Vector layers

Style your points, lines and polygons by customizing style properties to create engaging Felt maps.

The Style Editor provides different options for styling points, lines, and polygons, allowing you to control color, size, opacity, and many other visual properties. You can create simple maps or data-driven visualizations that vary based on attributes.

The style editor

Once your data is uploaded to Felt you can your layers with the style editor.

• To open the style editor, click on the layer you want to customize from the legend

Choosing your map type

Depending on the geometry of your vector layer (point, line or polygon), you will have different visualization options. To select your visualization choose it from the Type drop-down in the General section of the style editor.

Simple

The default style when you upload a data layer — a single color for all features. Available for point, line and polygon layers.

Categories

A categorical visualization maps a distinct color for every unique value of your chosen data attribute. A common type of categorical visualization are landuse maps where each landuse type is set to a distinct color. Available for point, line and polygon layers.

Color range

For numeric attributes. The color of each feature will be relative to the values in the data. Available for point, line and polygon layers.

Learn more about classification methods for numeric data

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.

• Standard Deviation: the Standard Deviation method chooses classes based on the number of standard deviations from the mean. This classification method works very well for data that follows a bell curve (a Gaussian or “normal” distribution).

By default, the legend in a Standard Deviation classification does not show the breakpoint values, using instead the nº of standard deviations.

• Geometric Intervals: a classification method where the size of each class increases exponentially, very useful for right-skewed data. The term “Geometric” comes from the fact that the class breaks are chosen from a geometric series (where each value equals the previous value, multiplied by a constant coefficient).

• Jenks Natural Breaks: the Jenks optimization method can be used to find “natural” classes inside your data by minimizing the variation inside classes while maximizing the variation between classes. It works well for most kinds of distributions, which is why it’s the default method in Felt.

Size range

The size of a point or line will be proportional or sized based on groups of values in the data. Available for point and line layers.

Learn more about classification methods for numeric data

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.

• Standard Deviation: the Standard Deviation method chooses classes based on the number of standard deviations from the mean. This classification method works very well for data that follows a bell curve (a Gaussian or “normal” distribution).

By default, the legend in a Standard Deviation classification does not show the breakpoint values, using instead the nº of standard deviations.

• Geometric Intervals: a classification method where the size of each class increases exponentially, very useful for right-skewed data. The term “Geometric” comes from the fact that the class breaks are chosen from a geometric series (where each value equals the previous value, multiplied by a constant coefficient).

• Jenks Natural Breaks: the Jenks optimization method can be used to find “natural” classes inside your data by minimizing the variation inside classes while maximizing the variation between classes. It works well for most kinds of distributions, which is why it’s the default method in Felt.

Heatmap

Select a color palette and adjust the size and intensity styling properties to refine the visualization. Available for point layers.

H3

Display point counts or aggregated numeric attributes (sum, average, max, or min) in each hexagon. Provide a specific H3 resolution for a visualization that doesn’t change across zoom levels, or set the layer to adjust resolution based on zoom. Use popups to show other aggregated values on hover or click. Available for point layers.

Check out this video to learn more about H3 visualizations.

This feature is only available to customers on the Enterprise plan. To upgrade, contact sales.

Filtered H3 visualizations are estimates. For optimal accuracy, update the layer after filtering it.

Icons

Customize your point data with a variety of icons to make your map more intuitive and visually appealing. Select from Felt's built-in icon library, emojis or upload custom icons by clicking the Icon drop-down in the Point section of the style editor. Available for point layers.

Custom icons

Upload your own icon sets to Felt to personalize your maps, highlight key features, or align them with your organization's branding. Icons are a powerful way to visually differentiate categories or other attributes within your point data, helping viewers quickly interpret the information. With Felt, you can organize your icons into Themes, making it easy to manage and apply them consistently across projects. Plus, shared access within your workspace ensures seamless collaboration and a unified look for your maps.

This feature is only available to customers on the Enterprise plan. To upgrade, contact sales.

Uploading custom icons

Felt supports a wide variety of image formats, including .png, .jpg, .svg, and other common image file types. To add icons, click the Upload icons button from the Custom tab of a point layer’s Icon picker.

You can upload icons one at a time, or you can select many icons to upload at once by selecting them in the file picker.

Add icons to a single map, or make them available across all maps by adding them to your workspace. When you have many icons, you can assign icons themes to make browsing and searching more manageable.

Once icons have been uploaded, you can edit or delete them via the Edit icons button on the Custom tab of the Icon picker.

Labelling

By default, Felt picks the field most likely to contain names and uses it to label your data layers. However, you can select another field or switch labels off entirely. You can access label styles and properties in the Label section of the style editor.

Labels can be fine-tuned in the following ways:

• Size: choose between small, medium and large sizes.

• Color: choose a color from the color picker or directly input an RGB, hex or HSL value.

• Halo: choose a color and width for the halo surrounding the label text.

• Style: many more options to customize your labels, including:

  • Weight: regular, medium, bold and italic weights, or a combination of these.

  • Case: convert labels to lower or uppercase.

  • Letter spacing: spacing between letters, in pixels.

  • Line spacing: spacing between lines, in pixels.

  • Max length: max character length for labels.

  • Placement: choose between automatic placement or prefer a certain direction (top, right, etc).

  • Offset: the offset between the feature and label, in pixels.

  • Padding: label padding is the amount of transparent pixels to add around your label items. Increasing this amount will result in fewer labels in the map.

Zoom-based styling

One of the more challenging aspects of digital cartography is ensuring your maps look good at all zoom levels. Felt gives you control over what your visualizations look like at each zoom by letting you not only control visibility of features and labels but also interpolate styling properties between zoom levels.

Setting Visibility

Feature and label visibility can be limited to a specific zoom range by toggling the Limit visibility menu. For instance, you might want to limit point or line labels to only appear when a user is at a higher zoom.

Interpolating style properties

Zoom-based styling in Felt allows you to adjust visual elements like line thickness, opacity, and color at different zoom levels, creating maps that reveal appropriate details as users zoom in or out. This technique enables features like roads to appear thinner at zoomed-out views and thicker when zoomed in, or polygons to gradually fade in as the user zooms closer to a specific area.

Check out this video to learn more about interpolating style properties.

Customizing your legend

Add a caption, retouch colors and display names in the Legend tab.

Resetting layer styles

If at any point you want to go back to the default style, all you have to do is click on the Actions > Reset style option in a layer’s overflow (three dots) menu

Advanced options

For more control fine-tuning your visualization or if you prefer writing style through code you can use the advanced style editor.

To open the advanced editor for any layer

• Click on the layer's three dot (overflow) menu and select Actions > Edit style

• Once opened, you’ll see the advanced editor open on the right-hand side of the screen. This panel contains the Felt Style Language (FSL) for the selected layer.

The Felt Style Language (FSL) is a JSON-based specification of Felt styles, and is the single source of truth for a layer’s style. You can check this yourself by making changes in the Style Editor while having the Advanced Style Editor open:

Learn more about FSL in our Developer docs.