As part of a piece of coursework, I’ve created a map visualisation using Tableau to show the number of Power Plants per sq. km per country globally.
This is using the Global Power Plant Database for Power Plant information:
As well as the World Bank Surface Area dataset for country surface areas:
I’ve created two versions of this; the first colours countries simply based on the number of power plants per sq. km:
The second of these uses the logarithm of the number of power plants per sq. km:
The second of these definitely has a more visible range of values (the first looks like a map with the UK highlighted in brown), although I’m concerned that with the logarithm, the map has less meaning to it - the data it represents isn’t anything meaningful to a person. It could be seen as being misleading as well, and the key has less meaning also.
Is there a better way this information could be portrayed, avoiding the issues that certain countries have vastly higher values than others (primary issue with the first map) and that logarithms are misleading and non-intuitive (primary issues with the second map)?
(The following information was required to be included for the coursework:
• Visual Design Type: Map
• Name of Tool: Tableau
• Country: All countries present in power plant dataset, bar those lacking area data in the surface area dataset (see Data Preparation)
• Year: 2000 – 2018
• Visual Mappings: Natural logarithm of the number of power plants per square kilometre mapped to colour of each country. The actual number of power plants per square kilometre can be viewed through a tooltip; using this as the colouring made the UK the sole brown area, with the rest of the map in blue, hence the need for logarithmic colouring.
• Unique Observation: the UK has many power plants for its area; Mongolia and various African countries have very few power plants for their area.
• Data Preparation: Finding the number of power plants per square kilometre, and then finding the logarithm of this. The surface area of countries dataset is missing data for certain countries, such as Sudan.