1. Symbolizing and Classifying How to improve your displayed data. ?

2. Simple Feature – Default Display

3. Your geodatasets can have numerous fields of information. For example, this dataset for the contiguous U.S. has 48 fields. The question is, how do you classify the data for display?

5. Qualitative Data Categorical Data –Name e.g. Southwest, West –Type e.g. concrete, asphalt, gravel – Condition e.g. good, fair, poor –A numeric ID e.g. 3427, 3433, 2700 Grouped measurements –e.g. 0-25, 26-50, 51-75 ……. Unique Values –e.g. Texas, Oklahoma, California

7. Unique Value using state names

8. Unique Value using Sub-region

9. Unique Values using many fields

11. Quantitative Data: How much of something is there? Working with numeric values. Counts or amounts e.g. total sales Ratios e.g. Population / area Ranked or ordinal data e.g. 2 nd, 3 rd, 4 th

13. Classification Methods for Quantitative Data: How do you group data? Natural Breaks (Jenks – default) Equal Interval Quantile Standard Deviation

15. Natural Breaks – Obvious differences

16. Changing Classifications

17. Equal Interval Equal range of values regardless of the number of features per class.

19. Equal Interval Notice that the values are grouped into equal ranges. There are not an equal Number of states per class.

20. Quantile

22. Quantile An equal number of features regardless of the range of values There are 5 states in each category – the top most populated, the next 5 most populated ….. The 5 least populated states.

23. Standard Deviation Great for population and income data

25. Standard Deviation 2000 Population Data

26. Once you have classified the data, you can change the colors, symbols, and or the fill patterns as much as you please. Experiment with colors and patterns. We will discuss this in a future class.