Skip To Content

Create and use a choropleth map

Choropleth maps use the Counts and Amounts (Color) smart mapping symbol type to show normalized data as shaded points, lines, or areas. Choropleth maps help answer questions about your data, such as: How do rates or percentages compare by geographic feature?

Example

A crime analyst is researching crime frequencies across the city and the correlation between crime and other social issues, such as high unemployment rates. City officials will use the results to implement new social programming across the city in an effort to reduce crime. A choropleth map can be used to visualize the unemployment rates in police districts across the city and compare them to crime rates.

Choropleth map showing the unemployment rate for each police district in Philadelphia

Darker areas in the above map indicate high levels of unemployment, while lighter areas indicate low levels of unemployment.

Create a choropleth map

A choropleth map is automatically created when a rate/ratio field is used to create a map. A numeric field can also be used to create a choropleth map by switching Symbol Type from Counts and Amounts (Size) to Counts and Amounts (Color). Numeric data should then be normalized using the Divide By parameter when used to create a choropleth map.

To create a choropleth map with a rate, ratio, or proportion, use the following steps:

  1. Expand a dataset in the data pane so that the fields are visible.
  2. Select a rate/ratio field Rate/ratio field.
    Tip:

    If you have rate/ratio values in a number field Number field, you can change the field role by clicking the field icon and choosing Rate/Ratio.

  3. Drag the field to the page and onto the Map drop zone. A choropleth map will be created using Counts and Amounts (Color) as the Symbol type setting.
    Note:

    The Counts and Amounts (Color) smart mapping symbol type is applied by default when you create a map using a rate/ratio field. You can also apply Counts and Amounts (Color) to maps created using a number field.

To create a choropleth map using normalization, complete the following steps:

  1. Expand a dataset in the data pane so that the fields are visible.
  2. Select a number field Number field. The number should be a total, such as number of crimes or total sales.
  3. Drag the field to the page and onto the Map drop zone. A proportional symbol map is created.
  4. Expand the legend to display the Layer options pane.
  5. Browse to the Options tab Options.
  6. Change Symbol type to Counts and Amounts (Color).
  7. Choose a number field for the Divide by parameter. The field should have a number that can be used to create a proportion from the first number field, such as total population.

Usage notes

Click the Info button Info to turn the map card over. The back of the card includes statistics and a place to write a description of the map.

The Layer options pane is accessible from the layer legend and can be used to view the classification values being mapped, change the style of the map, and view information about selected features.

Use the Legend tab Legend to view the classification values of the choropleth map and make selections based on the values.

Use the Options tab Options to do the following:

  • Change the field being displayed on the map or switch to a different type of map.
  • Change the statistics for the display field. This option is only available if location was enabled on the dataset with aggregation allowed for identical features or if the dataset was created through spatial aggregation.
  • Change the classification type.
  • Change the number of classes being displayed.
  • Change, add, or remove the Divide by field.

Use the Style tab Style to change the symbol style properties, such as color palette, symbol size, outline thickness and color, and layer transparency.

Use the Pop-up tab Popup to view details for features selected on the map.

How choropleth maps work

In a process referred to as data classification, proportional numeric values are grouped into ranges, and each classification range is represented by a shade or color on the color ramp. The values should be proportions to reduce bias from different-sized areas.

Data classification

The following classification options are available for choropleth maps:

Classification methodDescriptionExample

Natural breaks

Classes are based on natural groupings inherent in the data. This is the default classification.

The natural breaks method should be used when you want to emphasize the natural groupings inherent in your data. Natural breaks should not be used to compare maps created with different data.

Use natural breaks to compare the crime rates in neighborhoods across a city. The crime rates will be grouped so that neighborhoods with a similar crime rate will be symbolized with the same color.

Equal interval

Divides the range of attribute values into equal-sized subranges.

The equal interval classification emphasizes the amount of an attribute relative to other values and should be used for data that has familiar ranges.

Use equal intervals to compare the percentage of trees with invasive beetles across parks in a county. The percentages range from 0 to 100. If you choose to use four bins, the classes will be based on 25% intervals.

Quantile

Divides the attributes into bins with equal numbers of features.

The quantile classification can distort the look of your map by placing similar values in different classes. Therefore, this classification method should be used on data that is relatively uniform. You can also use quantile classification as a method of visual ranking.

Use quantile intervals to compare the unemployment rates across states in the United States. If you apply five bins to the 50 states plus the District of Columbia, there will be approximately 10 states per bin. The results can be used to see the unemployment rates ranked in groups of 10.

Standard deviation

Classifies a feature based on how much the feature's attributes vary from the mean.

The standard deviation method works best on datasets that are normally distributed and for analyses in which the mean, or the distance from the mean, is important.

Tip:

Try pairing the standard deviation classification with a diverging color ramp. Diverging color ramps style the upper and lower extremes with dark shades and style the mean with a neutral color.

Use standard deviation and a diverging color ramp to compare the average life expectancy between countries. The countries with the highest and lowest life expectancy will be displayed in different dark shades. The colors will become lighter as the classes move closer to the mean global life expectancy.

Unclassed

Numeric data is displayed on a continuous scale, rather than in discrete classes.

The unclassed method should be used when you want to see gradual changes in your data.

Use an unclassed color ramp to style average temperature measurements for a given time range taken at regularly placed weather stations. The points will show gradual changes in temperature over the study area.

Manual

Manually add class breaks that are appropriate for your data.

The manual method should be used when there are known ranges that must be applied to your data, such as when you want to create multiple maps with the same bins.

Use a manual classification to compare the average household income in neighborhoods across a city over time. The manual classification can be used to apply the same bins to both maps so that patterns and comparisons can be made without making false assumptions due to differences in the classification.

Normalization and proportional data

Styling your map using graduated colors, like with a choropleth map, can lead to visual misinterpretations, especially when the features on the map are areas of various sizes or populations. In these cases, larger areas will naturally draw your attention, especially if they're styled with darker colors. You can counter the bias created from different-sized areas in choropleth maps by styling the maps by averages, proportions, rates, and ratios instead of counts or totals. When the data being displayed on a map is a proportional value, it's taking into account the differences between the features, whether it be population, area, or another factor.

Choropleth maps showing the number of restaurants and the number of restaurants per capita by county
(Left) The total number of restaurants in each county. This map is displaying totals, so it should not use graduated colors. (Right) The number of restaurants per capita in each county. This map is displaying proportional data, so a choropleth map is appropriate.

Both maps above use colors to show the number of restaurants by county. However, the map on the left shows the total number of restaurants and the map on the right shows the number of restaurants per capita. The counties have some variation in area, but the biggest variation is in the population across counties. The combination of large areas and a large number of restaurants emphasizes features such as Long Island and the Boston area, even over the smaller counties in New York City that are the same color. However, when the population of each county is taken into account, like in the map on the right, we see that the counties around Cape Cod and inland from the coast have a larger number of restaurants per capita and the majority of the other counties have an average number of restaurants per capita. The per capita map is a correct choropleth map.

Note:

If you want to make a map of counts or totals, such as the total number of restaurants by county, you can make a proportional symbol map.

If you want to create a choropleth map but you do not have proportional data, you can create proportions using a process called normalization. When you normalize your data, you take a number, like total crimes, and divide it by another number, like total population, to create a proportional value. Normalization can be performed when you create a choropleth map using the Divide by parameter on the Options tab Options. In the example above, the total number of restaurants in each county was normalized using the total population of the county.