Choropleth Maps: Examples, Best Practices, and How to Build

Choropleth maps are one of the most recognizable ways to get insights from geographic data. They show you how a value, like population density, income, or election results, changes across a region. Chances are, you’ve seen a choropleth map on the news or in a report, providing a powerful snapshot of how something varies from place to place.

This guide is here to demystify choropleth maps for everyone interested in better understanding their data. 

We’ll go over: 

• What these maps are and why they matter.
• How they work and some best practices for designing them well. 
• How to create them in popular tools
• Where their limitations lie. 

If you ever wanted to learn how to turn a dry data set into an easy-to-understand geographic story, you’re in the right place.

What is a choropleth map?

A choropleth map is a thematic map that uses colored or shaded areas to show numerical values tied to geographic regions. Think states, provinces, counties, or countries. The choropleth part refers to the use of colors for signifying information. A region with a higher value might get a darker color; a lower value might appear lighter.

The magic of a choropleth map is its ability to translate abstract numbers into a visual story, highlighting where a variable rises or falls across a map. For example, a data set with average income by state is paired with map shapes using a shared identifier, like a state name or code. The value for each region determines its shade, so you can immediately spot patterns or outliers.

How choropleth maps differ from other map types

Choosing the right map for your data can make a big difference to your story. Choropleth maps are often confused with a few other map types:

• Choropleth map vs heat map: Choropleth maps fill established regions (like counties or states) with colors based on a value. A heat map, on the other hand, colors grid squares or blurs across space to highlight concentrations of events, like crime hotspots. Heat maps aren’t restricted to political boundaries.
• Choropleth map vs symbol map: While choropleth maps use shading of areas, symbol maps display data using shapes or icons, such as circles. The size of the symbol represents the magnitude of the value. Symbol maps are great when you don’t want geographic area to determine how prominent a value appears.
• Choropleth map vs dot map: Dot maps use dots to show instances or counts, where every dot is equal to a certain quantity. The more dots, the higher the count. Dots are excellent for visualizing counts or distributions, while choropleths are better for normalized rates and ratios.

Understanding these differences helps you match your data and message to the most effective visualization.

When (and why) to use a choropleth map

Choropleth maps reveal patterns by geography that can spark questions, answers, or further exploration. Their power is in helping you and your audience recognize differences across space.

Common uses for choropleth maps

These maps thrive in certain scenarios:

• Population density by state or country: Quickly see which areas are crowded and which are less populated.
• Election results and voting patterns: Compare support for candidates or issues by county, state, or district.
• Public health metrics by region: Showcase variables like vaccination percentage or disease rate.
• Sales or performance by territory: Illustrate regions with high customer engagement or sales.

What makes them so effective

Here’s why choropleth maps are widely used:

• They highlight trends you might not spot in tables.
• You can compare regions instantly, no need to scan through rows of numbers.
• They’re familiar to most people, so interpretation is quick and comfortable.

When not to use a choropleth map

These maps aren’t suitable for every use case. Consider alternatives if:

• Your data sets are only raw counts, not normalized by area or population. Larger regions will always look “more important” even if that is misleading.
• You’re comparing very small or vastly different sized regions. Bigger areas dominate the map, hiding the impact of smaller ones.
• You’re displaying variations inside a region, or the precise location of events. Choropleth maps only show aggregate values for whole areas.

Ask yourself if showing data across a spatial area helps answer your question. If you only need exact values, or if your regions are too uneven, a different chart might work better.

How choropleth maps work

Building a choropleth map means taking geographic shapes, adding the right values to them, and using color to express a story. Let’s break down the basic mechanics.

1. Start with geographic boundaries

Every choropleth map begins with a digital file describing the shapes of the regions you want to display. These boundaries could be country outlines, state lines, zip code areas, or any meaningful space division. Standard formats are GeoJSON and Shapefile.

2. Join data to regions

You need a geographic identifier that exists in both your data set and your shapes, like a state name or code. Software uses this column to “join” each value to the right region on the map. If the spelling or coding is inconsistent, the join can fail, so a quick data check is important.

3. Normalize your values

Here’s a step many people miss: don’t use raw counts. Instead, adjust the numbers (normalize) by calculating rates per population, percentages, or densities. This helps you compare regions fairly. For example, using “cases per 100,000 people” or “sales per household” allows you to compare large and small areas equally.

4. Select a color scale and classification

A color scale gives each value (or range of values) a shade. There are two main approaches:

• Classed: Data is grouped into a few ranges, and each range gets a unique color. This simplifies the map.
• Continuous: A smooth gradient applies, every value along the range has a different shade.

The choice impacts how easy it is to interpret your map. A simple scale makes the story clearer, while too many shades can confuse viewers.

Types and variants of choropleth maps

Choropleth maps might seem simple, but there are several variants worth considering. Picking the right type makes sure your map supports your purpose and audience.

• Single-variable choropleth maps: These display just one value, like unemployment rate or percent vaccinated. Most choropleth maps fall in this category.
• Bivariate choropleth maps: Here, you plot two variables simultaneously, using a color grid. One axis (like shade) tracks one variable and the other axis (like hue) tracks the second variable. This helps compare relationships, such as income and education, but can be harder to read.
• Classed choropleth maps: Data values are grouped into three to seven classes, each with a unique color. This reduces noise and highlights major differences.
• Continuous color scale maps: Colors change gradually from low to high values, making it possible to spot subtle transitions.
• Sequential color schemes: A single hue shifts from light to dark, best for variables that go from low to high.
• Diverging color schemes: Two different hues fade into a neutral midpoint. Perfect for data centered around an average, zero, or target value.

Every map type has strengths and weaknesses, so match the variant to your data and what you want your readers to learn.

Design best practices and pitfalls

A choropleth map’s clarity and usefulness depend on thoughtful decisions at every stage. With a handful of principles, and by watching out for common mistakes, you can create maps that encourage learning and insight.

Best practices to follow

1. Normalize, always: This can’t be overstated. Always use rates, percentages, or similar measures so your findings are fair between regions.
2. Choose colorblind-safe palettes: About 8 percent of men and 1 percent of women have trouble seeing some form of color. Choose palettes that all readers can interpret confidently. Tools like ColorBrewer can help.
3. Limit colors and classes: Stick with between three and seven classes. Too many shades and readers will struggle to differentiate regions, losing your message.
4. Build a clear legend: Make sure your legend is easy to understand. Display precise breakpoints and avoid vague terms.
5. Display missing data: Missing values are common. Show these using a neutral shade or pattern. Leaving them blank can cause confusion.

Common pitfalls that reduce clarity

• Letting size dominate: Large regions inevitably stand out. Be ready to help your reader understand that area doesn’t always mean importance.
• Raw totals instead of normalized data: Raw counts don’t account for differences in region size or population. Use ratios or rates.
• Overcomplicating with color: Bright rainbows or too much contrast distract more than they inform. Keep it simple.
• Hiding or ignoring small regions: When areas are too small, even with a different color, they might disappear on the map altogether.
• Not providing context: Without a story or background, a reader might misinterpret your map.

Taking time on these areas pays off: Your map becomes accessible, honest, and actionable.

Making your choropleth map tell a story

A map is just a beginning. You add value by turning it into a clear story your audience can explore. Let’s walk through some storytelling techniques and give practical examples.

Examples of effective choropleth storytelling

• Unemployment rate by state: Suppose each state is colored by its unemployment rate, using a light-to-dark scale. You can guide attention to regions with high unemployment, helping viewers connect local economies to wider trends.
• Average income by county: With this approach, metropolitan counties might show much higher values than rural counterparts. This map can jumpstart explorations into economic inequality or provide a basis for deeper comparisons.
• Customer penetration by territory: A sales map lets you highlight strengths and opportunities. Darker regions could mean high sales, while lighter shades might prompt further regional strategy.

Storytelling tips

• Explain what each color means and how viewers should read the map.
• Call out unusual or important groups of regions, such as clusters of high values, or invite readers to compare their own region to others.
• Pair the map with a few targeted annotations or supporting charts, like bar or line graphs, for added depth.

A map with story-driven context is far more useful (and memorable) than one left to stand alone.

Step-by-step guide to creating a choropleth map

You don’t need advanced programming skills or expensive software to build a choropleth map. Many mapping tools, spreadsheets, and visualization platforms offer this feature, but every successful project starts with proper preparation and a sharp focus on best practices.

Step 1: Prepare your data

• Have the right geographic identifiers: Your data must include a column matching your regions, like state, zip code, or country names/codes.
• Clean your data: Fix inconsistencies and check for spelling issues. Make sure region names or codes are standardized and consistent.
• Normalize your data: Figure out the best way to compare across regions, often per capita, percentages, or another relevant ratio.
• Address missing values: Decide up front how you will communicate missing or unmatched regions. Document this clearly.

Step 2: Choose your mapping tool and import your data

Most major visualization tools include a map type pre-built for choropleths. The general workflow:

1. Import your cleaned, normalized data.
2. Select the choropleth (or filled map) chart type.
3. Assign your location column and value column.
4. Customize the color palette and class breaks as needed.
5. Add a clear legend and labels if helpful.

Step 3: Test and refine your design

• Adjust class breaks to see how they affect the story.
• Test your color choices for accessibility.
• Make sure smaller regions appear clearly, and that the legend aligns with what viewers see.
• Let colleagues or friends test-interpret your map: Their questions or confusion reveal what to clarify further.

Step 4: Create basic choropleths in Excel

Excel provides a map chart feature for regional data. Here’s how you can use it:

• Organize your data with region names and corresponding values.
• Select your data and use the “Map” chart option.
• Adjust the color scale and formatting but remember: Excel's mapping tools have limited classification and palette customization. For published maps or more control, you may require specialized tools.

Where Excel shines is simple, quick comparisons at a high level. If your project is public-facing or needs specific visual standards, you may want to switch to dedicated geographic visualization software.

Step 5: When to consider advanced tools

If you want more control, like custom boundaries, interactive legends, or more map projections, turn to GIS (Geographic Information Systems) tools or programming libraries. These let you manipulate every aspect of your map but require deeper technical knowledge.

Limitations of choropleth maps and when to use alternatives

Even the best choropleth maps can’t do everything. Understanding their limits helps you pick the right tool for the job (and avoid misleading your audience).

Key limitations

• Region size bias: Large areas command attention, even if their data value is unremarkable. This can obscure important patterns in more densely populated, smaller areas.
• Imprecise with raw counts: Remember, choropleths are designed for normalized, relative values. If you must show totals, look for other chart types.
• Aggregation hides detail: Choropleths show averages or totals for entire areas only, masking variation within each region.

Effective alternatives

• Proportional symbol maps: Overlay circles (or other shapes), scaled to value, on a map. Great for displaying absolute numbers without visual bias from region size.
• Dot density maps: Show raw counts as dots scattered within each region, providing an immediate, intuitive sense of concentration.
• Tables or bar charts: When precise value comparisons are needed for audiences, a sorted table or bar chart might answer questions more clearly than a map.

Consider your story and your audience’s needs to select the best format.

Conclusion and key takeaways

Choropleth maps empower anyone working with geographic data sets to uncover, highlight, and communicate patterns. They excel when your data involves rates, densities, or normalized metrics that benefit from spatial context.

To make your choropleth maps effective:

• Always normalize your data for a fair comparison.
• Limit color classes and choose accessible palettes.
• Pair the map with context, through annotations, stories, or companion charts.
• Know when a different chart type better fits your story.

Remember, great choropleth maps start with clear questions and careful choices. Use these maps when spatial patterns matter. And remember to support your readers’ learning by making the insights as clear as possible. With thoughtful design, you can turn geographic data into a guided, engaging exploration for any audience.