Visualization-2

Lesson 9
Visualization 2 Kush Kulshrestha

Why do we even need Viz?
There could be datasets that appear to be similar when using typical summary statistics, yet tell different stories
when graphed.
Example, each dataset consists of eleven (x,y) pairs as follows:

Observations:
• The average x value is 9 for each dataset.
• The average y value is 7.50 for each dataset
• The variance for x is 11 and the variance for y is 4.12
• The correlation between x and y is 0.816 for each dataset
So far these four datasets appear to be pretty similar. But when we plot these four data sets on an x/y coordinate
plane, we get the following results:

Now we see the real relationships in the datasets start to emerge

- Dataset I consists of a set of points that appear to follow a rough linear relationship with some variance.
- Dataset II fits a neat curve but doesn’t follow a linear relationship (maybe it’s quadratic?).
- Dataset III looks like a tight linear relationship between x and y, except for one large outlier.
- Dataset IV looks like x remains constant, except for one outlier as well.
Computing summary statistics or staring at the data wouldn’t have told us any of these stories. Instead, it’s
important to visualize the data to get a clear picture of what’s going on.

Cognitive Load and Clutter
Cognitive Load is the amount of mental effort required to interpret information.
Cognitive
Load
Intrinsic Extraneous Germane

Intrinsic Cognitive Load
Amount of memory we need to understand an idea.
Adding 2+2 is easier, doing difficult math division is difficult. If we don’t concentrate in the latter, we end up
spending more time.

Extraneous Cognitive Load
• It relates to how information is presented.
• It is the amount of extra brain power you need to understand a poorly designed Viz.
• Poor design requires more effort to identify problems and create a mental image.

Germane Cognitive Load
It is a way for the brain to look for the patterns to develop context.

Clutter is all the things you remove while still preserving
key ideas.
Reduce clutter to minimize user’s cognitive load
Less clutter = More effective Visualization

Report of USDA’s (US Department of Agriculture)
Economic Research Department
This is a regular report put out regularly by
renowned statisticians.

Things to be removed:
• 3 D effect
• Overuse of bright colors. They are drawing
attention to no specific place.
• Yellow ticker as a threshold!
• No sorting of the data?
• What is x-axis for? Unhelpful axis

Why not to use 3-D?
• 3-D doesn’t improve a Visualization
• Skews information (remember Steve Jobs?)
• Adds confusion

Some other tips:
• Use currency sign with the number
• Always use percent sign when
drawing percentages.
• Commas with number, whenever
number is bigger than 9999
• Use consistent scientific notation if
numbers are too big.

Gestalt Principles
Gestalt refers to the patterns that you perceive when presented with a few graphical elements or why we perceive
things the way we do.
These rules are important when it comes to creating data visualizations to ensure efficient, clean, informative and
correct information transfer.
Understanding gestalt is useful for improving traditional charts like bar charts and line charts.

Gestalt Principles - Similarity
Items that are similar are grouped together.
Visual elements similar in shape, size, colour, proximity, motion, and direction are perceived as part of a group.
Example:
We seek patterns, easily grouping the black dots into a number 4

Example:
We perceive vertical groups of squares and circles.
Example:
We perceive horizontal groups of same red/white
color elements.

Example:
We perceive green/grey colored squares as a
group.
Example:
A complex example of groups.

Gestalt Principles - Proximity
Objects that are close (spatially) are grouped together. The closer items are the more likely the perception they are
a group.
Example:
Instead of counting numerous distinct circles we see three “groups”.

Example:
The circles on the left appear to be grouped in vertical columns, while those on the right appear to be grouped in
the horizontal rows.

Example:
These groups appear to be separated by
color or contrast.
Proximity overpowers other signals of
distinction

Gestalt Principles - Continuity
Lines are seen as following smoothest path.
Example:
The eye tends to want to follow the straight line from one end of this figure to the other, and the curved line from
the top to the bottom, even when the lines change colour midway through

We perceive objects as belonging together, as part of a single whole, if they are aligned with one another or
appear to form a continuation of one another.
Example:
We tend to see the individual lines as a continuation of one another, more as a dashed line than separate lines.

Things that are aligned with one another appear to belong to the same group. In the table in Figure 4-17, it is
obvious which items are division names and which are department names, based on their distinct alignment.
Divisions, departments, and headcounts are clearly grouped, without any need for vertical grid lines to delineate
them. Even though the division and department columns overlap with no white space in between, their distinct
alignment alone makes them easy to distinguish.

Gestalt Principles - Closure
Humans have a keen dislike for loose ends. The principle of closure asserts that we perceive open structures as
closed, complete, and regular whenever there is a way that we can reasonably do so.
Example:
The Gestalt principle of closure explains why we see these as closed shapes, despite the fact that they are not
finished.

Example:
We can apply this tendency to perceive whole structures in dashboards, especially in the design of graphs. For
example, we can group objects (points, lines, or bars in a graph, etc.) into visual regions without the use of
complete borders or background colours to define the space.
This is good and required so that we can reduce the clutter.
The Gestalt principle of closure also explains why only two axes, rather than full enclosure, are required on a graph
to define the space in which the data appears.

More Example:

Gestalt Principles - Connectedness
Elements that are visually connected are perceived as more related than the elements with no connection.
Example:
The Gestalt principle of closure explains why we see these as closed shapes, despite the fact that they are not
finished.

Example:
Even though the spacing and the color is consistent within this collection of elements, those inside of the
connecting lines are perceived to be more related than the rest.
As are the ones connected by the lines..

Example:
And it is more powerful than proximity and similarity.

Brain Memory
Iconic
Short Term
Long Term
Pre-Attentive Attributes of a Visualization
Three types of memories we can influence:
We want to get something in readers brain without them even to think about it.
Why?
This decreases the cognitive load while conveying the information so that there is not much work to be done by
the brain to understand.

Example: Count the total number of 4’s in the figure.

Example: Now count the total number of 4’s in the figure. See the difference?

Good visualization
allow users to see what
we want them to see
before they know that
they have seen it.

So, how do I do all that?

Change one of these to focus on users attention:
• Size
• Color
• Orientation
• $h@pe
• Line composition
• Enclosure
• Intensity
• Position

Visualization-2

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