Displaying Data Quantitative vs. Qualitative Data Symbolizing Features: Qualitative Symbolizing Features: Quantitative Qualitative Symbology Different features get different symbols or colors, it is not dependent on a quantitative value. Limit to 10 unique values 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 dark the color The higher the value. Up to 5 or 6 shades of one color maximum. Quantitative Symbology Graduate 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. Normalizing Data Quantitative symbology can be normalized based upon the attributes of another field. For example population normalized by area Sales per state normalized by population. Normalizing data Displaying multiple attributes Symbolize features based on more than one attribute. Street type and traffic volume. Parcel land use and value. Quantitative Symbology Dot Density Good for showing areas of low and high concentrations Classifying the data based upon attribute value Default 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%) Classifying the data based upon attribute value 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. (crops which area perform better than average, which perform worse than average) Manual – defined by the user. Histograms