Displaying data geographically can be an effective tool to persuade and inform.  This is especially true when it comes to communicating on subjects based on a geographical region or its population (social, political, economic issues and so on).

Data maps are also popular amongst geographers, geologists and other scientists to help them visualise the physical aspects and phenomena that occurs in certain geographical areas.

So if you have data you want to display over a geographical region, then what are some of the options available to you?  Below I will explore some popular data maps, which will improve your knowledge on presenting geospatial data.

Choropleth Maps

One of the oldest and most commonly used form of data map.  Choropleth maps use patterns or differing shades of colour over geographical regions to represent the data. Choropleth maps are good at giving a general view of the data.

Colour scales or the progression of colours in Choropleth maps can vary.  Some choropleth maps have the colour scale blend as a gradient (like the above-right image) and some divide the colours into steps: so 0-10, 11-20, 21-30 for example would be each given a shade. Sometimes the scale is based on just one colour and varies it by hue, while other scales can blend 2-3 colour together.

The above-left map uses a full colour progression (multiple colours) to represent the distribution of categorised data.  A good example of this would be to use each colour to represent a political party, in order to see who has the most votes in each region.

One thing you need keep in-mind with Choropleth maps is that the larger areas/regions are given more emphasis, even if their designated values aren’t in proportion.  Another thing you need to remember is that values often needs to be normalised in order for the it to be accurately represented.  So if you’re displaying data based on a population within a region, then the data needs to be divided per square mile/km.

Bubbles Maps

With this type of data map, circles are displayed over their designated locations with the area of the circles being proportional to the data values.  Because of this, Bubble maps are good for comparing proportions over a map, without the problem Choropleth maps have of larger region shapes being emphasised more.

The downside to Bubble maps is that enormous circles can sometimes overlap and hide other smaller circles and even cover entire regions.

Point Maps

Also known as a dot distribution map, Point maps are a way of displaying data through detecting spatial patterns of the distribution of data over a geographical region.  Point maps are simply constructed by placing points onto a map.

A famous and historic example of a Point map is John Snow’s cholera map, still one of the best examples of Data Visualisation to date.

Snow took a map of the area of London where the cholera outbreak was occurring and plotted points on the cholera death locations.  He discovered from looking the results that most of the points clustered near a water pump on Broad Street at Cambridge Street.  This helped provide clear evidence that cholera was transmitted not through the inhalation of infected air, but through the ingestion of contaminated water or food.

Connection Maps

The best way to describe this data map is Point map but with the points connected to each other by a line.  Connection maps can be used when displaying data to show the connections and relationships between geographical regions and can also reveal spatial patterns.

Flow Maps

This type of data map is used to show the migration of objects or values between locations. You could think of Flow maps as like the previous Connection map, but with magnitudes assigned to them by the thickness of their lines and arrows used to show the migration direction.

A good example of a Flow map is Minard’s map of French wine exports for 1864, which shows the pink “flows” as a sort of metaphor for the flow of French wine across the world.

Chart Combination Maps

Other forms of data visualisation can be combined with a map to represent the data geographically. Common examples of this is the use of small pie charts and simple bar graphs placed in each region. But the ability of comparison is weakened when displayed in this manner.