Create and use maps with column chart symbols

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Column chart symbols are used with maps to show the number of occurrences or proportions of categorical data associated to a single feature, using either a count or a summary statistic. Column chart symbols are created on a map using a string field and a location field containing coincident values.

Column chart symbols can answer questions about your data, such as the following:

  • How are categorical values distributed geographically?
  • How is the data ranked?


A GIS analyst working for a consortium of colleges wants to find which states have high-value colleges (colleges with a high ratio of earnings after graduation compared to tuition and fee costs). Part of her analysis includes comparing the number of public, private for-profit, and private nonprofit schools both within and between states. Creating a map with column chart symbols allows the analyst to make both comparisons simultaneously.

Column chart symbols showing college types by state

The analyst can tell from the map that the states with larger populations have more colleges overall. She decides to normalize the data by changing the symbols from Expanded to Stacked Percent. That way, she can compare the proportions of college types between states without being influenced by the total numbers of colleges in the state.

Column chart symbols showing the proportion of college types by state

Create a map with column chart symbols


Column chart symbols can only be created with multipart datasets in which features are collocated. These datasets can be created using a one-to-many join, or by enabling location on a dataset. If location is enabled with addresses or coordinates, Repeat identical features must remain unselected.

Complete the following steps to create a map with column chart symbols:

  1. Create a multipart dataset using one of the following methods:
    • Create a join between two datasets. Generally, these datasets will include the features you want to display as column chart symbols and a boundary layer.

      The join should be one-to-many, meaning each boundary feature contains more than one feature to create the column chart. In the example above, a join was created between a dataset with college data and state boundaries.

    • Enable location for your dataset.

      If you enable location for points using addresses or coordinates, Repeat identical features must remain unselected. This method should only be used in situations where multiple data points appear in the exact same location. For example, a dataset containing monthly status updates for fire hydrants in a city could contain a new input each month for each fire hydrant. Enabling location without repeating identical features will aggregate the inputs for each hydrant.

      If you enable location using geography, there should be multiple features within each boundary. This method functions the same as a one-to-many join.

  2. Expand a dataset in the data pane so that the fields are visible.
  3. Select one of the following combinations of fields:
    • A location field plus a string field
    • A location field, a string field, and a number or rate/ratio field

    If you created a join in step 1, the location field you select must be the field that corresponds to the boundaries.


    You can search for fields using the search bar in the data pane.

  4. Drag the fields to the page and drop them on the Map drop zone.

    A map is created.

    If the map was created using a location field, string field, and number or rate/ratio field, then the map will be styled using pie chart symbols. If the map was created using a location field and a string field, then the map will be styled using unique symbols.

  5. Expand the legend to display the Layer options pane.
  6. Browse to the Symbology tab Symbology.
  7. Change Symbol type to Columns.

Usage notes

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 values and counts of the column chart symbols and make selections based on the values. To change the color associated with a category, click the symbol and choose a color from the palette, or enter a hex value.

Use the Symbology tab Symbology to do the following:

  • Change the field displayed on the map, or switch to a different type of map.
  • Change the numeric variable. The numerical variable determines the height of the bars or bar segments and can be based on a count of features or a number or rate/ratio field. If a number or rate/ratio field is chosen, the heights can be based on a sum, minimum, maximum, or average.
  • Change the display between Stacked Count, Stacked Percent, and Expanded.
    • Stacked Count—Each geographic feature is symbolized using a single bar with categories visualized as subgroups of the bar. The bar height is determined by the overall count for the geographic feature, and the height of each category segment is determined by the count of the category.
    • Stacked Percent—Each geographic feature is symbolized using a single bar with categories visualized as subgroups of the bar. The bar height is uniform for each geographical feature, and the height of each category segment represents the percentage of that category relative to the geographic feature.
    • Expanded—Each category is represented by a separate bar. Bar heights are determined by the count for each category.
  • Configure the map pop-ups to display with or without statistics, or disable pop-ups for the layer.

Use the Appearance tab Appearance to change the symbol style properties, such as symbol size and layer transparency.

Use the Attributes tab Attributes to view details for features selected on the map.

Use the Visualization type button Visualization type to switch directly between a map with column chart symbols and other visualizations, such as a summary table, stacked bar chart, chord diagram, or treemap.


Column chart symbols show all unique categories associated with a single feature or location. If the features contain many unique categories or large differences between minimum and maximum counts, the column chart symbols may be difficult to interpret.