Apply styles

Maps allow you to visualize data in a variety of ways. For example, you can visualize population data for countries as a sequence of colors, such as from light to dark, or as proportional circles, such as from small to large. This flexibility allows you to tell different stories and discover hidden patterns depending on how the data is presented. However, because map-making is so flexible, you must make decisions when there isn't always a single best answer.

With ArcGIS Maps for Fabric, you can explore various styling options using smart mapping defaults. When you style map layers in ArcGIS Maps for Fabric, the nature of the data determines the styling options that appear by default in the Styles pane. You can then experiment with color ramps, line weights, transparency, symbols, and other graphic elements, and see your choices reflected immediately on the map.

Tip:

To learn more about smart mapping, see the following resources:

Smart Mapping: Get Started story
Smart Mapping Styles story collection

Apply a style

The styling options provided for a layer are based on the type of data you are mapping. The available options depend on whether the the feature layer is composed of point, line, or polygon features, or whether you are mapping imagery or raster data. For example, heat map styling options are available for a layer composed of points, but not for line or polygon layers. The options are also different if you are mapping categorical or continuous data. For example, a point feature may only have location information such as geographic coordinates but may also have categorical information such as tree species or numeric information such as air temperature. Styling options also vary depending on whether you want to show one or two attributes, such as revenue or population. Not every styling option can be used for every type of data. By analyzing these and other characteristics of the data, ArcGIS Maps for Fabric automatically presents you with the best styling options.

Note:

Instead of styling a feature layer using explicit attributes in the layer, you can create a custom attribute expression written in ArcGIS Arcade to use for styling. This is available for most styles. For example, you can create an Arcade expression to derive a yearly sales figure for individual sales territories by summing the value of monthly sales fields. The yearly sales figures can then be represented as different-sized symbols on the map. You can also create an Arcade expression or edit an existing Arcade expression directly in ArcGIS Maps for Fabric. You can also use Arcade expressions when setting transparency for features or the rotation angle of symbols, as well as when configuring pop-ups and labels.

Tip:

You can use the ArcGIS Arcade assistant (beta) to generate ArcGIS Arcade expressions from instructions typed in natural language.

When you add a layer without any styling attached to it, such as a layer from your lakehouse, ArcGIS Maps for Fabric displays the layer with default styling applied. If you add a layer with existing styling applied, such as a layer from ArcGIS Online, ArcGIS Maps for Fabric respects that styling. You can change the style of a supported layer at any time by clicking the Styles button on the Settings toolbar.

To apply a style or change the style of a layer, do the following:

Tip:

Confirm that your ArcGIS organizational account user type is Creator or higher and that your user role is Contributor or higher in the Microsoft Fabric workspace where you are working to save changes to the map.

In ArcGIS Maps for Fabric, open the map containing the layer, or add the layer directly.
In the Layers pane, click the layer to select it.
On the Settings toolbar, click Styles .
In the Styles pane, do one of the following in the Choose attributes section:

Note:

You can skip this step if you want to show locations using a single symbol or map the locations of point features as a heat map.
- Click Field, find and select the attribute, and click Add to style an attribute in the layer.
- Click Expression and create a custom Arcade expression in the editor window, including providing a name, to style the layer.
  
  You can also use existing expressions to build new expressions; however, some variables may not work in all profiles —for example, an expression created for pop-ups may not work for styles. To use an existing expression, select it from the Suggestions tab under Existing.
Tip:

If you need help with any of the Arcade functions, click the arrow next to the function to see reference information about it.
To style additional attributes or create more expressions, repeat the previous step.

The style currently applied to the layer is selected in the Pick a style section.
Optionally, select a different style. Choose a style based on what you want to show.

For help choosing a style, see the Styles quick reference table.

Note:

Only the options that apply to the data appear. For example, if you only know the location of a feature, you can only use a single symbol or heat map, not size or color. However, if you have categorical or numeric information attached to those locations, smart mapping presents additional styling options.

Some styles also include a Theme option. Themes allow you to experiment with various views of your data. The availability of themes depends on the smart mapping style you choose.
Optionally, click Style options on the style card to customize the look of the layer.

Tip:

With Color and Size, Types and Size, Predominant category and Size, Relationship and Size, Types and Size (age), and Color and Size (age), you apply styling options to each attribute. For example, if you choose the Types and Size style, choose options for Types (unique symbols) and Counts and Amounts (size). To change the style applied to each attribute, drag them into a different order in the attributes list.

Tip:

Under Generate style range, choose an option from the drop-down menu to determine what data is used to generate the visual range for a given style. This option is not available for Types (unique symbols), Predominant category, Predominant category and Size, or Charts styles.
In the Style options pane, click Done when you finish customizing your style, or click Cancel to return to the Styles pane without saving your choices.
In the Styles pane, click Done.
Optionally, save your map to save your styling changes to the map.

Styles quick reference

When you style a layer using smart mapping, the available styling options depend on the type of data you are mapping (point, line, polygon, or imagery) as well as the type of data attributes (numbers, categories, dates, and so on) and number of attributes you choose. Each style helps you tell a slightly different story and answer different questions with the data.

The following table provides a quick reference of the smart mapping styling options available for various types of data and some of the key questions you can answer using each style:

Tip:

Explore examples of each style using the links in the Data type column below.

Data type	Questions that smart mapping can answer	Available smart mapping style
Location only Examples: recent earthquakes, white shark locations near New Zealand	Where are the features located? How are the features distributed geographically?	Location (single symbol) Heat map (point features only)
One numeric attribute Examples: market value of parcels, annual average daily traffic	How do the features compare to each other based on numeric values? Where are the highest and lowest values? Which features are above and below a specific attribute value?	Color and Size Counts and Amounts (color) Counts and Amounts (size)
Two numeric attributes Examples: number and rate of uninsured people, unemployment trend, richness and rarity of species	Where are the highest and lowest values? How are the numeric attributes related? What is the relationship between numeric totals and the rate or ratio? Are there any outliers? Which features have high values or low values for both attributes? Which features have low values for one attribute and high values for the other attribute? Where is the relationship pattern the strongest or weakest for each attribute?	Color and Size Compare A to B Relationship
Three numeric attributes Example: funding for conservation and wetlands programs, farming programs, and total funding	How are the numeric attributes related? Which features have high values or low values for both attributes? Which features have low values for one attribute and high values for the other attribute? Where is the relationship pattern the strongest or weakest for each attribute?	Relationship and Size
One or more numeric attributes (counts or amounts) with the same unit of measurement Example: distribution of population by race across the United States	How is the data distributed? How does the distribution of one attribute compare to other attributes?	Dot Density
Two to ten related numeric attributes with the same unit of measurement Examples: predominant ethnic population per census tract, predominant housing type by census tract and which tracts have the highest and lowest total housing units, language spoken at home by county, predominant type of fuel used to heat homes by census tract and total housing units	Which attribute has the highest value compared to other related attributes for each feature? Which has the lowest value? How much higher is the highest attribute value compared to other related attributes? What is the sum of the attributes for each feature and how does it compare to the other features?	Predominant category Predominant category and Size Charts Charts and Size
One category/type attribute Example: wind turbines by manufacturer	How is the data distributed or summarized by category?	Types (unique symbols)
One category/type and one numeric attribute Example: unemployed people by county	Where are the highest and lowest values? How is the data distributed by category?	Types and Size
Date only Example: senior smoke alarm installations	Where are older features and where are newer features? Which features have dates that are before or after a key date?	Discrete Time Periods (categories)
One date/time attribute Examples: Rotterdam buildings built or rebuilt after the Rotterdam Blitz, oldest buildings in Rotterdam, age of code violation from complaint to compliance date Note: Supported date and time field types include the following: Date Date only To learn more about supported field types, see Date and time fields in ArcGIS Online.	Where are older features and where are newer features? Which features have dates that are before or after a key date? Which features have a higher age (length of time between two dates) and which have a lower age? What's the distribution of features by time period?	Continuous Timeline (color) Continuous Timeline (size) Age (color) Age (size) Discrete Time Periods (categories)
Two date/time attributes Example: relationship between age of code violation (how long between complaint and compliance) and how recently the violations occurred	Where are older features and where are newer features? Which features have dates that are before or after a key date? Which features have a higher age (length of time between two dates) and which have a lower age? What is the relationship between the age of features and how old or new are they?	Color (age) and Size Color and Size (age)
One date/time attribute and one numeric attribute	Which features have a higher age (length of time between two dates) and which have a lower age? What is the relationship between a feature's age and a numeric attribute value?	Color (age) and Size Color and Size (age)
One date/time attribute and one category/type attribute	How is the data distributed by category? Which features have a higher age (length of time between two dates) and which have a lower age? What is the relationship between the age of a feature and its category?	Types and Size (age)
Categorical raster Example: land cover including forest, urban, and agriculture	How is the data distributed by category?	Unique Values
Multispectral imagery Examples: multiband Landsat imagery, high to low elevation in Charlotte	How is the data distributed by category?	Unique Values
Continuous raster or imagery Examples: United States temperatures, elevation in Iceland	What is the distribution of values? Where are the highest and lowest values? How might the values be grouped into ranges? What does the shadow look like in elevation data?	Stretch Classify Shaded Relief
Colormap Example: Soil map with legend	What is the distribution of different soil types? How are soil types associated with slope? What is the distribution of land-cover and land-use classes? How do roads impact forest land cover over time?	Colormap
Multidimensional Vector Field Examples: National Digital Forecast Database wind direction and magnitude, United States wind speeds	What is the direction and magnitude of wind using National Digital Forecast Database wind data?	Vector Field Flow