How to Create a Choropleth Map Online for Free

A choropleth map is one of the most effective ways to communicate geographic data at a glance. Whether you want to visualize election results by county, population density by country, or vaccination rates by region, a choropleth map turns raw numbers into an immediately readable color-coded picture. And with GeoDataTools, you can create one online for free — no coding, no GIS software, and no account required.

What Is a Choropleth Map?

A choropleth map (from the Greek choro, meaning "area or region," and plethos, meaning "multitude") is a thematic map in which geographic areas are shaded or colored in proportion to a statistical variable. The darker or more saturated a region appears, the higher (or lower, depending on the scale) its value for the chosen metric.

Choropleth maps appear everywhere in public data communication:

• Election maps — showing vote share or winning party by state, county, or constituency
• Demographic maps — illustrating population density, median income, age distribution, or unemployment rates by region
• Public health maps — tracking COVID-19 case rates, vaccination coverage, or disease prevalence by country or district
• Environmental maps — depicting air quality index, deforestation rates, or average temperature anomalies by administrative unit

The key characteristic of a choropleth map is that the coloring is tied to a specific numeric or categorical attribute of each area — not to individual points or events. This makes it the ideal format for comparing a single measurement across many geographic units at once.

What You Need to Get Started

Creating a choropleth map requires two ingredients: a geographic boundary file and a dataset with values you want to visualize.

1. A GeoJSON file with region boundaries. GeoJSON is the standard open format for representing geographic features in a web browser. Your GeoJSON must contain polygon or multipolygon features representing the regions you want to color — countries, states, provinces, census tracts, or any other administrative unit. Each feature must have a unique identifier (such as a country code, FIPS code, or region name) that can be matched to your data values.

2. Data values attached to each region. The simplest approach is to embed your data directly in the GeoJSON properties. For each feature, add the numeric or categorical attribute you want to map as a property. For example, a country feature might look like this:

{
  "type": "Feature",
  "geometry": { ... },
  "properties": {
    "name": "Germany",
    "iso_a2": "DE",
    "gdp_per_capita": 48717
  }
}

If your data is in a spreadsheet, you can join it to an existing boundary GeoJSON using a shared key column (such as ISO country codes), or use the converter tools on GeoDataTools to merge a CSV with geographic boundaries. Once your data values are stored as feature properties, you are ready to create your choropleth map.

If you do not already have boundary files, see the section below on free data sources, or read our guide on free shapefile data sources for a detailed overview of where to download high-quality geographic boundaries.

Step-by-Step: Creating Your Choropleth Map on GeoDataTools

Once your GeoJSON file is ready, creating the choropleth map takes only a few minutes in GeoDataTools.

• Step 1 — Open the app. Navigate to geodata.tools/app in any modern browser. No account or login is needed.
• Step 2 — Load your GeoJSON file. Drag and drop your .geojson file onto the map canvas, or click the upload button to browse your local files. The file is processed entirely in your browser — nothing is sent to any server.
• Step 3 — Open the color styling panel. Once your file is loaded, open the Layer Style or Color Styling panel. This is where you configure the choropleth visualization.
• Step 4 — Select the data property to map. From the property dropdown, choose the numeric attribute you want to visualize — for example, gdp_per_capita, case_rate, or vote_share. GeoDataTools will automatically read the range of values across all features.
• Step 5 — Choose a color scale. Select from a range of built-in color palettes. Sequential palettes (light to dark) work well for most data. Diverging palettes (two contrasting colors meeting at a midpoint) are ideal for data with a meaningful center value, such as percentage change or deviation from average.
• Step 6 — Set the number of classification bins. Choose how many color classes to use (typically 5 to 7). More bins give finer resolution; fewer bins make the map easier to read at a glance. GeoDataTools supports common classification methods such as equal interval, quantile, and natural breaks (Jenks).
• Step 7 — Inspect and refine. Click individual features on the map to see their exact attribute values in a popup. Adjust the color scale or classification method as needed until the map communicates your data clearly.

Choosing the Right Color Scale

Color scale selection has a direct impact on how accurately your audience reads the map. A poor color choice can create false impressions of patterns that do not exist in the data.

Sequential palettes progress from a light neutral color to a dark saturated hue. They are appropriate for data with a natural order where all values move in one direction — such as population counts, income levels, or case rates. Examples include light-to-dark blues, greens, or oranges.

Diverging palettes use two contrasting hues that diverge from a neutral midpoint. They are the right choice when your data has a meaningful center value — for example, a map showing percentage change relative to a baseline (positive values in one color, negative values in another), or a political map showing how far each district leans toward one party or another. Common diverging palettes include red-white-blue and purple-orange.

Colorblind-friendly palettes are an important consideration in public-facing maps. Roughly 8% of men and 0.5% of women have some form of color vision deficiency. The most common form is red-green color blindness, which makes red and green hues appear similar. To ensure your map is accessible to all viewers, prefer palettes that rely on differences in lightness and blue-orange or purple-orange contrasts rather than red-green pairs. The ColorBrewer palette library, which is widely used in GIS and built into many mapping tools, was specifically designed with colorblind safety in mind.

Avoid using rainbow (spectral) color scales for quantitative choropleth data. Rainbow scales introduce artificial boundaries and perceptual discontinuities that can mislead viewers about the underlying data distribution.

Common Choropleth Map Mistakes

Even experienced mapmakers fall into a few predictable traps with choropleth maps. Being aware of them will help you produce more accurate and honest visualizations.

• Using raw counts instead of normalized rates. A large country or region will almost always have more total cases, more people, or more events simply because it is bigger. Always normalize your data — use rates per 100,000 people, percentages, or densities per square kilometer — before mapping, unless the absolute count is the precise point you want to make.
• Ignoring area distortion. In a standard Web Mercator projection, Greenland appears larger than Africa, even though Africa is about 14 times bigger. When mapping data by area, consider using an equal-area projection so that visual weight corresponds to actual geographic size. For global datasets, the Equal Earth or Robinson projection is a better choice.
• Poor bin selection. The number and placement of classification breaks can dramatically change how a map looks and what story it tells. Always check whether natural breaks, quantile classification, or equal intervals best reflect your data distribution. Hiding an extreme outlier inside the highest or lowest bin can misrepresent the full range of values.
• Too many or too few classes. Using only two or three classes oversimplifies the data; using ten or more classes produces a map that is impossible to read without the legend. Five to seven classes is the conventional sweet spot for choropleth maps.
• Missing data shown as a color instead of a pattern. Regions with no data should be displayed with a distinct visual treatment — typically a light gray fill and a clear "No data" label in the legend — so readers do not confuse absent data with a low value.

Free Data Sources for Choropleth Maps

If you need geographic boundary files or attribute datasets to create your choropleth map, the following are reliable, freely available sources:

• Natural Earth (naturalearthdata.com) — High-quality global boundary files at 1:10m, 1:50m, and 1:110m scale. Includes country polygons, admin-1 (state/province) boundaries, and populated places. Available as Shapefile or GeoJSON.
• US Census TIGER/Line (census.gov) — Comprehensive boundary files for US states, counties, census tracts, ZIP code tabulation areas, and congressional districts. Joined with Census demographic data, these are the foundation of most US choropleth maps.
• Humanitarian Data Exchange (HDX) (data.humdata.org) — UN OCHA's open data platform with administrative boundary files for nearly every country in the world, particularly strong for developing countries and humanitarian response zones.
• Eurostat GISCO (ec.europa.eu/eurostat) — Official European Union boundary files at NUTS 0, 1, 2, and 3 levels, along with a wide range of socioeconomic datasets that can be directly joined for EU-focused choropleth maps.

For further guidance on finding and downloading geographic datasets, see our guide on free shapefile data sources.

Exporting and Sharing Your Map

Once your choropleth map looks the way you want it, GeoDataTools gives you several options for exporting and sharing your work.

• Export as GeoJSON. Download the styled GeoJSON file to use in other applications, embed in a web map with Leaflet or Mapbox GL JS, or archive for future use.
• Export as PNG or SVG. Capture a static image of your map for use in presentations, reports, or social media. SVG export preserves vector quality for print or further editing in design tools.
• Share a link. Generate a shareable URL that encodes your data and style settings, so collaborators or readers can open the exact same map view without needing to upload any files themselves.

For additional format conversion workflows — such as converting your data between GeoJSON, Shapefile, KML, and CSV — visit the converter tools hub on GeoDataTools. All conversions run in your browser and are completely free.

Choropleth maps are one of the most persuasive tools in the data storyteller's toolkit. With the right boundary file, clean normalized data, and a well-chosen color scale, you can communicate geographic patterns that would take paragraphs of text to explain. Open GeoDataTools and start mapping your data today — no code required.