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HELLO!

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I’m Benjamin from Interactive Things

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INTERACTIVE THINGSUser Experience & Data Visualization Studio

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DATAVISUALIZATION.CHResource for Data Visualization & Infographics

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7Principles for Engaging Users with Visualization

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SUPPLY

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Build an treasure chest of data.

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NARRATION

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Tell compelling stories with visualization.

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Stories are important cognitive events, for they encapsulate, into one compact package, information, knowledge, context, & emotion.

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Donald Norman

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Narrative Visualization: Telling Stories with Data

Martini Glass Structure

Interactive Slideshow

Drill-Down Story

Edward Segel & Jeffrey Heer

New York Times: Faces of the Dead

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Narrative Visualization: Telling Stories with Data

Martini Glass Structure

Interactive Slideshow

Drill-Down Story

Edward Segel & Jeffrey Heer

New York Times: Budget Forecasts, Compared With Reality

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Narrative Visualization: Telling Stories with Data

Martini Glass Structure

Interactive Slideshow

Drill-Down Story

Edward Segel & Jeffrey Heer

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New York Times: Murder: New York City

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PERSONALIZATION

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Make the reader feel at home.

New York Times: Jobless Rate for People Like You

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PARTICIPATION

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Foster the dialog between people.

New York Times: Mapping America

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ACTION

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Inspire the user to create real impact.

Green Peace & Daniel Fischer: Small Habits

Global Giving & Roland Loesslein: The Human's Development

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MEASUREMENT

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Analyze the hell out of your work.

Chartbeat

Bitly Clicky

Google Analytics

UNDP & Interactive Things: Human Development Index 2.0

OECD, Moritz Stefaner & Raureif: Better Life Index

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EXPERIMENTATION

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Push the medium forward.

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Another potential way to respresent the data is a standard bar

chart. Each item would have its own bar of similar dimensions

and, because they are distinct, the second variable of colour

could be kept and comparing or reordering the bars accordingly is

straightforward. The advantage of the treemap over a bar chart is

that it saves space and similar items can be clustered together, but

comparison and labelling su!er in exchange for compactness.

Horizon graph

Horizon graphs are a kind of overlaid line graph. They are a

relatively new invention, so their usefulness is still undetermined,

and their strengths and weaknesses are still emerging. They were

developed by a data visualization company called Panopticon to

better visualize and compare several data sets over time, side by

side without overlapping them.

In our roadside stand example there are three separate

businesses, but what if there were thirty? Thirty bar charts per

week lined up next to each other would be too much. A stacked bar

chart gives us the total sales, but what if we wanted to compare

within each week? We could make a line graph for each store and

then plot all thirty on the same graph. That, too, would quickly

get overcrowded with information. As we"ve seen, once we exceed

about #ve or six, we start to run out of unique colours, shapes,

thicknesses and so on. Graphing thirty at once becomes a mess.

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200

300

400

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Peter Gassner. Designing with Data - PDF. 13th August 2010. © Five Simple Steps.

141 A Practical Guide to Designing with data

This is the niche that horizon graphs are designed to !ll. They are

basic line graphs split horizontally into a set number of bands.

This example uses four bands, but they can contain as many

as required. Each of the bands is coloured with an increasing

intensity of blue. So far, this is no di"erent than a standard line

graph with some interesting shading.

The next part is the Eureka! moment. Since we are really only

interested in the outer edge of the line graph, why not collapse

each of the bands on top of each other to save space?

Now we have the same data presented in a quarter of the space.

By changing the colour of the shading, the graph appears to be

stacking up the parts. There is nothing hiding behind any of the

peaks so, unlike 3-D charts, nothing is being obscured.

This works with both positive and negative values.

Maybe in some weeks the shops were making a loss, which can

be represented with a di"erent colour, such as varying intensities

of red.

1 2 4 5 6 7 8 9 10 11 123

~ Area graphs and charts

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200

300

400

1 2 4 5 6 7 8 9 10 11 123

Peter Gassner. Designing with Data - PDF. 13th August 2010. © Five Simple Steps.

141 A Practical Guide to Designing with data

This is the niche that horizon graphs are designed to !ll. They are

basic line graphs split horizontally into a set number of bands.

This example uses four bands, but they can contain as many

as required. Each of the bands is coloured with an increasing

intensity of blue. So far, this is no di"erent than a standard line

graph with some interesting shading.

The next part is the Eureka! moment. Since we are really only

interested in the outer edge of the line graph, why not collapse

each of the bands on top of each other to save space?

Now we have the same data presented in a quarter of the space.

By changing the colour of the shading, the graph appears to be

stacking up the parts. There is nothing hiding behind any of the

peaks so, unlike 3-D charts, nothing is being obscured.

This works with both positive and negative values.

Maybe in some weeks the shops were making a loss, which can

be represented with a di"erent colour, such as varying intensities

of red.

1 2 4 5 6 7 8 9 10 11 123

~ Area graphs and charts

100

200

300

400

1 2 4 5 6 7 8 9 10 11 123

Peter Gassner. Designing with Data - PDF. 13th August 2010. © Five Simple Steps.

142

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

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!100!200!300!400

200300400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Shop 30

Shop 29

Shop 28

Shop 27

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Now you can rapidly see which weeks saw gains and which saw

losses, and at the same time also watch the line trend upwards

or downwards.

The horizon graph works really well when large numbers of

data sets need to be compared. Converting one line graph into a

horizon graph does save some space, but now that we have this

ribbon, we can stack more ribbons on top to get a quick view of all

the di!erent shops and compare them in a limited space.

The downside of horizon graphs appears when the data sets are

very large. This example was broken down into only four colour

bands; this was su"cient to allow each to be distinct. The more

bands required, however, the more colours and the greater

potential for confusion. By limiting the number of divisions, each

band represents a larger portion of the data. To compare several

horizon graphs, their coloured bands need to represent the same

divisions; if they don#t, the comparison will be di"cult.

Peter Gassner. Designing with Data - PDF. 13th August 2010. © Five Simple Steps.

142

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

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!100!200!300!400

200300400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Shop 30

Shop 29

Shop 28

Shop 27

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Now you can rapidly see which weeks saw gains and which saw

losses, and at the same time also watch the line trend upwards

or downwards.

The horizon graph works really well when large numbers of

data sets need to be compared. Converting one line graph into a

horizon graph does save some space, but now that we have this

ribbon, we can stack more ribbons on top to get a quick view of all

the di!erent shops and compare them in a limited space.

The downside of horizon graphs appears when the data sets are

very large. This example was broken down into only four colour

bands; this was su"cient to allow each to be distinct. The more

bands required, however, the more colours and the greater

potential for confusion. By limiting the number of divisions, each

band represents a larger portion of the data. To compare several

horizon graphs, their coloured bands need to represent the same

divisions; if they don#t, the comparison will be di"cult.

Peter Gassner. Designing with Data - PDF. 13th August 2010. © Five Simple Steps.

AnimatedComputed

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Interactive Dynamic

Radiales Netzwerk

Radiales NetzwerkRadial Network with Edge Bundling

Force Directed Layout

Force Directed Layout

Streamgraph

Voronoi Tessalation

Computed

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Interactive Dynamic Animated

Moritz Stefaner: Map Your Moves

PushPopPress: Our Choice

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Interactive DynamicComputed Animated

GE & Periscopic: Cancer Conversations

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Interactive Dynamic AnimatedComputed

City of Geneva, Lift & Interactive Things: Ville Vivante

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SUPPLYNARRATION

PERSONALIZATIONACTION

MEASUREMENTEXPERIMENTATION

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The idea for information design is:Don't get it original, get it right.

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Edward Tufte

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MERCI!

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Don’t hesitate to get in touch:+41 44 267 66 44hello@interactivethings.com@ixt