There are many ways to style a data layer by a numeric field. For example, a Continuous style maps a color (or point size) for every unique value in your data, similarly to a categorical visualization. In this case, the color for each value is interpolated from the endpoint colors.
However, it’s more common to group values from numeric fields into classes (green from 0 to 5, yellow from 6 to 10, etc). Classes can be chosen manually or picked from one of Felt’s default classification methods:
- Equal Intervals: classes are split by an interval of constant value (0, 5, 10, 15…). Useful for evenly distributed data. Equal interval classifications are very sensible to outliers - very small or large values can lead to the majority of values to be grouped into just one or two classes. This classification method follows an Arithmetic Series, where the value of each breakpoint is equal to the previous value plus a constant value.
- Quantiles: contrary to equal intervals, the quantiles classification ensures that every class contains the same number of values. This can look nice from a visual standpoint, but often quantiles classifications can be misleading, since the size of each class may vary widely. They work well for evenly distributed datasets.
