Cartographic Design

1. Cartographic Design • Data type and symbolization • spatial types - point, line, polygon • value types - nominal, ordinal, interval, ratio • visual variables -- using color, value, shape, size, orientation, and texture to create symbols • Cartographic Design principles • map scale • legibility • text size • density • complexity • contrast • Using labels and fonts

2. Cartographic Design • Symbolizing Thematic Data • Normalization • Classification schemes

3. Cartographic design is the process of using symbols to take some part of the real world and simplify it to make it more comprehensible.

4. Data Types • Point • Line • Polygon

5. Value Types • Nominal • Ordinal • Interval • Ratio Data without values Data with values

6. Nominal Values Data values do not represent any quantity or ordered relationship.

7. Ordinal Values Data values represent an ordered relationship, but there is no numerical information.

8. Interval and Ratio Values Interval data are numbers measured on a scale that does not have an absolute zero value and cannot be compared as ratios. Ratio data are numbers that are based on a meaningful zero. Both of these are difficult to map if you do not generalize them back to ordinal or nominal groups. Interval Data – You may add 10 or 20 degrees Celsius to a temperature, but 30 degrees is not three times as warm as 10 degrees.

9. Interval and Ratio Values It is very difficult to scale map symbols to portray interval and ratio data in an easily readable fashion. Scaling point sizes to the data value results in a 24-point dot that is 16 times larger than the 6-point dot. But scaling the area of the dots to the values results in sizes that are difficult to distinguish, even though the “24” dot really covers 4 times the area of the “6” dot. The third scale shows a compromise, which is difficult to come up with.

10. Visual Variables • Hue (color) • Value (darkness) • Shape • Size • Orientation • Spacing (texture)

11. Hue and Value

12. Shape and Size

13. Orientation and Spacing

14. Map Scale

15. Legibility • Text size • Density • Complexity • Contrast • Emphasis • Visual Balance

16. Density/Text Size

17. Density/Text Size

18. Density/Text Size

19. Complexity Complexity can change depending on the data you want to show. These two maps show the same data, but distributed differently among the counties. A simple distribution lets you use more symbols. In a complex distribution, you can’t see the difference between similar values.

20. Contrast – Color/Value

21. Contrast – Line Weight

22. Contrast – Subject Emphasis

23. Contrast – Symbol Discrimination

24. Contrast – Symbol Discrimination

25. Visual Balance

26. Labels • Water features are blue, italic • Never run a label upside-down

27. Labels • Avoid angled, straight line text • Don’t use too many fonts

28. Labels • Use a text mask to cover up lines that can’t be avoided. • Use upper- and lower-case text rather than all capitals.

29. Normalized Data Think of other variables that influence the data you want to show. Population – some counties have a small population because they cover a small area. Population Density -- Shows which areas really have a larger concentration of people

30. The data on this slide are NOT normalized. The distributions all look the same because they all depend on the population. Normalized Data Population Number of people less than 5 years old Number disabled people

31. The data on this slide are normalized. They look very different than the same data from the previous slide. Normalized Data Population Density Percent of population less than 5 years old Percent of population disabled

32. Classification Schemes Natural Breaks Quantile – each class has same number of members

33. Classification Schemes Standard Deviation Equal Interval

34. Grid North vs True North North isn’t always straight up on a map, but you can make it so.

35. North is Which Direction?

36. North is Which Direction?

37. North is Which Direction?

38. Python Script for True North # Rotate Data Frame so that True North is straight up. import arcpy, os, math # Find the current map view and get its extent mxd = arcpy.mapping.MapDocument("current") df = mxd.activeDataFrame # Figure the latitude and longitude of the top center of the map mapRef = df.spatialReference geoRef = arcpy.SpatialReference(4269) ptTopY = df.extent.YMax ptTopX = (df.extent.XMin + df.extent.XMax)/2 ptTop = arcpy.PointGeometry(arcpy.Point(ptTopX, ptTopY), mapRef) # Project to geographic coordinates ptTop = ptTop.projectAs(geoRef) # Find the projected map coordinate 0.5 degrees of latitude south of the top center ptBtmX = ptTop.centroid.X ptBtmY = ptTop.centroid.Y - 0.5 ptBtm = arcpy.PointGeometry(arcpy.Point(ptBtmX, ptBtmY), geoRef) # Project back to map coordinates ptTop = ptTop.projectAs(mapRef) ptBtm = ptBtm.projectAs(mapRef) # Figure the angle between the top of the map and the point 0.5 degrees south of it dX = ptBtm.centroid.X - ptTop.centroid.X dY = ptBtm.centroid.Y - ptTop.centroid.Y rAngle = math.atan(dX / dY) * 180 / 3.1415926 print rAngle # Rotate the map to make north straight up and down df.rotation = rAngle # Refresh arcpy.RefreshActiveView()

39. North is Which Direction?

40. Map Projections

41. Map Projections

42. Web Mercator Projection

43. Web Mercator Projection

44. Web Mercator Projection Web Mercator – North end of Montana looks pulled apart to me. State Plane – Meridians correctly converge towards the North Pole