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[MUSIC]. 
In this final lecture on effective visual 

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encoding, we're going to start with two 
quick exercises on effective presentation 

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of data. 
And then we're going to discuss five key 

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techniques for increasing the amount of 
information represented by spatial 

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position. 
So, we've already talked about why 

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effective visual encoding is important. 
Here is an example that I'd like you to 

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consider. 
So, look at these two representations of 

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quantitative data. 
Here we have the top level one where 

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color is used to represent it. 
And at the bottom, a line graph. 

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Why would the line graph be better to use 
than the color? 

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By now you should know. 
It's faster to interpret. 

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As you take the time to look at it you 
can see that. 

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There are more distinctions. 
You can clearly see the quantitative 

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information much more easily with a line 
graph than with the color, and there are 

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fewer errors of interpretation. 
Pretty simple. 

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Is this an effective visual 
representation, yes or no? 

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If no, what's wrong with it? 
This visual representation is not 

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expressive because it implies an 
incorrect ordinal relationship among the 

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countries. 
Alright, moving on to some new material. 

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We already know from research data in 
human perception, that spatial position 

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is the most accurate representation of 
quantitative data. 

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So, how can we leverage this fact? 
In 1999 Card, Mackinlay and Schneiderman 

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proposed five techniques to increase the 
amount of information encoded by spatial 

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position. 
Composition, alignment, folding, 

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recursion, and overloading. 
We're going to go through each of these 

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slide by slide. 
Composition refers to an orthogonal 

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placement of axes. 
You create a 2D metric space, and you can 

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line the data up along the x and y axes 
in multiple different ways. 

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Alignment, this refers to a, the 
repetition of an axis at different 

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positions in space. 
So, here you see we have the same time 

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axis repeated in two places, with two 
different graphs representing different 

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amounts of information on the y-axis. 
Folding, continuation of an axis in an 

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orthogonal dimension. 
Recursiion, or the repeated subdivision 

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of space. 
And finally, overloading, where we reuse 

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the same space for different data. 
[BLANK_AUDIO] Right, this concludes this 

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set of lectures on visual encoding. 
The next set of lectures we'll move to 

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visual perception.