![]() ![]() So, if we had say, 100 data values, we sort them from lowest to highest. With quantile data classification, we have an equal number of features in each class. So, you could use it but it's not necessarily always going to work. That I'm showing here is that you're going to end up this is when equal interval with something that could work but often doesn't work. So, this is a version of the data up here. Is that, you will make it look as though all of the values are very similar to one another, through either in class one or class two. Notice that, the vast majority of values are in the first two classes, and that could end up biasing the interpretation of your map. Here we have this distribution of values. The problem with it though, is as I've mentioned, is if you have a clustering of values like for example here, let me just clear that. It's a good way to start, to see well if I could do it this way, it gives you a nice simple legend, nice class boundaries that are easy to interpret. If you're showing a map to people that aren't really that fluent in math or that particular dataset, you want to make sure it's really easy for anyone to comprehend, then, you would think that an equal interval would work well and all else being equal. It's easy as I say here for a non-technical audience to interpret. So, you might think, Well, then why don't we just use equal interval of time because it sounds pretty straightforward. So, really these aren't exactly round numbers here, but if you had values, one to 20, 21 to 40, 41 to 60, that kind of thing, you have a difference of 20 for each one of them. So, these are equally spaced apart in terms of the value difference from the top to the bottom. So, if you key into these blue lines here, those are the class boundaries for this dataset. With an equal interval data classification, we're dividing up the data into equal ranges. ![]()
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