Skip to main content icon/video/no-internet
icon backReturn to Entries

Dot Density Maps

Dot density maps, or dot maps, portray the geographic distribution of discrete phenomena using an arrangement of identical point symbols, most commonly dots. The dot density technique dates to at least the 19th century and is today accepted as one of the primary techniques for representing geographic patterns. Dot density maps are particularly useful for understanding global distribution of the mapped phenomenon and comparing relative densities of different regions on the map. Dot density maps are also easy to understand, requiring little cognitive effort from the map reader when compared with isoline maps. However, retrieval of specific information from dot density maps is difficult as map users find manual counting of dots tedious and tend to underestimate dot totals as the density increases.

Types of Dot Density Maps

There are two kinds of dot density maps: one-to-one maps and one-to-many maps. In one-to-one dot density maps, each point on the map corresponds to a single incidence of the mapped phenomenon. One-to-one dot density maps are general-reference maps, symbolizing spatial location only. Because of this, care should be taken to ensure that a dot is accurately located on the map. Examples of data sets ideal for one-to-one dot density mapping include the major cities in Europe or locations of recent earthquakes along the Pacific Rim.

Although one-to-one dot density maps are more common in practice, the term dot density map typically refers to one-to-many dot density maps. In one-to-many dot density maps, each point on the map represents a predetermined number of incidences of the mapped phenomenon, called the dot value. One-to-many dot density maps are thematic maps, symbolizing an aggregated variable atop a reference base map. Use of a one-to-many dot density map, rather than the one-to-one counterpart, is necessary when the only available data are aggregated to areal enumeration units or there are too many point incidences within the map extent for legible representation, necessitating aggregation by the cartographer. Examples of data sets ideal for one-to-many dot density mapping include the population of the United States and the number of dairy cows in Wisconsin, both aggregated by county.

Aggregated Data and One-to-Many Dot Density Maps

Not all aggregated data are appropriate for one-to-many dot density mapping. Alan MacEachren and David DiBiase developed a typology of aggregated data based on two characteristics of the mapped phenomenon depending on whether (1) the phenomenon occurs at discrete locations in space or exists continuously throughout the extent of the map or (2) the phenomenon changes abruptly at enumeration boundaries or varies smoothly throughout the extent of the map. Each data model is then paired with a recommended mapping technique. Figure 1 provides simple and effective guidance for determining the appropriate thematic map technique for representing aggregate data, given the characteristics of the mapped phenomenon. Only aggregated data of phenomena that exist discretely in space and vary smoothly across space should be mapped using the dot density technique. Only magnitude data should be displayed with dot density maps; derived values, such as averages, rates, and percentages, are theoretically continuous and should therefore be mapped with a choropleth.

...

  • Loading...
locked icon

Sign in to access this content

Get a 30 day FREE TRIAL

  • Watch videos from a variety of sources bringing classroom topics to life
  • Read modern, diverse business cases
  • Explore hundreds of books and reference titles

Read next

More like this

Sage Recommends

We found other relevant content for you on other Sage platforms.

close

Have you created a personal profile? Login or create a profile so that you can save clips, playlists and searches

close
close
close Signed In to profile You are signed in as:
close
Logged in Institution Institution