In the New Zealand map above, one dot = 27,500 sheep, which is known as the dot value of the map. Not sure you should use a dot density map? Potential alternatives include choropleth maps (if your data can be standardized and are attached to enumeration units), graduated/proportional symbol maps, and cartograms. Further, to avoid confusion, ideally dots should be distributed only in areas where the phenomenon actually exists (e.g., no dots in lakes for a map of population).

To combat this, dot density maps should not be made at too large a scale. To help with this problem, you can add numbers directly on the map or provide a table to accompany the map.ДЄdditionally, although most dot density maps distribute dots randomly, map readers may potentially infer dot locations as precise locations of the phenomenon being mapped (e.g., the actual exact location of a person). TheyвЂ™ll likely know that some places have вЂњmoreвЂќ sheep than others, but they wonвЂ™t know necessarily by how much. For example, few people will have the time or interest in counting hundreds (or thousands) or dots in order to know the precise number of sheep in New Zealand (above). While the versatility and easy-to-grasp nature of dot density maps is appealing, they have one fundamental drawback: theyвЂ™re terrible for retrieving rates or numbers from the map. From this map, we can see sheep are generally found throughout NZ, but that there are more sheep raised on the eastern side of the islands than the western side. Here is a dot density map that shows the number of sheep in New Zealand (by province). number of hectares of land treated for weed control in Russia, 1997 (1 dot = 500 hectares).number of people, by county, USA, 2010 (1 dot = 10,000 people).earthquake epicenters across the Pacific for the past 10 years (1 dot = 1 epicenter).the distribution of car dealerships in Belgium (1 dot = 1 dealership).There are at least three big advantages of dot density maps over choropleth maps: (1) on a dot density map you can map raw data / simple counts (e.g., number of farms) or rates and ratios (e.g., number of farms per sq kilometer) (2) your data need not be tied to enumeration units and hence some of the concerns inherent in choropleth maps can be side-stepped with dot density maps (unless, of course, your data are reported by enumeration units-in which case youвЂ™re probably stuck with them) and (3) dot density maps work fine in black and white, when color isnвЂ™t an option.Д®xample datasets appropriate for dot density maps: Albers Equal Area Conic, Sinusoidal, and Cylindrical Equal Area are all good choices. This is critical - using a map projection which does not preserve the size of areas will distort the perceived density of the dots. NOTE: All dot density maps must be drawn on an equal area map projection. There are two basic types: one-to-one dot density maps (one dot represents one object or count) and one-to-many dot density maps in which one dot stands for a number of things or a value (e.g., 1 dot = 1,000 acres of wheat production). Dot density maps have been popular for 150 years because they are easy to understand and, at a glance, show us intuitively where things clump or cluster. Dot density maps are a simple yet highly effective way to show density differences in geographic distributions across a landscape.

Dot method map