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# Ways to map quantitative data

Release 9.2

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Quantitative data is data that describes features in terms of a quantitative value measuring some magnitude of the feature. Unlike categorical data, whose features are described by a unique attribute value, such as a name (see Drawing features to show categories), quantitative data generally describes counts or amounts, ratios, or ranked values. For example, data representing precipitation, population, and habitat suitability can all be mapped quantitatively.

Which quantitative value should you map?

Knowing what type of data you have and what you want to show will help you determine what quantitative value to map. In general, you can follow these guidelines:

• Map counts or amounts if you want to see actual measured values as well as relative magnitude. Use care when mapping counts, because the values may be influenced by other factors and could yield a misleading map. For example, when making a map showing the total sales figures of a product by state, the total sales figure is likely to reflect the differences in population among the states.
• Map ratios if you want to minimize differences based on the size of areas or number of features in each area. Ratios are created by dividing two data values. Using ratios is also referred to as normalizing the data. For example, dividing the 18- to 30-year-old population by the total population yields the percentage of people aged 18–30. Similarly, dividing a value by the area of the feature yields a value per unit area, or density.
• Map ranks if you are interested in relative measures and actual values are not important. For example, you may know a feature with a rank of 3 is higher than one ranked 2 and lower than one ranked 4, but you can't tell how much higher or lower.

Should you map individual values or group them in classes?

When you map quantitative data, you can either assign each value its own symbol or you can group values into classes using a different symbol for each class.
If you are only mapping a few values (less than 10), you can assign a unique symbol to each value. This may present a more accurate picture of the data since you are not predetermining which features are grouped together. More likely, your data values will be too numerous to map individually and you'll want to group them in classes, or classify the data. A good example of classified data is a temperature map in a newspaper. Instead of displaying individual temperatures, these maps show temperature bands, where each band represents a given range in temperature.

Learn how to set a classification for your data