Data & Narratives

Harkanwal Singh

Data visualisation for communication

A bit of background

Experience in data journalism, teaching, engineering.

My data visualisation approach*

Data visualisation with code

This workshop is about the craft of data visualisation

Tools do matter

Today’s agenda:

Inspiration & Motivation

Reading a visualisation

Principles and examples

Data visualisation design process

Narratives and editorial thinking

Vocabulary and techniques

Colour in-depth

Exploring data visualisation tools

Inspiration &

Motivation

Visualisation as a language, informed by data journalism practice - to make real world impact.

My framework for data visualisation

Data journalism

Outside of New Zealand - an innovative landscape of data visualisation practice.

Computer-aided-reporting back in 1980s

Precision journalism

A place to experiment, get real feedback

Provide value, compete with clickbait

The innovators - Mike Bostock, Rich Harris

NYT graphics dept was my starting point for inspiration*

Learning from data journalism practice

A comparison between practices and paths overseas versus NZ

Who gets to breathe clean air in New Delhi? (NYT)

Shifting smoke - Wildfires in US (Reuters)

COVID simulation (Washington Post)

2021: The year in visual stories and graphics (NYT)

This is what fuels the West’s inferno (Washington Post)

John Burn-Murdoch at FT

Mona Chalabi

Data visualisation history

William Playfair, Charles Minard, Florence Nightingale, John Snow and many others

Data visualisation before we started thinking with templates

Rules are invented and meant to be broken

This could be a whole talk in itself

W.E.B Du Bois and the data portrait of black America

William Playfair

Invented the bar chart, line chart, pie chart.

Charles Minard

We have [made] an honest, straightforward exhibit of a small nation of people, picturing their life and development without apology or gloss, and above all made by themselves.

~ W.E.B Du Bois

Hans Rosling

the greatest data storyteller

Whenever the data hype gets too much for me, I revisit Rosling’s talks and books.

Numbers, but not only numbers. The world cannot be understood without numbers, and it cannot be understood with numbers alone. Love numbers for what they tell you about real lives.

Hans Rosling

Curb your dramatic instincts

Reading a visualisation

(or, how to invent new charts)

Marks

A mark is a basic graphical element in an image.

Marks are geometric primitive objects classified according to the number of spatial dimensions they require.

Channels

A visual channel is a way to control the appearance of marks, independent of the dimensionality of the geometric primitive.

Every graph is made up of marks and channels

What happens when you click the bar chart button

Mapping

Your data gets mapped from a spreadsheet column to a geometry

Understanding this is the key to become better at data visualisation than your BI software

Let’s break down this visualisation

At the start, you will find this slightly tedious but with time, it should become a habit.

Marks & channels?

Marks - point

Channel - vertical and horizontal

What changed here?

Channel - Sized to population

One step closer

New channel added: colour

Almost there

Channel - Colour with opacity

What else is there

Context

Gridlines, better legend, title, annotation

Look at the original again

Effectiveness of different visualisation

Reading a visualisation

Exercise

Take a visualisation at work, or in the media and break it down to maps and channels.

Think about why some of the choices are good or bad.

Can you think of another way of creating the visualisation using marks and channels?

Each visualisation includes editorial decisions*

*It depends whether you are making it, or the software defaults. More on that later.

Map shows land area

Main channels:

Colour

Swatches

Mark - line

Channel - length

States where votes are split

Map shows electoral college votes as area

Split votes are now part of the map

Mark is now Circle - size proportional to the lead

Map serves as background now

Mark is now angle -

Showing shift in margin

Also, length to show the size

Let the data speak

...and see it ramble on and on.

One data set visualised 25 ways

Sketching is every data vis creator’s superpower*

Sketch your variables to marks and channels

Exercise

Pick a data set that you are reasonably familiar with, start with paper and pencil, sketch out different ways that you could visualise the data set.

Patterns, emotions and impact

It’s about patterns

Somewhat delightful

And at other times

Somewhat visceral

Sometimes,

just plain confusing

Auckland Council Unitary Plan Maps

Tasks for the user

The journey to perceptual inference

A different approach

Chris McDowall’s maps for the Spinoff

Similarity

Leveraging the idea of suburbs

Another version

Selected categories

Focus on overview and filtering

http://insights.nzherald.co.nz/article/auckland-unitary-plan/

Attention

Designed to work for small devices and quick scan

Principles before process

(Decide what matters to you and your audience)

Fidelity

To data and to the audience,

Beauty

Is always a worthwhile goal

Usefulness

Serve a purpose, answer a question, develop understanding, provoke more questions.

What’s your data visualisation design process?

Communicating data well is combination of art and science. It is rewarding for you and the end user, when done well.

If you do not have a process and are chucking data into a tool, or a programming language, you are not going to do it well.

Data

The Latin word data is the plural of 'datum', "(thing) given”.

Users

Users come to a data product with needs and requirements

Design process is an interplay between given data and user needs

What happens when there is a mismatch?

Your organisation has dashboards that exist in perpetuity without anyone knowing why they are being maintained and who exactly is benefiting from them.

Visualise with intent

‘Good design is honest. It does not make a product appear more innovative, powerful or valuable than it really is. It does not attempt to manipulate the consumer with promises that cannot be kept.’

Dieter Rams

Andy Kirk’s seven hats of visualisation design

Write down your own design process

Exercise

What is the process you currently follow? How can you make it better?

Don’t build a dashboard for the sake of building one, try and solve an actual problem.

Exploratory data analysis

If we need a short suggestion of what exploratory data analysis is, I would suggest that

It is an attitude AND

A flexibility AND

Some graph paper (or transparencies, or both)*.

~ John Tukey

A note about data analysis

Doing this effectively and well is the key to your design process and discovering narratives.

The real glamour of of data jobs - cleaning up a dataset

How you do exploratory data analysis matters

Your tools will dictate the speed of discovery

Make lots of charts, make them early

This here is your data visualisation playground - use it

Narratives & editorial thinking

What do you mean when you say data storytelling?

You’ve to understand your audience, not just your data.

Then, the communication becomes about them and not you.

No singular stories

Data unlike stories is not static

Your data has to say something

Narratives open a pathway to more than one interpretation

Your analysis, visualisation and design choices dictate the narrative

Data is never ever neutral

Angle

What view(s) of your data is most relevant? In language terms, what question should your eventually chosen charts answer?

Framing

What data items and values will you include and exclude? What is most representative of your subject.

Focus

Are there any features of your data you would wish to emphasise? This is especially relevant to explanatory visualisations: if you have something to say, say it.

For sale: baby shoes, never worn

Communication vs interpretation.

“The story is triggered in our mind, when we read this passage and start to infer meaning, implication and context. A story is being presented only if it is accompanied by some explanation of the meaning of the data. Otherwise, any story derived is what the views form themselves.” ~ Andy Kirk

Martini Glass Structure

You are gearing up towards a strong ending. You want your audience to leave with a point.

Structuring visual narratives to feed the curiosity - Gurman Bhatia

Familiarise with data, setup for exploration

Narrative Visualisation: Telling Stories with Data(2010)

How to do this effectively and consistently

Sketching

Easiest way to prototype a narrative is by sketching it out

Do this with somewhat realistic data

Your narrative is informed by what your data has to say

Strike a balance between your expertise and user requirements

Vocabulary & techniques

Techniques beyond a single chart

Here is an example of NZonAir survey data on media usage.

What if we wanted another categorical variable

This is something you absolutely shouldn’t present.

Small multiples

One of the most effective techniques when comparing across different categories

Filter to emphasise your point

Keep the whole data set in the background to show comparison

Source

Make interaction meaningful

Once you have a visual defined, manipulate the same to show different facets of the data.

Source

Averages hide the data

Datasaurus by Alberto Cairo, via Autodesk

Source

Show the distribution, include the average

We come back to the Marks and Channels chart - think about how this differs

Colour in-depth

Colour matters

It is the most domination visual element of your visualisation.

Design a colour palette

Organisations use brand colours.

Start with defaults, explore from there.

Colour imbues meaning

Use it to make impact

Your charts can have a brand feel without exploiting the data elements

Please read the blog posts from Lisa

Experiment by creating alternate versions

Data visualisation as a brand

Exploring