![]() ![]() As a result, this map displays more light-shaded colors compared to only 2 with dark shading.īut what happens if you want the count of countries in each class to be close to equal? That’s when you should use a quantile map. However, only 2 countries have more than 20 letters. For example, class 1 has 113 countries out of 176 countries with four, five, six, and seven letters. Then, it divides 20 by 5 and you get an interval ( 20/5=4).Īlmost always, equal interval choropleth maps result in an unequal count of countries per class. In our example, we generated 5 classes but the number of classes is entirely up to you. When you plot each country and its number of characters on a map, it looks like this (the brackets indicate the count):Įqual interval data classification subtracts the maximum value from the minimum value ( 24-4=20). The maximum number of characters is 24, which is the Central African Republic. The minimum number of characters of a country is 4 such as Peru. Class 1: 4 – 8 (113 countries have four, five, six, seven, or eight letters).Ultimately, the question is how do we define those class boundaries or bins? In other words, how do we classify the data into groups?įirst, let’s try dividing classes into evenly-spaced groupings like equal intervals below and see what happens. When we group by classes, there’s less shading and we aggregate the data by group. In this story, we learned how to use GeoPandas library to plot choropleth maps of India to describe the state-wise distribution of English speakers as per a 2019 survey. So this is why we use data classification. Which country belongs to which group? It’s hard to tell because there are so many colors to differentiate each one. As the letter count increases, the shading gets darker. If you plot out 4 to 22 characters, it will have a lot of colors.įor example, the four-letter countries are the lightest shades of green. Whereas, Bosnia and Herzegovina has 22 characters.Mali, Cuba, Peru, and others are four letter countries.In this example, we count the number of letters in country names. What’s changing is how we classify the data. The most important thing you have to realize is that for each of these choropleth maps we create, we use the same data. Less informed decisions due to equal intervals Tends to create classes with varying countsĬonsiders data distribution for class ranges Intervals may result in uneven class sizes Useful for reducing extreme values’ impactĪdjusts intervals around natural clusters Suitable for data with uniform distribution Here is a breakdown of the three most common types of data classification methods: Aspectĭivides data into equal numbers of data pointsįinds natural groupings based on data distribution But it can also include the specific goals of your analysis and the visual representation you want to achieve. Although each classification method has its strengths and weaknesses, the choice should be based on the data’s distribution. ![]()
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