What are the best techniques for cleaning data for choropleth maps?

1 Checking data sources

Before you start mapping, you need to verify the quality and reliability of your data sources. You should check the date, origin, format, and documentation of the data, and compare it with other sources if possible. You should also ensure that the data matches the geographic boundaries and level of detail that you want to use for your map. For example, if you want to map data by county, you need to make sure that your data source has the same county definitions and codes as your map layer.

2 Handling missing values

Missing values can cause problems for choropleth maps, as they can create gaps or distortions in the color scale. Therefore, it is important to identify and handle missing values in your data before mapping. Depending on the context and purpose of your map, there are various ways to deal with missing values, such as deleting or excluding the rows or regions with missing values, replacing them with the mean, median, mode, or zero of the variable, imputing them using a statistical method or a spatial interpolation technique, or indicating them with a different color or symbol on the map.

3 Standardizing data values

To make your choropleth map more meaningful and comparable, you should standardize your data values according to some common measure or scale. For example, if you want to map population density, you should divide the population by the area of each region. If you want to map percentages, you should ensure that they add up to 100% for each region. If you want to map rates or ratios, you should use a consistent denominator or base. Standardizing data values can also help you avoid outliers or extreme values that can skew the color scale or the perception of the map.

4 Choosing a color scheme

When creating a choropleth map, the color scheme is one of the most important aspects to consider, as it conveys the information and the message of the map. You should choose a color scheme that is appropriate for the type and distribution of your data, as well as for the audience and the medium of your map. Generally speaking, sequential colors should be used for continuous or ordinal data, such as temperature or income. Meanwhile, diverging colors are best for data with a meaningful midpoint or deviation from a norm, such as change or difference. Qualitative colors are more suitable for categorical or nominal data, such as regions or groups. Lastly, make sure to use colors that are perceptually uniform, contrast well, and are accessible for color-blind or low-vision users.

5 Testing and validating your map

After you have cleaned your data and created your choropleth map, it is important to test and validate it to ensure accuracy, clarity, and effectiveness. Check for any errors, inconsistencies, or biases in the data or the map and correct them if necessary. Evaluate the map from your intended audience's perspective and ask for feedback from others. Consider questions like: does your map show what you want to show? Does it answer the question or problem you have? Does it use the best data and design for your purpose? Does it communicate your message well? Does it avoid misleading or confusing your audience? Following these techniques can help you clean your data and create a choropleth map that is informative, attractive, and trustworthy.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?