This entry is part of the Chart Index, the reference library for the Chart Design Field Guide.

A choropleth map encodes a quantitative variable as the colour of geographic regions. Every county, state, or country becomes a shape; the colour of the shape is the value. The form is intuitive (most readers have used coloured maps since school) and has been the default for spatial data since John Snow's 1854 cholera map.

It is also one of the most-misused forms in modern data visualisation. The pathologies — encoding count instead of rate, using rainbow palettes, letting large sparsely-populated regions dominate the visual weight — are well documented and still everywhere. A clean choropleth is a careful choropleth.

What it is

A choropleth maps geographic regions to filled polygons (using a map projection) and a quantitative variable to colour. Each region is shaded based on its data value; a colour-scale legend translates colour back to value. The reading task is spatial-pattern recognition: where are the high regions, where are the low, what is the geographic structure?

Median household income by region — Australia2026 census · SA3 boundaries · AUD per year · sequential teal

Australia's SA3 boundaries shaded by median income. The eye reads the spatial pattern: capitals are dark, regional areas lighter, remote inland the lightest. The pattern is more communicative than any tabular summary, but it depends entirely on showing a rate (median income), not a count.

When to use it

Choropleths are the right choice when:

  • The data is geographic and ratio-based — rate per capita, percentage, density, average.
  • The reader's question is "where is this high, where is this low, what is the spatial pattern?"
  • The regions have comparable underlying populations or the value is already normalised.
  • The geographic boundaries are meaningful — postal codes for delivery analysis, states for political analysis.
  • The audience expects a map for spatial data.

When not to use it

  • Raw count data. See the callout above. Use a dot-density map, hexbin, or normalised choropleth.
  • Region areas matter more than region populations. Large sparsely-populated regions dominate visual weight even when their actual contribution is small. Use a cartogram (area-distorted map) or a hexbin instead.
  • Very few regions. A choropleth of 5 regions is a bar chart with a map projection. Use bars.
  • Mixed-resolution data. When some regions have many observations and others have few, the values are not equally reliable. Either filter to high-confidence regions or annotate the uncertainty.

Design principles

Always normalise

This is the single most important rule. Shade by per capita, per area, percentage, or another rate — never by raw count. A count of customers per postcode map is a population map; customers per 1,000 residents per postcode is the actual story.

Choose the right scale

Sequential scale for one-directional data (low to high). Diverging scale for data with a meaningful midpoint (above/below average, positive/negative change). Categorical scale only for true categorical data (e.g., region type). Never rainbow.

Use perceptually uniform palettes

Viridis, magma, or LAB-tuned single-hue ramps. The default ramps in spreadsheet tools are perceptually non-uniform — equal data steps produce unequal visual steps, distorting the reading. Use perceptually uniform palettes; the Analysis Report's sequential teal is one example.

Choropleth pathologies — count vs. rate
BY COUNT — DOMINATED BY POPULATIONCitySuburbRuralBY RATE — ACTUAL DATACitySuburbRural
The same data shaded by count produces a population map; shaded by rate produces the actual variable. The visual difference is dramatic.

Show the legend clearly

Every choropleth needs a colour-scale legend showing the value range. Include the minimum and maximum values; for sequential scales, include a midpoint. For diverging scales, make the midpoint explicit and meaningful.

Annotate notable regions

In a national map, a handful of regions usually carry the story. Label them directly — the city with the highest value, the region with the unexpected low. The unlabelled regions provide context; the labelled ones provide narrative.

Consider a cartogram for distortion-sensitive data

If geographic area is distorting the reading (Wyoming and California look comparably important on a US map despite very different populations), use a cartogram — a map where each region's area is proportional to its data value, not its geographic area. The shape becomes recognisable but distorted, and the visual weight matches the data.

Pair with a sorted bar chart

For precise comparison, complement the choropleth with a small inset bar chart of the top-N regions. The map shows spatial pattern; the bars show precise comparison. Two charts, one story.

Anatomy

The Composition of a Choropleth Map
ABCVALUELOWHIGHHIGHEST VALUELOW BUT LARGE REGIONUNLABELLED — CONTEXT ONLY
An anatomical guide

A choropleth's anatomy is geographic polygons + colour-scale legend + selective labels. The reading task is spatial; the scale legend is the precision lookup; the labels provide narrative.

  • Cartogram — area-distorted map. Better when geographic area misleads.
  • Hexbin / H3 map — uniform grid cells. Removes the irregular-region problem.
  • Dot-density map — for raw count data; one dot per N events.
  • Bivariate choropleth — two variables encoded as a 2D colour scale; advanced technique.
  • Value-by-alpha — choropleth modulated by a second variable; covered in a separate entry.

Reading list

  • Snow, J. (1855). On the Mode of Communication of Cholera. The original public-health choropleth.
  • Brewer, C. (2003). Cartographic Design for Color. The science of map colour palettes.
  • MacEachren, A. (1995). How Maps Work. The foundational reference for thematic cartography.
  • Tobler, W. (1973). Choropleth Maps Without Class Intervals? — the case for continuous scales.