Example of Explanatory Visualization

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En provenance du cours de New York University Tandon School of Engineering

Information Visualization: Foundations

7 notes

New York University Tandon School of Engineering

Information Visualization: Foundations

7 notes

Cours 1 sur 4 dans Specialization Information 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.

À partir de la leçon

Introduction to Information Visualization

Information Visualization Definition1:07

Visualization Example: Introductory Demo with Tableau2:05

Key Concepts and Definitions0:51

The InfoVis pipeline (Diagram of Data Visualization Process)1:33

Key Concept: Computer Based Graphical Representations and Visualizing Abstract Data3:38

Key Concept: Interactivity1:10

Key Concept: Amplifying Cognition6:20

Example of Amplifying Cognition: "The Game of 15"3:55

Why Visualize Data?2:56

Why Use Visualization?3:06

Example of Explanatory Visualization4:01

Examples of Exploratory and Confirmatory Visualization6:02

Examples of Explanatory Visualizations and Tools3:11

Why Use a Graphical Representation?5:56

Problems with Summary Statistics2:56

Why Use Computers to Visualize Data?2:52

Why Use Interaction?2:25

Assessing the Quality of a Visualization6:39

Rencontrer les enseignants

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

In this example, we use an article that comes from The New York Times.

The New York Times is particularly famous for

using very clever and very well-designed data visualizations in

their articles to explain visually

the concepts that are described verbally in the article.

In this specific case,

I've selected an article whose title is

"How Americans Think About Climate Change, in Six Maps".

This is by Nadja Popovich.

John Schwartz, and Tatiana Schlossberg.

And it's been published on March 21st, 2017.

So, let's see.

So the idea here is to show data,

to talk about data that has been released by

the Yale Program on Climate Communication

and that gives a detailed view of public opinion on global warming.

The first chart that they show is a map.

And in this map,

what is shown is the percentage of adults per congressional district

who support strict CO2 limits on existing coal-fired power plants.

And as you can see,

there is quite a lot of support and this support tend to be higher on

the west coast and there is also some concentration up here in the east coast,

New York State and in its neighborhood.

Okay, so then, the journalist describes

some of these findings and go to the next finding.

The next one is,

most people think that climate change will

harm Americans but they don't think it will happen to them.

And there is another visual representation.

Again, two maps showing the percentage of adults per country who think, on the left,

global warming will harm people in the United States,

or global warming, on the right,

will harm me personally.

And what is really interesting is that you can see

a big difference between these two maps.

There are way, way fewer people that think that global warming will harm them personally.

But the same people think that global warming is going

to harm someone in the United States, let's say, globally.

So that's a very interesting comparison.

And as you can se, the two visualizations

here are communicating this information very, very directly.

The next one I want to show you is the one about Texas and Florida.

So here, the authors focus on two specific states.

Texas and Florida are vulnerable to

climate change but residents are split on how much to worry about it.

And again, we have percentage of adults per

county who are at least somewhat worried about global warming.

And here, you can see how these information is distributed in Florida.

And as you can see, the percentage is way,

way higher in the area around Miami and Keys.

And we have a similar trend in Texas.

The closer we are to the coast,

the more worried people are.

So once again, this is an example of explanatory visualization.

These interesting and beautiful charts and maps are used in

an article to explain these complex concepts that are extracted from data.

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