Change Style reference (Map Viewer Classic)

Note:

A new Map Viewer is now available. The new Map Viewer was previously available as a separate beta installation but is now available in the portal automatically. Open it from the app launcher to get started. To learn more, see the new Map Viewer help documentation.

Map Viewer Classic allows you to explore your data through a variety of smart mapping styles. When you use Change Style in Map Viewer Classic, the nature of your data determines the styling suggestions you see by default. Once you decide how you want to present the layer, you can make changes to its appearance that are immediately reflected on the map. With Map Viewer Classic, you have control over styling elements such as color ramps, line weights, transparency, and symbols.

Supported layer types

You can use Change Style smart mapping with the following types of layers:

• Hosted feature layer
• CSV on the web
• CSV, SHP, GPX added to map
• ArcGIS Server feature layer
• ArcGIS Server map image layer that supports dynamic layers
• Individual feature layers from an ArcGIS Server map image layer
• ArcGIS Server image layer with vector field data (symbology changes only)
• ArcGIS Server stream service
• GeoRSS (Location (Single symbol) in single-layer GeoRSS layers only)
• OGC WFS layer (Location (Single symbol) only)

General styling options

When you apply a style to your layer using the Change Style pane, you can change and rotate symbols as needed. When styling data with numeric values using color, you can choose a color theme to help you tell your story.

Change symbols

If you want to use different symbols in your layer, you can change all the symbols at once. The choices you see depend on the type of symbols you are using. To change symbols, do the following:

1. Click Symbols.
2. Do any of the following:
   • For Shape, click a symbol set and click the symbol you want to use. For Location (Single symbol) and Counts and Amounts (Size), adjust the size of the shape, and if you want to use your own symbol, click Use an image, enter the URL of the image, and click the add button . For best results, use a square image (PNG, GIF, or JPEG) no greater than 120 pixels wide by 120 pixels high. Other sizes will be adjusted to fit.
   • For Fill, click a color and adjust the transparency.

     For Counts and Amounts (Color), click a color ramp and invert the ramp. This flips the colors.

   • For Outline, click a color, change the transparency, and change the line width. For polygons, check the box to adjust the outline automatically.
   • For line symbols, click a color, change the transparency, specify a line width, and select a line pattern. To show line direction, select an arrow option. For single-arrow lines, arrow placement is based on the start and end points of each line feature.

Rotate symbols

Rotate symbols by an angle, determined by a chosen field, when you want the symbol to reflect direction—for example, the direction the wind is blowing or a vehicle is traveling. When selecting a point symbol, choose one that points north so that the rotation matches the resulting direction of the symbol.

To rotate symbols, do the following:

1. Check the Rotate symbols (degrees) box.
2. Do one of the following to set the rotation angle:
   • To use an attribute, select the attribute you want.
   • Custom expressions written in the Arcade scripting language can also be used when setting the rotation angle. You can select it at the bottom of the drop-down menu. If you want to edit the expression or its name, click the Edit Expression button and use the editor window to edit it.
   • If you want to create an Arcade expression, select New Expression from the drop-down menu and use the editor window to create your expression, including giving it a name.

     Note:

     You can use existing expressions to build new expressions, but be aware that certain variables may not work across profiles. To use an existing expression, select it from the Existing tab in the editor window.

   Arcade expressions are supported for all styles except Heat map, Predominant Category, and Predominant Category and Size styles.

   Tip:

   If you need help with any of the Arcade functions, click the Information button beside the function to see reference information about the function.

3. Select one of the following:

   Geographic	Angles are measured clockwise from the 12 o'clock position (geographic rotation).
   Arithmetic	Angles are measured counterclockwise from the 3 o'clock position (arithmetic rotation). Note: With arithmetic rotation, the symbol—assumed to be pointing north—is first rotated 90 degrees clockwise to align with 0 degrees before the counterclockwise rotation from the field attribute is applied.

Color themes

If you are using a color ramp to style numeric values in your data, you can choose from a selection of themes and apply the one that best suits your data and the story you want to tell. Depending on the smart mapping style you choose, the following themes may be available:

• High to Low—Shows the range of values from high to low. This theme emphasizes the highest and lowest values in your data, using dark to light (or light to dark if you prefer) shades of color. Choose this theme when you want to show the overall spread or range of values in your data. For example, when mapping incomes in your city, you can use this theme to highlight the areas with the highest and lowest incomes to help identify areas that might need assistance.
• Above and Below—Shows values above and below a value such as zero or the average. By default, this theme centers your map around the statistical mean and maps all values above the mean as one color and all values below the mean as a different color. You can also choose a value other than the statistical mean, depending on your needs. Choose this theme when you want to anchor your map around a specific value. For example, when mapping traffic levels, you can use this theme to emphasize areas with below-average traffic.
• Centered On—Centers on and highlights a particular range of values. This theme helps to show statistically significant areas of the data by adding transparency to the outliers while highlighting the values around the mean +-1 standard deviation. For example, when mapping the average percentage of rainfall in an area, you can use this theme to emphasize the areas that received statistically average amounts of rainfall while making the anomalies more transparent.
• Extremes—Highlights the extreme values in the data. This theme helps to showcase the statistically extreme edges of the data by adding focus to the values that fall outside of +-1 standard deviation. For example, you can use this theme to highlight the highest and lowest data values, such as the least expensive and most expensive housing in an area.

Classification methods

If you style a layer using color or size to show numeric data, the layer is styled by default using a continuous color ramp (see Counts and Amounts (Color)) or a sequence of proportional symbols (see Counts and Amounts (Size)). You also have the option of classifying your data—that is, dividing it into classes, or groups—and defining the ranges and breaks for the classes. For example, you may want to group the ages of individuals into classes of ten (0-9, 10-19, 20-29, and so on). Classification lets you create a more generalized (less detailed) picture of your data to tell a specific story.

Depending on how much data you have in your layer, you can also choose the number of classes—one through ten. The more data you have, the more classes you can have. The way in which you define the class ranges and breaks—the high and low values that bracket each class—determines which features fall into each class and what the layer looks like. By changing the classes using different classification methods, you can create very different looking maps. Generally, the goal is to make sure features with similar values are in the same class.

Equal interval

Equal interval classification divides the range of attribute values into subranges of equal size. With this classification method, you specify the number of intervals (or subranges), and the data is divided automatically. For example, if you specify three classes for an attribute field whose values range from 0 to 300, three classes with ranges of 0–100, 101–200, and 201–300 are created.

Equal interval is best applied to familiar data ranges, such as percentages and temperature. This method emphasizes the amount of an attribute value relative to other values. For example, it could show that a store is part of the group of stores that make up the top one-third of all sales.

Natural breaks

Natural breaks (also known as Jenks Optimal) classes are based on natural groupings inherent in the data. Class breaks that best group similar values and that maximize the differences between classes—for example, tree height in a national forest—are identified. The features are divided into classes whose boundaries are set where there are relatively big differences in the data values.

Because natural breaks classification places clustered values in the same class, this method is good for mapping data values that are not evenly distributed.

Standard deviation

Standard deviation classification shows you how much a feature's attribute value varies from the mean. By emphasizing values above the mean and below the mean, standard deviation classification helps show which features are above or below an average value. Use this classification method when it is important to know how values relate to the mean, such as when looking at population density in a given area, or comparing foreclosure rates across the country. For greater detail in your map, you can change the class size from 1 standard deviation to .5 standard deviation.

Quantile

With quantile classification, each class contains an equal number of features—for example, 10 per class or 20 per class. There are no empty classes or classes with too few or too many values. Quantile classification is well suited to linearly (evenly) distributed data. If you need to have the same number of features or values in each class, use quantile classification.

Because features are grouped in equal numbers in each class, the resulting map can often be misleading. Similar features can be placed in adjacent classes, or features with widely different values can be put in the same class. You can minimize this distortion by increasing the number of classes.

Manual breaks

If you want to define your own classes, you can manually add class breaks and set class ranges that are appropriate for your data. Alternatively, you can start with one of the standard classification methods and make adjustments as needed. There may already be certain standards or guidelines for mapping your data—for example, an agency might use standard classes or breaks for all maps, such as the Fujita scale (F-scale) used to classify tornado strength. Place the breaks where you want or need them.

Styling considerations

• Imagery layers have a different workflow specific to changing the symbology.
• When you edit a feature layer in Map Viewer Classic, the map displays the symbology and templates configured by the layer owner. When you finish editing, the styles you set for your map will once again appear.
• Scene Viewer does not support heat maps.
• Not all ArcGIS mapping and business apps support the following smart mapping options: heat maps, counts and amounts when Classify Data is unchecked, or per-feature transparency. When styling maps targeted for an ArcGIS app, consider these limitations. For example, if your organization views maps in an app that doesn't fully support smart mapping, you can style income data using colors with natural breaks.
• If you save your style changes to a hosted feature layer item, you cannot use it to publish a hosted tile layer if the styling includes heat maps, counts and amounts when Classify Data is unchecked, multiple attributes, or per-feature transparency.
• For counts and amounts (color), the histogram that shows data distribution may not appear if the layer does not have sufficient data or it takes too long to retrieve the data.
• The Heat Map style is not supported for layers with clustering enabled.
• Heat maps are always displayed below feature layers in the map. You cannot change the order of heat maps to appear on top of feature layers.