Dot Density Maps
Dot density maps also display the density of data values in a given area. A dot might represent a single data point (one-to-one), or it could be used to represent a set of data in which each point shares an attribute. For example, each dot might represent one smoker or one hundred smokers in an area (one-to-many). This type of map enables you to visualize how occurrences of the value being measured are clustered, but is difficult for users to estimate exact values based on the number of points.
Dot density maps display dot density in a randomized distribution. In other words, the dot on the map does not translate to the exact occurrence/location of the value being mapped. Random points used to create a dot density map should be generated within the specific geographic area that they occur. This is important because you don’t want your reader to assume that a point is tied to a specific geographic location (for instance, that a dot shows a person’s location). You can further reduce this inference by restricting the zoom level of the map.
- Number of people by census block in a NYC (1 dot = 100 people)
- Wildfire locations in California over the last 5 years (1 dot = 1 fire)
- What are two different ways that data can be represented with a dot density map?
- How are dot locations generated for this map type? How could you prevent users from making assumptions about dot locations?