Working with spatial data Geoprocessing Spatial data infrastructure Digitizing File geodatabases Geocoding Interactive maps Map Animations Map layouts Spatial analysis 3D GIS Proximity analysis Raster analysis Analyzing Spatial data Network analysis Data mining Spatial regression

3 Outline Graphic design principles Color Symbolizing points Symbolizing lines Symbolizing polygons GIS queries 3

4 Lecture 2 GRAPHIC DESIGN PRINCIPLES

5 Light vs. dark colors High color value (dark color) is perceived as more important 5

6 Contrast The greater the difference in value between an object and its background, the greater the contrast. Keep the background light and use lots of contrast for important features! 6

7 Bad map: Not enough contrast Contrast is needed to distinguish features 7

8 Good map: better contrast 8

9 Graphic hierarchy Assign bright colors (red, orange, yellow, green, blue) to important graphic elements (features) Important features are known as figure All features in figure 9

10 Graphic hierarchy Assign drab colors to the graphic elements that provide orientation or context Contextual features known as ground Circles in figure, squares and lines in ground 10

11 Bad and good maps 11

12 Graphic hierarchy Place a strong boundary, such as a heavy black line, around points or polygons that are important to increase figure Use a coarse, heavy cross-hatch or pattern to make some polygons important, placing them in figure 12

13 Overarching principle Due to John Tukey, author of a classic series of books on graphic design: Minimize ink! Use lots of white space and make every pixel count Elements you can and should delete are chart junk 13

14 Bad map: chart junk 14

15 Good map: chart junk gone 15

16 Lecture 2 COLOR 16

17 Terms Hue is the basic color Value is the amount of black in the color, here seen in saturated color ramps (ranging from a pure hue to gray or black) 17

18 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 18

19 Monochromatic color scale Series of colors of the same hue with color value varied from low to high (or vice versa) 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 19

20 Dichromatic color scale Used for attributes that have a natural middle such as 0 Examples: regression residuals, increases and decreases, etc. Two monochromatic scales joined together with a low color value in the center, with color value increasing toward both ends 20

21 Dichromatic map Symmetric break points centered on 0 make it easy to interpret the map 21

22 Diochromatic map example 22

23 Color tips Colors have meaning Political and cultural Cool colors Calming Appear smaller Recede Warm colors Exciting Overpower cool colors 23

24 Learn more about GIS colors Website Books Brewer, Cynthia A Designed Maps: A Sourcebook for GIS Users. Redlands: ESRI Press Brewer, Cynthia A Designing Better Maps: A Guide for GIS Users. Redlands: ESRI Press 24

25 Lecture 2 SYMBOLIZING POINTS 25

26 Undifferentiated points 26

27 Unique-values symbolization Differentiated points, based on code attribute. Use shape and color. 27

28 Size-graduated point markers For magnitude at points Use exaggerated size differences 28

29 Industry-specific point markers Not good for multiple features at smaller scales Simple points better for analysis 29

30 Industry-specific point markers Good for large scale (zoomed in) maps 30

31 Lecture 2 SYMBOLIZING LINES 31

32 Displaying lines For analytical maps, most lines are ground features and should be light shades (e.g. gray or light brown) 32

33 Displaying lines Consider using dashed lines to signify less important line features and solid lines for the important ones Use industry standards 33

34 Displaying polygons 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 34

35 Lecture 2 SYMBOLIZING POLYGONS

36 Unique values Use code values for symbolization 36

37 Choropleth maps Color-coded polygon maps Represent numeric attributes (e.g., population, number of housing units, percentage of vacant housing units) 37

38 Bad map, good map: monochromatic 38

39 What kind of data to plot? Depends on purpose Population (or population segment) to study demand for goods/services Population density (e.g., persons/sq mile) for behavior (such as contagion) Normalized population segment (population segment/total population) to study composition or behavior 39

40 Population: choropleth map 40

41 Population: graduated point markers 41

42 Fishnet 42

43 Population density 43

44 Population dot density 44

45 Population segment 45

46 Normalized population segment 46

47 Numeric scales Process of placing data into groups (classes or bins) defined by break points Break points Are right sides of intervals Keep the number of intervals small (3-7) Use a mathematical progression or formula instead of arbitrary values CensusTracts VAC_UNITS / TOTUNITS 0% % 5.02% % 10.08% % 16.12% % 24.18% % Break points 47

48 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 Analysts use quantiles a lot Because they provide information about the shape of the distribution Almost always the first scale that I use for a new map 48

49 Quantile example Shows that an increasing width (geometric) scale is needed 49

50 Increasing width scales Data distributions often deviate from a bellshaped curve and most often are skewed to the right with the right tail elongated (long-tailed distributions) Alternative 1: Keep doubling the interval of each category, 0 5, 5 15, 15 35, have interval widths of 5, 10, 20, and 40. Alternative 2: Exponential/geometric break points that are powers such as 2 n or 3 n times 10 to an integer power. Can start with zero as an additional class if that value appears in the data 0, 1 2, 3 4, 5 8, 9 16, and so forth 50

51 Custom geometric scale Powers of 2 51

52 Equal intervals Easiest to understand Best to use familiar interval widths that are 1, 2, or 5 times 10 to a power but have to implement manually Example interval widths: 100, 200, 300, etc. Not good for highly-peaked or skewed data distributions 52

53 Equal interval example Not good here because too many tracts fall into low classes 53

54 Natural breaks (Jenks) May be useful for exploratory work Picks breaks points using a clustering method: maximizes the differences between classes and minimizes differences within classes Generally, there are relatively large jumps in value between classes and class intervals are variable in width Class ranges are specific to the individual dataset, thus it is difficult to compare maps 54

55 Original maps (natural breaks) 55

56 New maps (same classes) 56

57 Lecture 2 GIS QUERIES 57

58 GIS queries Powerful relationship between data table and vector-based graphics unique to GIS Records from a feature attribute table are selected by using query criteria Query will automatically highlight the corresponding graphic features 58

59 Simple attribute queries Simple query criterion <data attribute>< logical operator><value> NatureCode ='DRUGS' DATE >= ' ' % wild card % symbol stands for zero, one, or more characters of any kind NAME like ' BUR%' Selects any crime with names starting with the letters BUR, including burglaries (BUR), business burglaries(burbus), and residential burglaries (BURRES) 59

60 Simple attribute queries 60

61 Compound attribute queries Compound query criteria Combine two or more simple queries with the logical connectives AND or OR "NATURE_COD" = 'DRUGS' AND "DATE" > Selects records that satisfy both criteria simultaneously Result are drug crimes that were committed after August 1,

62 Compound attribute queries 62

63 Summary Graphic design principles Color Symbolizing points Symbolizing lines Symbolizing polygons GIS queries 63

64 Labs and Assignments Lab: 2-1 to 2-8 Assignments: 2-1 Due: Feb 13,