Going Beyond Two Attributes

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Del curso dictado por New York University Tandon School of Engineering

Information Visualization: Foundations

39 calificaciones

New York University Tandon School of Engineering

Information Visualization: Foundations

39 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 111:34

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 and 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 you want to represent an 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 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 these 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 grouped 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,

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.

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.

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 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.

The last one I want to show you is the stacked version of the line chart,

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

So, now these stacked area chart is good

if the only question that you have is how

does the proportion of these values change over time?

But it's not very good for reading the individual values of these categories over time,

and it's 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.

This is clearly problematic because it's not 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 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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