1. Data Representation and Mapping
Ming-Chun Lee

2. What is a map?
A miniaturized and convenient representation of spatial reality
A picture or diagram, usually two- dimensional, showing all or part of the Earth and is a device for transferring selected information about the mapped area to the map viewer

3. Types of Maps
Reference Maps - Used to emphasize the location of spatial phenomenon
USGS topographic maps
Road maps
Thematic maps - used to display the spatial pattern of a particular theme
Maps of population in the United States
Geological maps

4. Characteristics of Maps
All maps are reductions of reality
All maps portray data which has been generalized, classified, and simplified
All maps use symbols to designate elements of reality. Data are portrayed by the use of various marks, such as dots, lines, patterns, and colors which are referred in a legend

5. Map Elements

6. Data classification and scaling
Nominal Scaling
Data are differentiated by qualitative or intrinsic differences between features, without a quantitative relationship. The nominal scale locates and names items and places them in exclusive categories.
Ordinal Scaling
The data are ranked based on some quantitative measurement. They are only ranked from lowest to highest, without defining their numerical value.
Interval/Ratio Scaling
Scaling adds the dimension of distance between the ranked data by employing some standard units. Scaling adds magnitude to the ranks.
Interval scaling starts at some arbitrary point, such as 32°F,
Ratio scaling begins with zero.

7. Nominal Data Examples

8. Ordinal Data Examples

9. Interval/Ratio Data Examples

10. The range of visual resources
As cartographers reduce the world to points, lines, and areas, they use a variety of visual resources. Jacques Bertin in his book The Semiology of Graphics (1983), inventories these resources using the categories of size, shape, value, texture or pattern, hue, orientation, and shape.

11. To increase the legibility of figures, use different line types and colors (and labels) rather than just different colors

12. Use hatching and labeling

13. Qualitative: nominal classes
Dr. Cynthia Brewer / Department of Geography / The Pennsylvania State University

14. Sequential: for numeric classes
Dr. Cynthia Brewer / Department of Geography / The Pennsylvania State University

15. Qualitative sequential: numeric and nominal
Dr. Cynthia Brewer / Department of Geography / The Pennsylvania State University

16. Interval/Ratio Data

17. colors are nice, but what’s wrong with this map?
Primary home heating fuel. U.S. Department of Commerce

18. Vector Symbology: Discrete Attribute Data
Single Symbol
All Features Look the Same
Categories
Unique Values
Using One Attribute, Set a Distinct Symbol for Each Value
Unique Values, Many Fields
Using Several Attributes, Set a Distinct Symbol for Each Combination
Match to Symbols In a Style
Use Preset Symbology Based on an Attribute

19. Vector Symbology: Discrete Attribute Data
Unique Values: Arterial Class
Unique Values, Many Fields: Arterial Class & Bike Class
Single Symbol

20. Vector Symbology: Continuous Attribute Data
Quantities
Graduated Colors
Maps Colors Along a Gradient to Discrete, Ordered Ranges of Attribute Values
Graduated Symbols
Maps Sizes of Symbols to Discrete, Ordered Ranges of Attribute Values
Proportional Symbols
Continuously Varies Symbol Sizes by Attribute Values

21. Vector Symbology: Continuous Attribute Data
Graduated Colors
Graduated Symbol Sizes
Proportional Symbol Sizes

22. Vector Symbology: Charts
Pie
Bar/Column
Stacked

23. Symbology: Raster Layers
Views of Radio Towers
Unique Values
Discrete Data  Discrete Colors
Classified
Continuous Data  Discrete Colors
Stretched
Continuous Data  Continuous Colors
Aspect
Slope

24. Map Layout Layout View

25. Map Layout: Data Frames
Layout View Toolbar
Data Frame Object
Extents of GIS Data
Data Frame Edge
Paper Margin
Paper Edge

26. Map Layout: Auxiliary Elements

27. Map Layout: Multiple Data Frames

28. Map Layout: Full Map Layout
Title
North Arrow
Scale
Body
Landmark Text (opt.)
Legend
Overview (opt.)

29. Normalization Set Value to One Attribute
Set Normalization to Another Attribute
ArcMap Calculates Color Based On:
Displayed Value = Value / Normalization
Examples:
Density: Value / Area
Proportional Growth: New Value / Old Value
Proportional Population: Subgroup Population / Total Population

30. Total Population by Nation

31. Total Population by Nation

32. Normalization for Population Density

33. World Population Density by Nation

34. Total Population by County

35. Total Population by County

36. Normalization for Population Growth

37. US Population Growth by County

38. Classification How are Continuous Data Categorized in Symbology?
Classification Methods
Equal Interval/Defined Interval
Place Breaks at Equal Intervals, Specifying Number or Width of Breaks
Standard Deviation
Place Breaks at Equal Standard Deviations From the Mean Value
Quantile
Place Breaks Such That Groups Have Equal Size Memberships
Natural Breaks
Place Breaks Between Clusters of Data
Manual Breaks

39. Ozone Levels in California

40. Histogram x-Axis: y-Axis: Bars:
The full range of data values, classified into narrow range categories
y-Axis:
Number of features/cells, or frequency
Bars:
The number of features in each narrow range category

41. Equal Interval/Defined Interval
Lecture 12

42. Equal Interval/Defined Interval
Guarantees a linear relationship between the data values and the color selected

43. Standard Deviation
Lecture 12

44. Standard Deviation
Similar to Equal Interval, but uses a statistical basis for determining the interval size

45. Quantile
Lecture 12

46. Quantile
Guarantees that each color will be assigned to approximately the same number of features
Effectively divides your data into equally-sized groups
Results in Greatest Overall Differentiation

47. Natural Breaks
Lecture 12

48. Natural Breaks Uses an algorithm to place breaks such that:
The variance within groups is minimized, and
The variance between groups is maximized
Results will tend to be irregularly-sized intervals
This is the default in ArcMap

49. Manual Breaks
Lecture 12

50. Manual Breaks
Can reflect policy-based or arbitrary thresholds and categories
Tedious to set up

51. Classifications: When to Use
Equal Interval
You want to be able to compare relative values using the colors
Standard Deviation
You expect your data to be normally distributed
Quantile
You want your breaks to be narrower in clusters of data
Natural Breaks
You want to use your data to identify clusters of values
Manual Breaks
You have an external source for setting breaks