A choropleth map colours each region by a value: darker where the number is higher, lighter where it is lower. It is the most common thematic map in the world, used for election results, disease rates, income levels, population density and almost any statistic that belongs to an area. It is also one of the easiest maps to get wrong, because the choice of classes and colours changes the story the map tells. This guide shows you how to make a choropleth map in QGIS step by step, and how to make one that tells the truth.

What Makes a Good Choropleth Map

A choropleth map works when the data is a rate, a ratio or a density attached to an area, such as cases per 100,000 people, income per household or population per square kilometre. It does not work for raw counts, because a large region will always look more important than a small one, even if its rate is lower. Always normalise: divide your count by the population or the area before you map it. The second rule is to use a single-hue or sequential colour ramp, light to dark, because the eye reads a single scale naturally. A rainbow ramp misleads, and a diverging ramp, light in the middle and dark at both ends, is only right when your data has a meaningful middle, such as a change from negative to positive. The third rule is to choose your classes honestly, because the class breaks decide where the dark areas fall.

Setting Up the Choropleth in QGIS

Load your polygon layer into QGIS, and make sure it has a numeric field with the value you want to map, already normalised as a rate or a density. Open Layer Properties, go to the Symbology tab, and change the renderer from Single Symbol to Graduated. Choose the value field, then pick a colour ramp: a sequential ramp such as Blues or YlOrRd is the safe default. Set the number of classes to five, which is the sweet spot for most maps: fewer and the map is too coarse, more and the reader cannot tell the classes apart. Click Classify, and QGIS will divide your data into five classes and colour each polygon. Click Apply and your choropleth appears. Right-click the layer and choose Zoom to Layer if you cannot see it, and add a basemap underneath for context if the polygons float in empty space.

Choosing the Classification Method

The classification method is the most important decision in a choropleth map, and QGIS offers several. Equal interval divides the range into equal-sized classes, which is honest but often puts most polygons in one class when the data is skewed. Quantile puts the same number of polygons in each class, which always looks balanced but can hide real differences. Natural breaks, also called Jenks, finds the natural gaps in your data and groups similar values together, which is usually the best default for real-world data. Pretty breaks rounds the class limits to nice numbers, which makes the legend easier to read. Always try at least two methods and compare: if the story changes completely when you change the method, your data is probably skewed, and you should say so in the map's notes rather than hiding it. For skewed data, consider a logarithmic scale or mapping the data in quintiles and labelling the classes with their actual ranges.

Styling and Labelling the Map

Give the polygons a thin white or light grey border, so the boundaries are visible without competing with the fill colours. Add a clear legend with the class ranges written out, not just the colours, because a legend that says only dark blue and light blue tells the reader nothing. Add a title that says what the map shows and when the data is from, a scale bar and a north arrow if the map will be printed, and a short note under the map stating the data source and the classification method. If the map is for the web, consider adding labels for the largest or most important regions, but keep them sparse: a choropleth with a label on every polygon is unreadable. Export the finished map from the Print Layout, where you can control the page size, the legend position and the resolution.

Common Mistakes to Avoid

The classic mistake is mapping raw counts instead of rates, which makes big areas look important regardless of their actual values. The second is using too many classes, which turns the map into a patchwork nobody can read. The third is using a rainbow ramp, which the eye does not read as a scale. The fourth is ignoring small areas: a tiny district with a very high rate can be invisible on a choropleth, so consider adding a small inset or a table for the outliers. And the fifth is forgetting the data behind the map: always state the source, the date and the classification method, so the reader can judge the map for themselves. A choropleth map is a powerful way to show how a value varies across space, but it is only honest when the data is normalised, the classes are chosen with care, and the legend tells the whole story.