What is quantile classification in GIS?

Quantile Classification in GIS: Making Sense of Your Maps

Ever looked at a map and wondered how the data is being presented? In the world of Geographic Information Systems (GIS), it’s not just about pinpointing locations; it’s about telling a story with data. And one of the coolest tools for doing that is data classification – grouping those numbers into classes to reveal hidden patterns. Among the different ways to classify data, quantile classification is a real workhorse, especially when you want to show how data stacks up against each other.

Quantile Classification: What’s the Big Idea?

Okay, so what is quantile classification? Simply put, it’s a method that sorts your data into groups, with each group holding roughly the same number of things – whether those things are counties, cities, or anything else you’re mapping. Think of it like dividing a class of students into groups based on height, with each group having about the same number of kids.

So, if you’re mapping, say, the income levels of different neighborhoods and you want to use four classes, quantile classification will try to create four groups, each containing about the same number of neighborhoods. The goal? To give you a sense of how these neighborhoods rank in terms of income.

This approach shines when your data is spread out fairly evenly. It makes sure that no group is empty or too crowded. The real aim here is to highlight the ranking of your data – who’s on top, who’s in the middle, and who’s at the bottom.

How Does it Actually Work?

Here’s the nitty-gritty. The software takes all your data values, lines them up from smallest to largest, and then chops them into the number of classes you want, making sure each class has about the same count. The boundaries between the classes? Those are determined by how your data is distributed.

Let’s say you’re classifying countries by population size using five quantiles. The GIS software will order the countries from least to most populous and then split them into five groups, each containing roughly the same number of countries. The first group would be the least populated, and the last would be the most. Easy peasy.

Why Use Quantile Classification? The Perks

• Ranking Made Easy: It’s fantastic for showing how data values compare. Need to highlight the counties in the top 10% for something? Quantile’s your friend.
• No Empty Seats: It makes sure every class has something in it, so your map looks complete.
• Perfect for Ordinal Data: Got data that has a natural order, like “low,” “medium,” and “high”? Quantile loves it.

The Downsides? Keep an Eye Out

• Hiding the Real Story: It can sometimes mask the actual differences in values.
• Similar Stuff, Different Groups: Things that are almost the same might end up in different classes, making them seem more different than they are.
• Different Stuff, Same Group: Conversely, things that are wildly different could get lumped together, downplaying their differences.
• Potentially Misleading Maps: If your data isn’t evenly spread, the map can be a bit misleading.

When Should You Use It?

Quantile classification works best when:

• Your data is pretty evenly spread out.
• You want to emphasize the ranking of things.
• You want to avoid empty classes.

But be careful! If your data is all over the place, quantile classification might not be the best choice. It could paint a picture that isn’t quite accurate.

What Are the Alternatives?

There’s a whole toolbox of classification methods in GIS. Here are a few:

• Equal Interval: Divides the range of values into equal chunks. Great for things like temperature readings.
• Natural Breaks (Jenks): Finds natural groupings in your data. Perfect for unevenly distributed data.
• Standard Deviation: Shows how much values vary from the average.
• Geometrical Interval: A mix of equal interval, natural breaks and quantile.
• Manual Interval: You get to set the class ranges yourself.

How to Make It Better?

Want to minimize the distortion? Here’s what to do:

• More Classes: Using more classes can help show finer differences.
• Know Your Data: Always look at how your data is spread out before using quantile.
• Consider Other Methods: If your data is wonky, try a different classification method.

The Bottom Line

Quantile classification is a solid tool for visualizing data and showing rankings. Just remember its limitations, especially with uneven data. By understanding your data and what you want to show, you can make smart choices about when and how to use quantile classification to create maps that tell a clear and compelling story.