In a choropleth map, the map displays a set of meaningful, non-overlapping polygons (such as the US states), colored according to either the density or numeric total of the data points across each region. Your dataset can be configured with your choice of regions, such as regional, county or district boundaries for a country of interest, corporate sales areas, or economic zones.
Select this view by clicking the Choropleth map button:
You can visualize the data for regions, such as US states or UK postcode areas, on a choropleth map, giving you a visualization like this:
If you change any property or time filters, this updates the choropleth.
You can filter by choropleth region; see Filtering your dataset for more details about how to apply filters. Note that you can use the choropleth region statistics and filtering controls even when a choropleth is not shown on the map.
Visualizing categorical data on a choropleth
For categorical data, a choropleth shows the relative densities of data points in a layer across the specified areas, in this case the US states. The lighter regions show the most dense areas (‘white-hot’ areas), whilst the darker regions have the lowest density of data points.
Example: Using a choropleth to visualize geographical density
If, for example, you have a dataset that includes all of your event data across the US states, you might want to know:
Which four US states have the highest geographical density of events in my data?
By filtering on the four states with the highest number of events, you can then visualize this data on a choropleth. Notice that the choropleth coloring is based upon the density of the data relative to the size of the US state.
Visualizing continuous numeric data on a choropleth
For continuous numeric data, each region on the choropleth displays either the average or total for the selected property breakdown. Lighter colors indicate a relatively higher average or total (‘white-hot’ areas), whilst darker regions indicate relatively lower averages or totals.
Example: Using a choropleth colored by numeric total
If, for example, you have a dataset that includes population data across the US states; you might have the following question:
What is the total population density by US state in 2018 from census records?
By combining the census data with a geometry layer containing the polygons defining each of the US states, you can visualize the distribution of the population across each of the states.