2.2.1. Glyphs (Part 2)

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来自 University of Illinois at Urbana-Champaign 的课程

数据可视化

689 个评分

University of Illinois at Urbana-Champaign

数据可视化

689 个评分

课程 1（共 6 门，Specialization Data Mining ）

这这一课程中，我们将学习数据挖掘的基本概念及其基础的方法和应用，然后深入到数据挖掘的子领域——模式发现中，深入学习模式发现的概念、方法，及应用。我们也将介绍基于模式进行分类的方法以及一些模式发现有趣的应用。这一课程将给你提供学习技能和实践的机会，将可扩展的模式发现方法应用在在大体量交易数据上，讨论模式评估指标，以及学习用于挖掘各类不同的模式、序列模式，以及子图模式的方法。

从本节课中

Week 2: Visualization of Numerical Data

In this week's module, you will start to think about how to visualize data effectively. This will include assigning data to appropriate chart elements, using glyphs, parallel coordinates, and streamgraphs, as well as implementing principles of design and color to make your visualizations more engaging and effective.

2.2.1. Glyphs (Part 1)4:32

2.2.1. Glyphs (Part 2)6:30

2.2.2. Parallel Coordinates8:34

2.2.3. Stacked Graphs (Part 1)5:56

2.2.3. Stacked Graphs (Part 2)6:30

与讲师见面

John C. Hart
Professor of Computer Science
Department of Computer Science

[MUSIC]

So, in a bar chart for example, we can use glyphs to represent more than just

the actual value of data elements, we can use glyphs to represent their variants.

So in this case if we have bars, we can put a glyph at the top of each bar

indicating the value, but we can also add information, for

example, standard deviation or variants.

And, in fact, where the mean or median value is indicated and

where the data varies, so these could be 25%, 75% bars.

And we can see in some cases that the distribution of data that gets

aggregated and represented as a single bar,

the actual distribution is skewed in one direction or another.

In tables, you can use glyphs, you can represent entries as, for

example, colors, and that's a kind of a glyph, a very simple glyph.

But mapping table values to color is a very common thing, and

we're all used to this and seeing weather maps where heat is mapped to color,

as for example, this example here.

Quantitatively mapping color is not necessarily

the most effective way for perceptual accuracy.

We can tell that it's colder up here and warmer down here,

we just might not be able to tell how much colder or how much warmer it is unless

we go to the reference here, and try to make an equivalence on this

legend here showing what the average temperature is for this particular month.

And you can do this discretely as well.

This is an example of continuous variables.

You can do this discretely, and we do this in computer science all the time.

If I have a program that I'm trying to optimize, I might change one parameter

some discrete number of times, and some other parameter some

discrete number of times, and try to find the sweet spot in terms of performance.

The lowest performance here I'm displaying in red,

233 is the optimal value here,

and I can see which parameter of values are giving me worse results.

And these things may jump around quite a bit.

Programs tend to behave non-linearly as you change these parameters

because the systems they run on are quite complicated.

But having these glyphs, basically these numbers represented as color as well,

helps you hone in on the good values.

And this one was enabled just using Microsoft Excel.

The conditional formatting option allows you to

represent values with color as well.

So you can also use glyphs to do high dimensional visualization.

So in this case this is worlds within worlds,

I've set up horizontal and vertical nominal,

categorical axes and when I'm plotting inside

each of these table cells, is a separate plot, it's a scatter plot.

I just happen to have one data point in the scatter plot, so

I've embedded a two dimensional plot in each element of a two dimensional table.

So that enables you to look at four dimensional data

as basically a two dimensional table of two-dimensional plots.

In this case what I'm doing is I've got a two-dimensional table of glyphs.

Each glyph happens to be a two-dimensional plot.

And so I'm looking in this case at life expectancy, and

I'm looking at the rates of infant mortality inside each year from 2000,

2001, 2002, 2003, 2004.

And then vertically I'm looking at the various continents or regions, and

I'm looking at the life expectancy measured inside each one of those.

So I've got an outer table that's looking at the year and

vertically looking at the region of the world, and

then inside each year and each region of the world,

I'm plotting infant mortality versus life expectancy.

You can see some shifts in the data as we move across different areas of the world.

And you can see some shifts in infant mortality, but

you can see that there aren't as many shifts as you move from year to year.

There's another interesting glyph that you can use called Chernoff faces, and

in this case you're creating a table, some other layout, but

the data points are being plotted with faces, and the attributes of the face

are indicating various measures of some dimension of the data set.

In this case this is 12 state judges as

rated by lawyers and you can map certain features, certain aspects,

of the way the state judges were being measured, for example, the number of

times they were overturned, might change the expression from happy to sad.

And the size of the face may be the number of cases they've seen.

So, you know, this face may look quite large, and this face look

quite small because this state judge has reviewed more cases than has this judge.

And so you can't make value judgments based on the appearance of the faces, but

the appearance of the faces and the expressions that they

have can give you another data channel that you can perceive and remember,

actually quite well as a way of finding interesting aspects of the data,

things in the data that stand out.

So we learned that we can use glyphs to display many other attributes

of our data when we're using a chart or some other data visualization.

[MUSIC]

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