Now, after we've gone

through several examples of data abstraction,

you may ask yourself why is it useful to identify attribute types?

Why as a visualization designer does it make

me better equipped to create effective visualizations?

Well, the idea here is that knowing what kind of attribute and an attribute is,

is going to give you guidance in selecting appropriate graphical visual representations.

This is something that we are going to discuss in much more details in the next lesson,

but here I want to give you a few examples so that

you can grasp the concept a little better,

I don't want this constant to be too

abstract even if we are talking about data abstraction.

So, the first one is an example showing you that in a line chart,

a chart like the one that you are seeing in front of you right now,

it's inappropriate to use unordered attributes.

So, data attributes that are not either ordinal or quantitative,

they don't have an order, okay.

So, in the first plot,

I'm showing something changing over time.

So, on the X axis I have time and

on the Y axis I have quantity that is changing over time.

So this is an appropriate use of a line chart.

But the line chart on the right hand side tries to use exactly the same design,

but on the X axis,

I have a number of categories.

In this case, I have a number of

different burrows in the vehicle collisions dataset that we used previously.

Now, when you look at this chart,

you may think that what this chart is showing

is either something changing over time because you are used

to see a line chart when on the X axis you have time or you may

think even worse if you realize that on the X axis you have categories,

you may think that this chart is actually showing you some useful trend,

but actually there is no particular trend here because one important characteristic

of categorical attributes when they are mapped

into a chart like this one is that they can be reordered in any way,

because there's no intrinsic order.

But if you reorder this chart,

you can get completely different patterns.

So, the patterns are actually not meaningful.

So, that's an example,

think about what I just said,

all this reasoning is based on

the idea of knowing what kind of attributes we are talking about.

Let me give you another example.

So, if we are using a bar chart,

we know that bar charts can accommodate information about

categories and frequencies or statistics associated to these categories.

Another useful thing is what I just said previously,

is that we know that categorical attributes when they are

mapped in a chart like this one can be reordered in any way we like.

Here, I reordered this bar chart according to the values,

the frequencies, or the statistic that is mapped to the height of the bar.

Often, being able to reorder the bars of a bar chart is very useful because it

allows us to more easily read the progression of values, okay.

So, one thing that is useful to know when we have

categorical attributes is that categorical attributes can be reordered.

Note that this is not true if you have a similar chart,

where on the X axis you actually have ordinal or quantitative data,

you can no longer reorder the bars in

a bar chart or other graphical elements if you are using a different chart.

Another example, if in the dataset we have

spatial attributes and let's say that more

specifically we have attributes that describe geographical information,

we know that one visual representation that is available to us is a map.

Now, as we will see in the future,

using a map is not necessarily always

the best solution when we want to visualize spatial data,

but for sure what we know is that if we have spatial information, a spatial attribute,

we know that using

a spatial visual representation like a map is one of the available options,

so, it's very useful.

Last one, say that we want to visualize information that

come from an attribute that we know to be quantitative and diverging.

So, in these specific visual representation,

we have a heat map also called a matrix,

when we have two categorical attributes

and at the intersection of these two categorical attributes,

we have a quantity that is actually diverging,

it's a diverging quantitative attribute.

So, now notice the difference between the first one and the second one.

In the first one on top,

I am showing the value of

these quantitative attribute through

a color scale that uses different levels of intensity,

but exactly the same color U,

U is basically the name of the color the type of color, right?

Whereas in the one below,

I'm using a different color scale.

In this color scale,

we have three main color use,

three main color types.

We have red, white,

and blue, and the zero value is mapped to white,

positive values are mapped to different color intensities of blue,

and negative values are mapped to different color intensities of red.

Now, try to compare these two and figure out,

which cells have negative values and which cells have positive values.

The one on top is very hard to identify,

actually to distinguish between positive and negative,

whereas the second one is very easy to distinguish between positive and negative values.

Once again, knowing the characteristics of an attribute.

In this case, knowing that is add quantitative diverging attributes helps

us make specific decisions on how to visualize this attribute.