Going Beyond Two Attributes

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Del curso dictado por New York University Tandon School of Engineering

Information Visualization: Foundations

calificaciones

New York University Tandon School of Engineering

Information Visualization: Foundations

calificaciones

Curso 1 de 4 en SpecializationInformation Visualization

The main goal of this specialization is to provide the knowledge and practical skills necessary to develop a strong foundation on information visualization and to design and develop advanced applications for visual data analysis. This course aims at introducing fundamental knowledge for information visualization. The main goal is to provide the students with the necessary “vocabulary” to describe visualizations in a way that helps them reason about what designs are appropriate for a given problem. This module also gives a broad overview of the field of visualization, introducing its goals, methods and applications. A learner with some or no previous knowledge in Information Visualization will get a sense of what visualization is, what it is for and in how many different situations it can be applied; will practice to describe data in a way that is useful for visualization design; will familiarize with fundamental charts to talk about the concept of visual encoding and decoding.

De la lección

Fundamental Graphs and Data Transformation

Overview: Fundamental Graphs and Data Transformation1:48

How to Visualize?3:23

Fundamental Graphs9:10

Alternate Representations Part 112:03

Alternate Representations Part 26:05

Going Beyond Two Attributes6:41

Scatter Plots + Faceting4:51

Data Transformation6:34

Common / Useful Data Transformations - Part 14:20

Common / Useful Data Transformations - Part 25:46

Conoce a los instructores

Enrico Bertini
Associate Professor
Computer Science and Engineering
Cristian Felix
PhD. Candidate
Computer Science and Engineering

Now I want to show you what happens when you are trying to

represent additional information with these graphs.

In particular, if you have additional attributes that you want

to be able to introduce in the representation.

Let's start with the bar chart.

So let's say that in a bar chart now want to represent

information coming from an additional categorical attribute.

In this example that I have here,

I'm using once again the vehicle collision data.

And I'm representing in this case information about what kind of vehicle is,

Is involved in the accident, and what are the main causes for these accidents.

So we have vehicle type, main cause for the accident, and for

each combination of these two categories, how many accidents or

how many people injured we have.

So how do we represent this information with a bar chart?

There are two main designs, the stacked bar chart, and the grouped bar chart.

In a stacked bar chart, this information is represented as follows.

So we have as many bars as the number of categories that

are included in the first categorical attribute.

And as many segments within each bar as the values

that are in the other categorical attribute.

So in this case, for instance, we have vehicle types

represented by the three bars that you see in the stacked bar chart.

And the main causes for collisions represented by the four colored,

sorry, the five colored categories, okay?

The five colored main contributing factors for the collisions.

In the group bar chart, we have exactly the same information,

but represented by a different configuration.

In this case, we have that the same

bar graph is repeated multiple times for

the number of categories that exist for the other categorical attribute.

So one categorical attribute is mapped to the bar chart.

And this bar chart is repeated as many times as the number

of categories that you have in the other categorical attribute.

So what are the advantages and disadvantages of these two graphs?

Well, the stacked bar graph is very good when your

main question is regarding the proportion.

So if it's important for

you to understand what is the proportion of values within each category.

So it's very good to communicate proportions.

Sometimes this is also called part-to-whole information.

Whereas the grouped bar chart is better when the goal is to

compare every single value, one to another.

So now, let me show you alternative designs for

the line charts when we want to represent additional information on top of them.

And once again, let's imagine that we have an additional

categorical value that we want to be able to map using a line chart.

So in this case we have, again, using the vehicle collision data,

we have on the X axis, time, month of the date.

And on the Y axis we have number of collisions.

But now we have an additional attribute.

And this additional attribute here is the borough.

So the borough where these collisions happen.

So how is this accommodated in a line chart?

In this first design, we see that we have separate lines,

each one representing one borough.

So that's one possible design when

we want to represent an additional categorical attribute.

And this is very good because, since we have the lines represented

in the same space, it's easy to compare their values.

The next variation is similar to the previous one, but every

single line chart, every single line, is represented in a separate line chart.

So it's a sort of series of line charts,

one next to the other, one above or below the other.

So that's another option to represent the same data.

And the last one I want to show you is the stacked version of the line chart.

Where every single value, so

the values across the categories are stacked on on top of the other.

So now, this stacked area

chart is good if the only question that you have is

how does the proportion of these values change over time?

But is not very good for

reading the individual values of theses categories over time.

And is not good to compare the values of these categories over time, why?

Well, because the problem here is that since the values are stacked,

the pattern that you see in the lines is affected by the shape

of the line that is below the one that you are observing.

And this is clearly problematic, because

the information that is represented in a stacked area chart like this one,

cannot be consumed the same way you consume the information

that is presented in a line chart, in a regular line chart.

As I said before, because the pattern doesn't really

represent the values that are associated to each line chart.

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