Thematic Mapping With CARTO

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Thematic maps are referred to the maps which are designed for emphasizing the spatial pattern of one or more spatial attribute [1] and showing the distribution pattern of a selected theme; such as population density, family income, maximum daily temperature, etc. They are useful tools in decision making since they can give quick visual summaries about our spatial data.

CARTO, which is an open-source software built on PostGIS and PostgreSQL [2], is an online mapping platform which allows end-users to produce a variety of Web GIS end products, including thematic maps. In CARTO, you can manage the data and create maps through its MapBuilder.  It also allows users to build customized web map applications and interfaces through APIs and programming by its Engine. One of CARTO’s unique aspects is its variety of thematic mapping options (which is extended by Data Observatory). It is an open-source platform and has free license options for academic purposes along with cloud offering.

In order to create any map on CARTO, first, the dataset which will be used to form the map should be defined and connected first. The data can be a CSV file, spreadsheet or can be provided by a link to connect.

Depending on your feature type (i.e. point, line or polygon) or the dimension of the data, the style tab in the CARTO layer menu gives you different options.  Another segment enables you to select the visual representation is Style. For example, for a point feature, you can select a fixed value, or the size of the point will be derived from the value stored in the attribute table (Figure 1).

Figure 1. Style Tab in CARTO Layer Interface
Figure 1. Style Tab in CARTO Layer Interface

Representing Attribute Information With Thematic Maps

An important aspect of thematic mapping is to how to represent the attribute information. Thematic maps are powerful visuals to emphasize spatial variation of one or a small number of geographic distributions, therefore, one should be more careful about especially using polygon data since the large areas might create a bias on the output map. Sometimes normalization of the data is necessary to make an easier comparison. For example, as given in the below figure, when the representation changes from raw totals to percent of the total population, the overall story told by the thematic map becomes more meaningful.

Figure 2. The effect with the Total Female Population for Each Country (Left) and The Percent of the Total Population for a Given Country That is Female (Right) (Source : [3])
Figure 2. The effect with the Total Female Population for Each Country (Left) and The Percent of the Total Population for a Given Country That is Female (Right) (Source : [3])
Different coloring options are also available to pick from any color ramp.  Color as the most powerful element in map symbolization and should be selected in a way not to lead any misperception. It is a general rule of thumb to pick a palette that keeps a uniform level of saturation and lightness for each category.

There are four different classification techniques which help to generate breaks of how the data are going to be classified in the display.  Quantiles classification creates an equal number of hexbins in each class or color. Equal interval creates equal-sized subranges and data is classified based on these subranges. In Jenks (natural break) is a classification method which is used to reduce the deviations within each bin while increasing the average deviation between bins.  Head/tails is a new classification scheme which is more suitable for data with a heavy-tailed distribution. This classification technique breaks the data in a way to best visualize data falling along a long-tail distribution. The details of this technique can be found in [4].


Along with choropleth maps, which is described above, there are other popular thematic maps that can be generated in CARTO such as heat maps, proportional symbol maps, and dot-density maps [5].

Mapping Density Point Features

Density point features in a map can be thematically represented as heat maps. This type of visualization makes it easier to distinguish the density of points independent from the zoom factor.  Heat map option is provided in CARTO as an aggregation option (Figure 3).

Point Aggregation Options in CARTO Layer Interface
Point Aggregation Options in CARTO Layer Interface

Since the output of a heat map is a raster file, resolution in heat maps affects the end visualization which is also closely coupled how the data is interpreted. More details about heat maps can be found in [6].

In CARTO, it is also possible to add animation to your map by using the time attribute. Animated time-series maps can add a temporal dimension which creates a stronger impact on the output temporal map.  Along with an animated time-series map, widgets such as histogram and category bars can give more information about the selected feature on the map interactively. A step by step example is given at [7] on understanding the time-series widget in CARTO.

As an open-source platform, CARTO enables creating dynamic thematic maps with ease.  There are several tutorials to help interested users to get more familiar with CARTO can be found at [8].


[1] Slocum, T., Mc Master, R., Kessler, F., & Howard, H. (2010). Thematic Cartography and Geovisualization (Amazon affiliate link*). 3rd eds.


[3] Cartographic Tips for Thematic Maps

[4] Jiang, Bin. “Head/tail breaks: A new classification scheme for data with a heavy-tailed distribution.” The Professional Geographer3 (2013): 482-494.

[5] Popular Thematic Map Types and Techniques for Spatial Data

[6] Dempsey, Caitlin. Heat Maps in GIS.

[7] Understanding the Time-Series Widget

[8]CARTO Tutorials

* GIS Lounge is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to



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