[MUSIC]. 
In this final lecture on effective visual 
encoding, we're going to start with two 
quick exercises on effective presentation 
of data. 
And then we're going to discuss five key 
techniques for increasing the amount of 
information represented by spatial 
position. 
So, we've already talked about why 
effective visual encoding is important. 
Here is an example that I'd like you to 
consider. 
So, look at these two representations of 
quantitative data. 
Here we have the top level one where 
color is used to represent it. 
And at the bottom, a line graph. 
Why would the line graph be better to use 
than the color? 
By now you should know. 
It's faster to interpret. 
As you take the time to look at it you 
can see that. 
There are more distinctions. 
You can clearly see the quantitative 
information much more easily with a line 
graph than with the color, and there are 
fewer errors of interpretation. 
Pretty simple. 
Is this an effective visual 
representation, yes or no? 
If no, what's wrong with it? 
This visual representation is not 
expressive because it implies an 
incorrect ordinal relationship among the 
countries. 
Alright, moving on to some new material. 
We already know from research data in 
human perception, that spatial position 
is the most accurate representation of 
quantitative data. 
So, how can we leverage this fact? 
In 1999 Card, Mackinlay and Schneiderman 
proposed five techniques to increase the 
amount of information encoded by spatial 
position. 
Composition, alignment, folding, 
recursion, and overloading. 
We're going to go through each of these 
slide by slide. 
Composition refers to an orthogonal 
placement of axes. 
You create a 2D metric space, and you can 
line the data up along the x and y axes 
in multiple different ways. 
Alignment, this refers to a, the 
repetition of an axis at different 
positions in space. 
So, here you see we have the same time 
axis repeated in two places, with two 
different graphs representing different 
amounts of information on the y-axis. 
Folding, continuation of an axis in an 
orthogonal dimension. 
Recursiion, or the repeated subdivision 
of space. 
And finally, overloading, where we reuse 
the same space for different data. 
[BLANK_AUDIO] Right, this concludes this 
set of lectures on visual encoding. 
The next set of lectures we'll move to 
visual perception.