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@wassx #ILV Informationsvisualisierungen Information Visualisation Information Visualisation Lecture 3 - Visualisation

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Page 1: Information Visualisation - Lecture 3

@wassx#ILV Informationsvisualisierungen

Information Visualisation

Information Visualisation

Lecture 3 - Visualisation

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A story…

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Cognition

Sitting in park, reading newspaper. Suddenly something appears in the corner of your eye.

You raise the hand to block. Afterwards you recognise that a ball nearly hit your face.

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Cognition

Lesson learned #1

Vision is fast, but reason is slow.

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Cognition

Lesson learned #2

Your brain calculated estimated position of impact and prompt your arms to react.

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Cognition

Lesson learned #3

Seeing, perceiving and knowing are different phenomena.

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Gestalt Laws

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Gestalt Laws

Attempt to understand pattern perception. Clear description of many basic perceptual phenomena.

1912 - Gestalt school of psychology (Max Wertheimer, Kurt Koffka and Wolfgang Köhler)

Koffka WertheimerKöhler

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Gestalt Laws

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Gestalt Laws

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Gestalt Laws

Proximity

Spatial proximity is a powerful organising principle. Things which are close together are perceived as a group.

Additionally it has perceptual efficiency. Easier to pick information close to fovea, less time and effort will be spent in neural processing and eye. (-> cognitive load)

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Gestalt Laws

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Gestalt Laws

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Gestalt Laws

Similarity

Shapes of individual pattern elements can also determine how they are grouped.Similar elements tend to be grouped together.

Texture and color are separate channels

Useful when design targets differentiation. Users can easily attend to either one pattern or the other.

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Gestalt Laws

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Gestalt Laws

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Gestalt Laws

Connectedness

Steve Palmer and Irvin Rock argued that connectedness was overlooked by Gestalt psychologists.

Palmer, Stephen; Neff, Jonathan; Beck, Diane (1997). "Grouping and Amodal Perception". In Rock, Irvin. Indirect perception. MIT Press/Bradford Books series in cognitive psychology.

Connectedness can be more powerful than proximity, color, shape or size. Connecting with lines express relationships (node-link diagram)

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Gestalt Laws

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Gestalt Laws

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Gestalt Laws

Continuity

Humans are more likely to construct visual entities out of visual elements that are smooth and continuous.

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Gestalt Laws

Continuity

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Gestalt Laws

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Gestalt Laws

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Gestalt Laws

Symmetry

Symmetrically arranged pairs of lines are perceived more strongly as forming a visual whole than a pair of parallel lines.

Makes pattern comparisons easier. Dakin and Herbert suggests that we are most sensitive to symmetrical patterns that are small in terms of visual angle ( <1 degree horizontally and <2 degrees vertically, and centered around the fovea)

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1689030/pdf/9608727.pdf

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Gestalt Laws

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Gestalt Laws

Closure and Common Region

Perceptual tendency to close contours that have gaps in them. (-> data ink ratio) Wherever a closed contour is seen, regions of space are divided into "inside" and "outside".

Region enclosed by a contour becomes a common region. Common region much stronger than proximity.

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Gestalt Laws

Closure and Common Region

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Gestalt Laws

Figure and Ground

https://www.pinterest.com/pin/562387072188816835/

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Gestalt Laws

Figure and Ground

https://www.pinterest.com/pin/562387072188816835/

Brain decides what is the foreground (figure) in a scene. Decision is made on various cues: movement, color, size,…

If not clear, figure competes with ground (cognitive load)

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Gestalt Laws

https://www.youtube.com/watch?v=nuH6dIcgaoU

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Gestalt Laws

Common Fate

Mental grouping of entities which move in the same direction or have a common destination.

Objects which share a common motion.

https://www.windyty.com/?53.878,-27.993,4

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Hands-on #3

Find a information graphic or visualisation and discuss in one paragraph the use of the Gestalt Principles. (Good example / bad example)

~15min

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Visual Properties for Encoding

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Visual Properties for Encoding

Designing Data Visualizations, Noah Iliinsky & Julie Steele

Choosing Appropriate Visual Encodings

Different properties for different type of data.

Key factors of a visual property are: 1. property is naturally ordered 2. how many distinct values reader can easily differentiate

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Visual Properties for Encoding

Designing Data Visualizations, Noah Iliinsky & Julie Steele

Natural Ordering

„Natural Order“ is determined by our visual system and „software“ in our brains by unintentionally assigning an order, or ranking to different values of that property.

Independent of language, culture, convention,…

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Visual Properties for Encoding

Designing Data Visualizations, Noah Iliinsky & Julie Steele

Natural Ordering

quantitative differences

ordinal differences

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Visual Properties for Encoding

Designing Data Visualizations, Noah Iliinsky & Julie Steele

Natural Ordering

How about COLOR?

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Visual Properties for Encoding

Designing Data Visualizations, Noah Iliinsky & Julie Steele

Natural Ordering

How about COLOR?

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Visual Properties for Encoding

Designing Data Visualizations, Noah Iliinsky & Julie Steele

Natural Ordering

No.

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Visual Properties for Encoding

Designing Data Visualizations, Noah Iliinsky & Julie Steele

Natural Ordering

Color (hue) is NOT naturally ordered. „Ordering“ based on social conventions about color and ordering by wavelength in the physical world. But no non-negotiable natural ordering built into our brain.

3 4vs.

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Designing Data Visualizations, Noah Iliinsky & Julie Steele

Natural Ordering

Visual Properties for Encoding

But luminance and saturation are naturally ordered.

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Designing Data Visualizations, Noah Iliinsky & Julie Steele

Distinct Values

Visual Properties for Encoding

Reader must be able to perceive, differentiate and remember distinct values.

Big amount of values.

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Visual Properties for Encoding

Designing Data Visualizations, Noah Iliinsky & Julie Steele

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https://www.behance.net/gallery/11685745/Datavisualisation-of-a-Game-of-Thrones

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Visual Properties for Encoding

Designing Data Visualizations, Noah Iliinsky & Julie Steele

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Designing Data Visualizations, Noah Iliinsky & Julie Steele

Redundant Encoding

Visual Properties for Encoding

If unused visual properties are left, consider using them for redundantly encode dimensions.

Using more channels makes acquisition of information faster, easier and more accurate.

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Visual Properties for Encoding

Don’t forget…

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Designing Data Visualizations, Noah Iliinsky & Julie Steele

Compatibility with Reality

Visual Properties for Encoding

Align encodings with things and relationships known from reality.

Compatibility

Extra cues from physical world and cultural conventions.

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Visual Properties for Encoding

Designing Data Visualizations, Noah Iliinsky & Julie Steele

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Designing Data Visualizations, Noah Iliinsky & Julie Steele

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Visual Properties for Encoding

Designing Data Visualizations, Noah Iliinsky & Julie Steele

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Visual Properties for Encoding

http://www.mymarketresearchmethods.com/wp-content/uploads/2013/01/visualization1.png

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Visual Properties for Encoding

Think for whom you are designing for. Keep in mind ~7% of males have some kind of color weakness.

Check used colors with appropriate tools:

Colorblind Vision

Photoshop

Online tools….

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Visualising Patterns over Time

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Visualising Patterns over Time

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Visualising Patterns over Time

http://projects.flowingdata.com/life-expectancy/

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Visualising Patterns over Time

http://skedasis.com/d3/slopegraph/

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Visualising Proportions

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Visualising Proportions

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Visualising Proportions

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Visualising Proportions

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Visualising Proportions

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Visualising Relations

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Visualising Relations

http://mbostock.github.io/d3/talk/20111116/iris-splom.html

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Visualising Relations

http://bl.ocks.org/mbostock/4063530

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Visualising Relations

http://bl.ocks.org/mbostock/4063550

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Visualising Relations

http://bl.ocks.org/mbostock/4063530

sankey

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Spotting Differences

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Spotting Differences

http://bl.ocks.org/tjdecke/5558084

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Spotting Differences

http://bl.ocks.org/tjdecke/5558084

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Spotting Differences

Chernoff faces

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Spotting Differences

Chernoff faces

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Visualising Spatial Relationships

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Visualising Spatial Relationships

Airport data

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Visualising Spatial Relationships

http://ssz.fr/parite/

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Visualising Spatial Relationships

http://avtanski.net/projects/gps/

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Checklist

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Checklist

Determine Your Goals and Supporting Data

• What information need are you attempting to satisfy with this visualization?

• What values or data dimensions are relevant in this context?

• Which of these dimensions matter; matter most; and matter least?

• What are the key relationships that need to be communicated?

• What properties or values may make some individual data points more interesting than the rest?

• What actions might be taken once the reader’s information need is satisfied, and what values will justify that action?

Designing Data Visualizations, Noah Iliinsky & Julie Steele, O'Reilly 2011

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Checklist

Consider Your Reader

• What information does the reader need to be successful?

• How much detail does the reader need?

• How long does the reader have to make any learned information effective?

• What learned or cultural assumptions does the reader have that may affect your design choices?

Designing Data Visualizations, Noah Iliinsky & Julie Steele, O'Reilly 2011

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ChecklistSelect Axes, Layout, and Placement

• Can you encode your most important data dimension or relationship with position?

• Is there a secondary grouping, dimension, or relationship that can be represented spatially? What if you rearrange or invert groupings?

• Does your direction make sense? Where does the data begin and end? Where should the reader start reading? Which way to the relationships flow?

• Does the placement of your entities reflect their relationships to each other?

• Does the placement of your entities reflect their relationship to reality?

Designing Data Visualizations, Noah Iliinsky & Julie Steele, O'Reilly 2011

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Checklist

Evaluate Your Encoding Entities

• Are you using conventional encodings and formats? If not, are you sure you have something better?

• Are you using color to represent quantity? Stop it. Use size or placement instead.

• Are your shapes, colors, icons, and text evocative of the properties that exist and that you want to communicate?

• Are you using the same visual encoding for more than one data dimension? Try to pick another one.

• Are you using extra visual properties to redundantly encode your data? Good job!

Designing Data Visualizations, Noah Iliinsky & Julie Steele, O'Reilly 2011

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Checklist

Reveal the Data’s Relationships

• Are the most important relationships revealed?

• Do the relationships need to be called out with links or labels? Or a specific flag?

• Are all the displayed relationships actually relevant and useful?

• Are you redundantly encoding your links?

Designing Data Visualizations, Noah Iliinsky & Julie Steele, O'Reilly 2011

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ChecklistChoose Titles, Tags, and Labels• Is the reader from within your industry or outside of it? What about

other readers outside of the core audience group? Consider how this will affect your vocabulary choices.

• Is it worth using an industry term for the sake of precision (knowing that the reader may have to look it up), or would a lay term work just as well?

• Will the reader be able to decipher any unknown terms from context, or will a vocabulary gap obscure the meaning of all or part of the information presented?

• Is everything important labeled? Are all of your labels necessary? Is your key or legend necessary? Is it ordered in a useful way?

Designing Data Visualizations, Noah Iliinsky & Julie Steele, O'Reilly 2011

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Checklist

Analyze Patterns and Consistency

• Have you been consistent in membership, ordering, placement, and other encodings?

• Things that are the same should look the same. Is that so?

• Things that are different should look different. Is that so?

Designing Data Visualizations, Noah Iliinsky & Julie Steele, O'Reilly 2011