Graphical heuristics: Data-ink ratio (Edward Tufte)

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Del curso dictado por University of Michigan

Applied Plotting, Charting & Data Representation in Python

1949 calificaciones

University of Michigan

Applied Plotting, Charting & Data Representation in Python

1949 calificaciones

Curso 2 de 5 en SpecializationApplied Data Science with Python

This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data. This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.

De la lección

Module 1: Principles of Information Visualization

In this module, you will get an introduction to principles of information visualization. We will be introduced to tools for thinking about design and graphical heuristics for thinking about creating effective visualizations. All of the course information on grading, prerequisites, and expectations are on the course syllabus, which is included in this module.

Introduction4:29

About the Professor: Christopher Brooks1:25

Tools for Thinking about Design (Alberto Cairo)8:36

Graphical heuristics: Data-ink ratio (Edward Tufte)4:44

Graphical heuristics: Chart junk (Edward Tufte)5:11

Graphical heuristics: Lie Factor and Spark Lines (Edward Tufte)3:47

The Truthful Art (Alberto Cairo)8:40

Conoce a los instructores

Christopher Brooks

Edward Tufte is a seminal figure on how to design visual displays of quantitative

information.

In fact, this is the title of my favorite book of his, and

I highly encourage you to check out his work.

In this book, he introduces two interesting graphical heuristics,

the data-ink ratio and chart junk.

First, what's a heuristic?

A heuristic is a process or rule that is meant to guide you in decision making.

Is by definition not known to be optimal or perfect, but to be practical in nature.

Heuristics are meant to be followed until you've a reason to deviate from them.

Tufte's first graphical heuristic is data-ink ratio.

Tufte defines data-ink as the non-erasable core of a graphic.

The non-redundant ink arranged in response to variation in the numbers represented.

In other words, the data-ink is essential to the sense-making process for

a given variable.

Tufte defines the data-ink ratio as the amount of data-ink

divided by the total ink required to print the graphic.

Now I don't think that he's actually suggesting we measure the amount of ink

laid down on the page, but instead is suggesting that we remove those

elements which don't add new information to the graphic.

Perhaps one of the best known examples showing data-ink reduction was done by

Darkhorse Analytics, an information, visualization, and

design company in Edmonton, Alberta, Canada.

They've put together four examples of how the data-ink ratio can be approved.

And I'm going to link to those in the next reading assignment.

But I'm going to walk through the first one of those here.

We start with the table of the data about food items and

the number of calories in those items.

The first step is to remove unwanted background imagery

since it provides no value to understanding the chart.

We also remove the gray background behind the bars since it provides no

conceptual value.

Then we get rid of the redundancy throughout.

This includes getting rid of the legend,

since each bar is labeled directly along the x-axis.

The title in the y-axis labels are trimmed, since there's plenty of reference

throughout the image to this being about calories for food items.

This leaves us with a much smaller image.

There are some heavy lines forming borders which add no value, so

we can remove them too.

Color is a tricky item to include in charts and graphs.

We won't be going into depth on color theory in this course.

But color is a challenge when trying to engage with persons who might

be color vision deficient or color blind.

There are some tactics to work around.

For instance, it used to be best practice to replace coloration with patterns,

sometimes called hatching in graphics.

This brings its own challenges, which we'll see in a bit.

Instead, the solution is almost always to remove all color, except for

one which you can use as emphasis and link to that color in your text.

Someone then viewing a chart out of context would still get the sense

of emphasis without being overwhelmed by a rainbow of data.

So let's drop colors except for bacon,

with the presumption that this text is clearly referenced in the data.

Three-dimensional bars and

drop shadows can go as well, since these don't add extra value.

We could also drop the bolding throughout.

There's still a lot of gridwork left in this image and

it's a bit unclear what the value of this gridwork is, so we're going to drop it.

Now grids can be valuable, but they're often just a distraction.

For the moment though, study the image.

What's the difference in the number of calories between bacon,

our data point of interest, and say, potato chips or chili dogs?

Just removing the lines doesn't make it easier to read.

But the values of the data are pretty simple here.

So let's directly label each bar in the graphic.

Now, how big is the difference between bacon and

potato chips or bacon and chili dogs?

Certainly, you have to do a bit of math, but you can still get a quick estimate by

glancing at the graphic, and an exact value through comparing numbers directly.

And there you have it.

By increasing the data-ink ratio, we have made the graphic not only simpler and

more readable, but increased the amount of information the viewer sees.

I encourage you to check out the other examples from Darkhorse Analytics

linked in the next reading.

As they've done this clean up for

a variety of different visuals including maps.

We'll continue our exploration of Tufte's heuristics by looking at chart junk

in the next lecture.

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