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

Classifying Features Qualitative Quantitative

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)

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.

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.

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

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

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.

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

Classifying the data based upon attribute value

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%)

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.

Classifying the data based upon attribute value Default

Histograms