1. Quantitative vs. Qualitative Data
Displaying Data
Quantitative vs. Qualitative Data

2. Classifying Features
Qualitative
Quantitative

3. Qualitative Symbology
Different features get different symbols or colors, it is not dependent on a quantitative (numerical) value. Limit to 10 unique values (i.e. colors) generally.
Same symbol
Unique symbol (value)

4. Quantitative Symbology
Uses values, or quantities, contained in numeric attribute fields
Graduated colors:
The color varies with the
numeric attribute value,
Usually the darker the color
the higher the value. Limit
the number to 6 shades of one color.

5. Quantitative Symbology
Graduated Symbols
The symbol size varies with
the attribute value.
Usually the larger the symbol the greater the value.
This shows relative values,
not absolute values.

6. Quantitative Symbology
Proportional symbols
Symbols vary in size
to show exact values.

7. normalized by population
Normalizing Data
Quantitative symbology can be normalized based upon the attributes of another field.
Sales per state
normalized by population
Population normalized by area

8. Displaying multiple attributes
Symbolize features based on more than one attribute.
Street type and traffic volume.
Parcel land use and value.
This is difficult to do effectively.

9. Quantitative Symbology
Dot Density
Good for showing
areas of low and high concentrations.
Can only use for
values associated
with polygon
features.

10. Classifying the data based upon attribute value

11. Classification places attribute values into groups
Natural breaks (default) – the gaps in the data are identified and the intervals are assigned accordingly. Shows clusters or concentrations of values.
Equal interval – range of values is equal within each class (61-70, 71-80, 81-90, etc.) Bands of temperature.
Quantile – the number of features with in each class are the same (top 25%)

12. Classification places attribute values into groups
Standard Deviation- the range of values with each class is based on the standard deviation of the data. Shows the amount the attribute value varies from the mean. (e.g. crops - which areas perform better than average? which perform worse than average?)
Manual – defined by the user.

13. Classifying the data based upon attribute value
Default

14. Histograms