> For the complete documentation index, see [llms.txt](https://developers.felt.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://developers.felt.com/felt-style-language/classification-methods.md).

# Classification methods

[Numeric](/felt-style-language/types-of-visualizations/numeric-visualizations-color-and-size.md), [H3](/felt-style-language/types-of-visualizations/h3.md), and [raster](/felt-style-language/types-of-visualizations/raster.md) (numeric and tinted hillshade) visualizations all turn a range of values into discrete classes (or a smooth gradient) using the `steps` field in the `config` block. The classification method decides where the breaks between classes fall.

## Methods

| Method            | What it does                                                   | Best for                                                           |
| ----------------- | -------------------------------------------------------------- | ------------------------------------------------------------------ |
| `continuous`      | Smooth gradient, no discrete classes                           | Proportional symbols, smooth color ramps                           |
| `jenks`           | Natural breaks — minimizes variance within each class          | Most cases; a good starting point                                  |
| `quantiles`       | Equal number of features in each class                         | Balanced representation, evenly distributed data                   |
| `equal-intervals` | Equal-sized value bins                                         | When the value ranges themselves matter                            |
| `stddev`          | Classes in 1σ steps with the middle class centered on the mean | Data with a near-normal distribution                               |
| `geo-intervals`   | Break points in a geometric progression between min and max    | Multiplicative, strongly skewed data (values must be non-negative) |

All shortcut methods except `continuous` require a `count`. Raster algebra visualizations support only `continuous` and `equal-intervals`.

## Specifying `steps`

There are two ways to define `steps`.

**Shortcut (recommended when you don't know the data's distribution).** Name a method and a class count and let Felt compute the break points from the data:

```json
{
  "config": {
    "numericAttribute": "population_density",
    "steps": {"type": "jenks", "count": 5}
  }
}
```

For a smooth gradient, use the continuous shortcut:

```json
{
  "config": {
    "numericAttribute": "temperature",
    "steps": {"type": "continuous"}
  }
}
```

**Explicit break points (when you know the values you want).** Provide the break points directly:

```json
{
  "config": {
    "numericAttribute": "magnitude",
    "steps": [0, 3, 5, 7, 9]
  }
}
```

## How breaks map to colors and sizes

* **Classed:** `N` break points produce `N − 1` classes. An explicit `color` or `size` array must therefore have exactly `N − 1` entries. (A palette shortcut such as `@galaxy` is expanded for you.)
* **Continuous:** provide **2 or more** colors and Felt interpolates between them across the full value range. For proportional symbols, give `size: [min, max]` and sizes scale smoothly between those two values.

{% hint style="info" %}
For choropleths and other classed thematic maps, 5–7 classes usually reads best. For right-skewed data (common with population or income), prefer `quantiles` or `jenks` over `equal-intervals`. When you have the data's min/max on hand, use them to inform the method and breaks.
{% endhint %}

See [Numeric visualizations](/felt-style-language/types-of-visualizations/numeric-visualizations-color-and-size.md) for full color and size examples, and [Colors & palettes](/felt-style-language/colors-and-palettes.md) for palette choices.


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