As our understanding of Location Intelligence and its applications across the public and private sector grows, thematic maps are becoming a more critical part of any professional’s toolkit.
Unlike reference maps, which tell us where something is, thematic maps tell us how something is.
Thematic maps pull in attributes or statistics about a location and represent that data in a way that enables a greater understanding of the relationships between locations and the discovery of spatial patterns in the data that we are exploring.
There are a number of visualization techniques and thematic map types that have different applications depending on the type of data that you are exploring and the type of spatial analysis that you are looking to do. The methodology and the type of map that you want to create may be different, for example, if you are exploring global shipping data or voter propensity, or environmental disaster impact.
Let’s take a look at five thematic map visualization techniques that are particularly useful to decision makers, analysts, storytellers, and others who are looking to draw insights from their data, tell a powerful story, or gain a greater understanding of the world around us.
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A choropleth map is a thematic map where geographic regions are colored, shaded, or patterned in relation to a value.
This type of map is particularly useful when visualizing a variable and how it changes across defined regions or geopolitical areas.
For example, a choropleth map is extremely useful when looking at vote totals by political party per county in the United States, as below. In a choropleth map, color can be used to represent distinct attributes or, as in the example below, to represent weight of a value (a strong or weak party vote-share shown as light or dark colors).
Fun Fact: the common use of red and blue to represent Republicans and Democrats respectively, is a modern phenomena. Established during the 2000 Presidential Election, when the protracted debate over results lead to choropleth maps being a staple of political news coverage, institutions gradually settled on the red as republican/blue as democrat color scheme to provide viewers with a common understanding regardless of their preferred news source.
A heat map represents the intensity of an incident’s occurrence within a dataset. A heatmap uses color to represent intensity, though unlike a choropleth map, a heatmap does not use geographical or geo-political boundaries to group data. This technique requires point geometries, as you are looking to map the frequency of an occurrence at a specific point.
Visualizing the intensity of occurrence using a heat map is a technique commonly used when tracking weather and natural phenomena, in which established borders and boundaries are less useful for understanding impact areas. In the heat map below, drought conditions across the United States are visualized based on intensity, giving us a greater understanding of past and potential impact areas.
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A proportional symbol map can represent data tied to a specific geographical point or data that is aggregated to a point from a wider area.
In these maps, a symbol is used to represent the data at that specific or aggregate point, and then scaled by value, so that a larger symbol represents a greater value. The size of each symbol can be proportional to the value you are visualizing or you can set 3 to 5 ‘classes’ of values allowing for comparison and classification of locations.
Proportional symbol maps are extremely useful for clearly telling the story of your data, as in the above map, showing urban populations by country around the world.
Additionally, with 4.5% of all people having some level of color-blindness, a proportional symbol map adds a level of accessibility to your visualization over some of the more color focused options. Our Head of Cartography, Mamata Akella, has also provided some best practices for designing a powerful proportional symbol map.
A dot density map uses a dot to represent a feature or attribute in your data.
Some dot density maps are ‘one-to-one’ in which each dot represents a single occurrence or data point, or ‘one to many’ in which each dot represents a set of aggregated data, for example one dot may represent 100 individuals with a certain attribute. Both of these types of dot density map visualize the scatter of your data, which can provide insights into where instances of an occurrence are clustered.
Fun fact: One of the best known early applications of Location Intelligence was John Snow’s map of cholera patients in London in 1854. A ‘one-to-one’ dot density map, Snow plotted each recorded case of cholera and in an early example of spatial analysis, was able to determine that a high density of cases were clustered around a specific water pump, the source of the cholera outbreak.
More of a technique than a type, if your data has a temporal component (taking place over time), you can transform any of the above visualizations into an animated time-series map. Looking at your data over time can both improve your ability to gain insights and create a stronger and more compelling visual.
The example below visualizes GPS data over a 24 hour period for a subset of cars using a navigation system in the city of Berlin.
Putting your data on an appropriate time scale will allow you to make important business decisions. Mapping foot traffic over the course of a week, for example, may inform hours of operation for a retail location while mapping and animating a century’s worth of sea level measurements can paint a vivid picture on the impact of global climate change.
With many applications from social listening to resource management to demographic projection, animating your data as a time-series map unlocks a new dimension at which to view your data.
As you begin to develop your own thematic maps, it is important to always ask yourself what question you are hoping to answer. By understanding your goals, you will best be able to select which type of map and which techniques to use. To discover how to create thematic maps with CARTO, talk with our Data Specialists.
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