Choropleth maps are widely used in mapping despite their apparent shortcomings. What are other alternative thematic maps that can convey data more effectively? In this article, we review some of the best thematic mapping techniques out there, their use cases, and the pros and cons of each type.

But first, what is a thematic map?

What Is a Thematic Map?

Thematic maps focus on a specific theme. They pull together relevant information of the subject (Covid cases, election results, income distribution, etc.) and represent the data spatially to understand the relationship between these themes and their geographic locations.

While navigation maps help us find our way from point A to point Z, and reference maps portray features like coastline and terrains, thematic maps focus on a specific theme or subject.

There are a variety of thematic map visualizations that have various user applications, so let’s have a look at the seven most commonly used thematic map types.

7 Types of Thematic Maps

1. Choropleth Map
2. Bivariate Choropleth Map
3. Value-by-Alpha Map
4. Dot Distribution Map
6. Heat Map
7. Cartogram

1. Choropleth Map

The choropleth map is one of the most frequently used maps in geospatial data. With this type of thematic map, we use color to represent statistics proportionally to its location.  For example, let’s look at the unemployment rate by U.S. county. As you can see, choropleths are good at displaying densities using colors.

We use choropleth maps to convey statistical values in different geographical scales, from local to global. However, choropleth maps have some limitations. One particular drawback of using choropleth maps is that geographical spaces aren’t uniform, and thus the displayed results might not portray the right results. For example, large geographic areas (like Texas or California) might dominate the visual.

Make sure to normalize the attribute for the choropleth map (otherwise, the visual is misleading). One way to normalize the choropleth map is to divide the statistical values with the population of the geographic area.

• They clearly display densities (ratios) of quantities using color.

• The visual tends to generalize the data.
• Geographical spaces aren’t uniform so some areas may appear overrepresented.

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2. Bivariate Choropleth Map

Bivariate choropleths are similar to choropleth maps with one exception: Instead of using one variable to display densities, bivariate choropleth maps (as the name might suggest) use two variables at once. This method compares two dissimilar distributions on the same map.

The best use cases for bivariate choropleth maps are when you have two dissimilar attributes that you want to display at once.

• You can visualize two themes at once.
• They’re aesthetically pleasing.

• They can be complex and hard to read.
• It’s hard to have an interactive version of a bivariate map.

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3. Value-by-Alpha Map

Another thematic map closely related to the bivariate choropleth is the value-by-alpha (VBA) map. Value-by-alpha is a bivariate choropleth technique where we consider two variables that affect each other such as election results and population density. The second variable acts as an equalizer for the other variable of interest.

VBA modifies the background color through the alpha variable (transparency). Thus, lower values fade into the background, while higher values rise to the top. VBA maps came into existence to reduce the larger size bias in choropleth maps.

• They display bivariate relationships with classes of more than three.
• They’re aesthetically pleasing.

• They’re complex and sometimes hard to read.
• It’s hard to have an interactive version of the bivariate map.

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4. Dot Distribution Map

A dot distribution map (or dot density map) is a thematic map type that uses dots to display the presence or absence of a feature. Typically, one point is assigned to represent a larger quantity. For example, in the below map, one dot represents 100 indigenous people in Australia.

This map clearly shows the trend or spatial pattern of where indigenous people live across Australia.

Note that the points are mostly generated randomly, which means the points are arbitrarily placed inside the geographic area. For example, if you have a rectangle as your geographic area, the points can have different patterns depending on the random selection. The smaller the geographic area the better. The example I used from Australia above uses cities as the geographic area. The map, however, uses the region boundaries more appropriately for visual purposes. Data scientists should interpret this accordingly.

• The best way to visualize spatial patterns.
• An effective way to represent different categories using colors.

• Randomly generated points might differ from one iteration to another.
• If the map doesn’t have borders drawn, we don’t know exactly where these points are located.

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Graduated symbol maps are an alternative to choropleth maps. The difference between them is that, instead of using color to indicate feature attributes or statistics, the graduated symbol map (or graduated circle map) uses points. The data is likely stored in polygons and then converted to centroid points for these areas. We use this type of map when we intend to visualize quantity rather than density (as we do with the choropleth map).

We divide the feature attribute quantities into classes using different classification techniques like quantile, natural breaks and equal intervals. For example, the graduated symbol map above separates the population for some cities into five classes. Each of these classes has a specific dot size depending on the classification of the population in that city.

• This does a better job showing raw quantities (rather than densities as we saw with choropleth maps).
• They clearly convey where and how much (quantities).

• They are less exact than distribution maps.
• They need preprocessing to derive centroids.
• Overlapping circles can cause confusion (but you can use transparency to help that shortcoming).

6. Heat Maps

Heat maps display the density of points on a geographic map and can effectively visualize the intensity of the variable through a color scale. A heat map shows hot spots or concentrations of points. This technique is often used when geographic boundaries are not that important.

• Heat maps make it easy to understand relationships between data points and the overall trend.

• If you don’t use the color appropriately, you might affect the visualization’s legibility.
• Color transitions might reflect perceptions that aren’t present in the data.

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7. Cartogram Map

A cartogram map is a thematic map type in which you rescale the size of an area to be proportional to the feature it represents. Therefore, the rescaled size communicates the feature attributes selected. As a result cartograms necessarily distort area sizes.

There are different types of cartograms but the most widely used is what we call contiguous cartograms. For contiguous cartograms, you can maintain the topology but you end up distorting the shape dramatically.

• They’re good at showing numbers (such as a population).