Let’s start off with how you can visualize data tied to an area, or often referred to as a polygon map. In the picture below you can see that I have a map with most of the countries in the world and I’m using colors to indicate which OICA region they belong to. This data might not give the biggest insights in the world, but it shows how easy it is to use colors to indicate grouping of data. I could also use color to indicate a measure on top of the area, such as sales per region.
After color there is not that many more methods to use to encode data onto the polygon. We can for example not change the size or orientation of the polygon as it represents a physical area. But what we can do is to introduce another representation on top of the area and then encode additional information on that location. In this case the easiest method would be to create a point location within each area, such as calculating the center position of the region. This works well if each area only has one polygon, but if it for example is a country with many islands you would have to pick the largest area and then calculate the center point of that one. Or your point may end up in the sea!
Below is an example where I’ve put a position in each area and I’m using and an additional color scale to indicate which trading block each country belongs to, on top of the OICA region. For the point you can of course use any of the methods I outlined in my previous blog post on point data such as shape, size or even a chart.
The second method of showing area is to use a heatmap. This is a great way to visualize data in these scenarios.
- You have too many regions that are detailed and you want to see an aggregated overview.
- You have many points that you want to aggregate together into a custom area.
- You want to show the coverage
In the picture below the charging stations for Tesla are plotted across North America as small points. We then use a heatmap to show which area each charging station can cover. When areas overlap we use colors to indicate the density so that the consumers of our visualization can see coverage area.
When using a heatmap the only viable option for me to visualize the data would be to use color and opacity as there is no guarantee that the areas won’t overlap each other. But you could experiment with creating points or any other type of representation and see how it works.
Hopefully this has inspired you to work more with areas on a map and for my next blog post I will tackle how to visualize flows on a map!
Check out some of the GeoAnalytics examples yourself here!