Quick Summary

Loading...

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

Information Visualization: Foundations

notes

New York University Tandon School of Engineering

Information Visualization: Foundations

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

Graphical Components and Mapping Strategies

Overview: Graphical Components and Mapping Strategies6:33

Marks + Channels1:15

Channels, Part 16:27

Channels, Part 27:41

Examples for Graphical Components3:13

Graphical "Decoding"8:53

Quality of visual encoding: Expressiveness Principle7:01

Quality of Visual Encoding: Effectiveness Principle, Part 110:45

Quality of Visual Encoding: Effectiveness Principle, Part 24:45

Evaluate Visualizations8:13

Using the Principles to Design Visualization4:31

Contextual Components: Legends, Labels, and Annotations4:05

Annotations5:57

Contextual Components: Axes, Grids, Reference Lines7:23

Quick Summary4:28

Rencontrer les enseignants

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

Before we conclude, let me summarize the main ideas that we discussed in this module.

We started by discussing the idea of visual encoding and decoding.

What is visual encoding?

The rules, the methods that transform data into a graphical representation.

What is visual decoding or graphical decoding? Is the opposite.

You observe a visualization and you try to extract out of it the rules,

the mappings that created the visualization out of data.

Then more in details I introduced the idea that every visualization is made

of two main graphical components: Marks and channels.

The marks are the graphical elements that represent the objects, the data items.

And the channels are the graphical properties that encode properties of the data.

What we called data attributes.

Then we said, okay now that we know how to encode and

decode visualization and what the language is,

how do we know whether a given encoding is good or not?

And we introduced two main principles.

The first one is the expressiveness principle

and the second one is the effectiveness principle.

The effectiveness principle is basically the idea that the visual representation

shouldn't communicate information that is not actually contained in the data.

The effectiveness principle is that you should try to use

the most effective channels for the most important information that you want to present.

And then we looked more into what it means for a channel to be effective.

And I presented to you a table or diagram,

that shows you two main parameters of effectiveness.

One is that, regarding the fact that the channel needs to

be appropriate for the kind of information that you are presenting,

in particular you have channels that are

appropriate for quantitative and ordered attributes,

and you have channels that are appropriate for

categorical information, categorical attributes.

Then I also presented the ranking in terms of accuracy.

So we saw that some channels are more effective than others,

more accurate than others at presenting quantitative information,

and some other channels are more or less effective at presenting categorical information.

Finally, we went through a few exercises showing

you how these two principles and the ability to

identify graphical elements and

encoding rules is useful for evaluation and design of visualization.

So it's a very very powerful tool.

With these tools, you can evaluate existing visualizations and you

can design new visualizations or redesign existing visualizations.

Very important tool. Finally, I

concluded with introducing the idea that there are a number of contextual components.

We don't have all only marks and channels,

but typically we have a number of additional components that even if they are contextual,

they are actually really really important and useful for

interpreting and reading values out of a visualization.

In particular, I talked about labels,

legends and annotations as a way to help the reader

interpret the meaning of the objects that are displayed on the graph.

And then I talked about axis,

grids and trend lines that help you

comparing and reading values out of the visual representation.

Explorer notre catalogue

Rejoignez-nous gratuitement et obtenez des recommendations, des mises à jour et des offres personnalisées.

Coursera propose un accès universel à la meilleure formation au monde, en partenariat avec des universités et des organisations du plus haut niveau, pour proposer des cours en ligne.

© 2018 Coursera Inc. Tous droits réservés.

Télécharger dans l'App Store

Disponible sur Google Play