there are different ways of describing informational data.

And typically, there are four classes that are used.

So, the idea is that information or a measurement can be either nominal,

ordinal, interval, or ratio.

So, what does it mean? So, a nominal measurement means that basically,

these information is about a number of categories.

The only operation that we can do with categories is

comparing them to see whether a category is the same as another category.

Then we have ordinal information.

What is ordinal information?

Well, ordinal is very similar to categorical, to nominal,

but the difference is that we can identify an order in the elements,

but we can't really tell anything about how much bigger or

how much lower a given element is compared to another.

So, any ranking is an example of ordinal measurement or data,

or for instance you see when measurements are extracted from surveys,

when we ask people to answer a question about something and they have to specify say,

medium, high, low something,

well, that would be an example of a of an ordinal scale.

Because why is it an ordinal scale?

Well, because we can't really tell what's the quantitative difference

between these elements but we know that there is an order.

Then we have interval and ratio scales.

So, interval and ratio scales are about measuring quantities,

both about measuring quantities.

But the main difference is that interval scales don't have a zero value,

and because of that there are certain operations don't make sense,

like for instance, division doesn't make sense.

What are examples of interval measurements?

Well, for instance percent or temperature are

examples of interval measurements. Then we have racial.

So, for instance measurements like weight

or height of a person is an example of a ratio scale.

So, in visualization, we like to categorize these elements,

these type of measurements in a smaller set.

Let me tell you what the set is first and then why it is useful in visualization.

Well, the set is quantitative information,

sequential information, and categorical information.

So, why do we do that?

Well, because when someone has to decide what is

the appropriate visual representation or

the appropriate visual channel to represent a given piece of information,

it's very important to know the properties of this information.

And in particular, whether this information is about a quantity, it's about a sequence,

or about categories, and that's the reason why we talk about quantitative data,

sequential data, and categorical data.