Create and use a heat chart

A heat chart is used to visualize the numeric relationship between two categorical variables. A heat chart consists of a rectangular grid composed of two categorical variables. Each cell in the grid is symbolized using a numeric value.

Heat charts can help you answer questions about your data, such as: How are numeric values distributed or summarized by two categories? How are two categories related?

Examples

A crime analyst is studying the frequency of theft-related crimes in her city. She wants to know the type of incident occurring most often and the months that have the most crime. A heat chart can be used to visualize the relative prevalence of each crime for each month.

Create a heat chart

To create a heat chart, complete the following steps:

1. Select one of the following combinations of data:
   • Two string fields
   • Two string fields plus a number or rate/ratio field

   Note:

   If you do not select a number or rate/ratio field, your data will be aggregated and a count will be displayed.

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

2. Create the heat chart using the following steps:
   1. Drag the selected fields to a new card.
   2. Hover over the Chart drop zone.
   3. Drop the selected fields on Heat Chart.

Tip:

You can also create charts using the Chart menu above the data pane or the Visualization type button on an existing card. For the Chart menu, only charts that are compatible with your data selection will be enabled. For the Visualization type menu, only compatible visualizations (including maps, charts, or tables) will be displayed.

Heat charts can also be created using View Heat Chart, which is accessed from the Action button under Find answers > How is it distributed?.

Usage notes

Heat charts are symbolized using graduated colors.

This chart type creates a result dataset in the data pane, which includes the fields used to create the chart. The result dataset can be used to create additional visualizations, rename the fields on the chart axes or in the pop-ups, or apply filters to the chart.

Use the Layer options button to open the Layer options pane and update the following configuration options:

• The Legend tab can be used to make selections on the chart. The pop out legend button displays the legend as a separate card on your page.
• The Symbology tab changes the classification type and number of classes.
• The Appearance tab changes the color palette and outline color.

Use the Card filter button to remove any unwanted data from your chart. Filters can be applied to all string, number, rate/ratio, and date/time fields. A card filter does not affect other cards using the same dataset.

Use the Visualization type button to switch directly between a heat chart and other visualizations, such as a grouped summary table, a bar chart with a Subgroup field, or a data clock.

Use the Maximize button to enlarge the card. Other cards on the page will be reduced to thumbnails. The card can be returned to its previous size using the Restore down button .

Use the Enable cross filters button to allow filters to be created on the card using selections on other cards. Cross filters can be removed using the Disable cross filters button .

Use the Flip card button to view the back of the card. The Card info tab provides information about the data on the card and the Export data tab allows users to export the data from the card.

Use the Card options button to access the following menu options:

• Appearance button —Change the background color, foreground color, and border of the card.
• Edit labels button —Create custom labels for the chart axes. To edit the labels, click the Edit labels button then click the axis to make it editable.
• Order button —Move the card forward or send the card backward relative to other cards on the page.
• Delete button —Removes the card from the page. If you did not intend to delete the card, you can retrieve it using the Undo button .

Limitations

The heat chart variables cannot exceed 3,000 unique values per axis. If one or both of the variables exceed the 3,000 value limit, a filter, such as a predefined filter, can be used to reduce the size of the dataset.