Thematic mapping techniques
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When approaching data visualisation, we need to make sure the correct tools are being used. Most importantly, think about the best way to represent and convey the message that the data is trying to tell. Making a map when a chart or graph would suffice could lead to the data being interpreted incorrectly.
The art of map making (or cartography) is a form of data visualisation and there are a number of mapping techniques we can use to bring data to life. Thematic mapping is how we map a particular theme to a geographic area. It tells us a story about a place and is commonly used to map subjects such as climate issues, population densities or health issues.
When creating a thematic map, it is important that we fully understand the data we are planning to depict and the consequence of representing this data incorrectly. Maps can sometimes misinform people, so choosing the correct map technique is crucial.
A dot density map is a map that uses a dot symbol to represent a feature. For one-to-one dot density maps, each dot is equally sized and each one represents the recording of a single thing. Each dot should be depicted in its correct spatial location to avoid the data being misrepresented and the message lost.
For one-to-many dot density maps, each dot depicts more than one recording of a thing and the amount each dot represents is determined by the cartographer. Dots can be randomly positioned and do not necessarily represent the exact spatial location of something. When creating a one-to-many dot density map, it’s important to include a legend indicating the value that one dot represents.
You can map raw data or simple counts
Shows density and distribution across a wide area
Gives a good visual representation of variations across the data
Clustering can make the data difficult to interpret
Areas with no dots can give a false sense of emptiness
Geography can become hidden
Dots on a one-to-many dot density map can be inferred as a single location of something
This method was used to great effect in the 19th Century when English physician John Snow mapped the location of cholera outbreaks. The map was created to help understand the pattern of cholera spread in the 1854 Broad Street cholera outbreak. Snow used this as an example of how cholera spread via the fecal-oral route through water systems as opposed to the miasma theory of disease spread.
A proportional symbol map uses map symbols that vary in size to represent a numeric variable. For example, circles may be used to show the location of cities within your map where each circle is sized proportional to the population of the city. This is one of the best methods for recording instances of something that falls within a geography.
Large quantities of data can be interpreted quickly
Useful for visualising differences between many places
Visually appealing
Easier to extract actual numbers than a dot density map (legend required)
Can use raw data (totals or counts) and standardised data (ratios or percentages)
Smaller geographic regions are not overlooked
As values get bigger symbols can begin to overlap
Size of symbols can obscure location
Map readers do not always estimate area of symbol well
A choropleth map is a map where geographic areas are coloured or styled in relation to a value. This technique is useful when visualising how a measurement varies across a geographic area. This is commonly used when looking at vote totals by a political party.
Easy to understand
Depicts spatial distributions of data well
Visually effective
Works well at virtually all scales
Map assumes the whole geographic regions has the same value when in fact they may not
Small areas can get lost and bigger areas can appear more important
May not work in black and white
Not suitable for raw data - normalise!
A heat map is a favourite amongst many and is used to represent the density of data in gradients of colour. Through the creation of a hot spot, it allows you to quickly identify areas for further analysis. They are typically used to depict things like crime hot spots or temperature.
Hot spots in data can be quickly identified and analysed further
Works well for temperature and pollution data
Excessive use of colour can affect the legibility of the map
Does not always depict data distribution very well
A standard cartogram is a type of map where the shape and size of regions are distorted based on a data value. In an equal area cartogram however, every region is represented by the same shape of the same size, giving equal representation to each area. The spatial relationship between areas remain as close as possible to reality and places which are located adjacent in reality, are adjacent on the cartogram. With this method, the use of colour is important and many people choose to add labelling stating the actual values.
Visually appealing
Allows for the addition of depicting extra data
Regions are all the same size – no risk of small regions getting lost
Each area can be labelled equally
The location is not 100% accurate
Can loose familiarity of the shape of overall area
Ordnance Survey’s Data Viz team have created their own equal area cartogram for Great Britain and the United Kingdom. You can download them in a range of formats and can find out more information here.
Summarising (or binning) data into a grid, whether it’s a hex grid or a square grid, allows data to be represented as an aggregated summary in equally sized areas. This not only allows for an easy comparison between different locations but also removes the area bias created by irregular geographies such as census boundaries.
Gridding data can also be an effective way to aggregate and summarise multiple complex datasets relating to an area. You can read more about the benefits of a gridded approach to geospatial analysis here: Approaching geospatial analytics with gridded data (arcgis.com).
· Visually appealing
Visually appealing
Easy to understand and interpret
Depicts spatial distributions of data well
No spatial bias and small areas are not lost
Can vary cell size to fit your data
Works well in 3D
Can loose familiarity of the shape of overall area
Boundary effects whereby cells around the edge of your area of interest may appear to have a low density (e.g. population appearing low around the coast due to the cell falling mainly over sea).
May not work in black and white
There are many more thematic mapping techniques beyond those described above and we’d recommend you do a bit of research on the ones listed below before you start mapping:
Flow maps
Isarithmic (isoline) maps
Standard cartograms
Chorochromatic (area class) maps
Dasymetric maps
When considering your map technique, it’s important to think about other factors too. How will the map be used? Is it an interactive web map or perhaps a static map? Are the connotations of the colour scheme suitable for the dataset type?
Another important consideration when creating a thematic map is the choice of basemap. This includes deciding what information is needed to provide context to your data and also it is styled to create an effective visual hierarchy so that the message of your map is communicated to users clearly.