Synchronously Visualized

Once again:  the New York Times presents an innovative graphic, which you always want to watch again and again.

It’s this Downhill Race at the Olympics:

.

Start

Run

Finish

 

The link to the moving graphic is below this picture:

For Statistics?

It would be exciting to follow such visualizations, e. g. on changes in unemployment, GDP etc. of different countries from today-minus-x to today.

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Easy-to-understand Statistics for the Public

In a recently published EUROSTAT publication, the authors demand innovative forms of communication from public statistics in order not to lose their socially important role. Among other things, they demand ‘…. to tell stories close to the people; to create communities around specific themes; to develop among citizens the ability to read the data and understand what is behind the statistical process.’

Telling Stories

The UNECE hackathon that has just been completed responds to this challenge.
‘A hackathon is an intensive problem-solving event. In this case, the focus is on statistical content and effective communication. The teams will be challenged to “Create a user-oriented product that tells a story about the younger population”. During the Hackathon, fifteen teams from nine countries had 64.5 hours to create a product that tells a story about the younger population. The teams were multidisciplinary – with members from statistical offices and other government departments. The product created should be innovative, engaging, and targeted towards the general public (that is, not specialists). There was no limit on the form of the product, but the teams had to include a mandatory SDG indicator in the product.
The mandatory indicator was “Proportion of youth (aged 15-24 years) not in education, employment or training” SDG indicator (Indicator 8.6.1).‘ (Source)

Winners

And the hackathon shows impressive results, even if only a few organisations have participated.

The four winners are:

My Favourites

My favourites are number 3 from the National Institute of Statistics and Geography (INEGI-Mexico) and number 2 from the Central Statistical Office of Poland.

Why?

The Mexican solution…

…is aesthetically pleasing and easy to use. The interaction is left to the user and can be individually controlled by him/her in the speed.

The diagrams do not stand alone, but are explained by short texts while scrolling.

The results are not just being accepted. Rather, the concepts are explained and questioned – statistics are presented with the methodological background.

The Polish solution…

…starts with a jourmalistic approach. Here too, the interactivity can be controlled by the user at the desired speed.

At the end, the authors also seek direct contact with the users; a quiz personalizes the statistical data and gives an individual assessment of where the users stand personally with regard to these statistics.

Success Factors

The two applications mentioned above combine decisive user-friendly features:
– visually attractive,
– easy-to-understand navigation that can be controlled by the user according to his needs,
– the journalistic approach,
– concise and instructive explanations,
– personalization,
– hints on the methodological background.

Many of the other applications show the frequently encountered weaknesses: Too much information should be provided, no courage to leave something behind and concentrate on the most important elements. And this leads to long texts and complex navigation with the effect that users quit quickly.

Learning by Doing

The New York Times did it after the election, in January 2017: You Draw It, Learning Statistics by drawing and comparing charts.

‘Draw your guesses on the charts below to see if you’re as smart
as you think you are.’

 

And Bayerischer Rundfunk did it before the election, in April 2017.

This kind of giving information is an excellent strategy to foster insights and against forgetting. And it’s an old tradition in didactics. 360 years ago Amos Comenius emphasized this technique in his Didactica Magna:

“Agenda agendo discantur”

 

You Can See in Numbers

‘We are extremely sad to announce that Professor Hans Rosling died this morning. Hans suffered from a pancreatic cancer which was diagnosed one year ago. He passed away early Tuesday morning, February 7, 2017, surrounded by his family in Uppsala, Sweden.’ Anna R. Rönnlund & Ola Rosling, Co-founders of Gapminder. He died aged 68.

rosling-30102009
Hans Rosling, Geneva, 2009-10-30

In 2009, the Swiss Statistics’ Meeting took place in Geneva, Switzerland. Hans Rosling was there and his talk’s topic: ‘Unveiling the beauty of statistics’. He wanted data to be free, free from legal and technical barriers. His ambition – and his success – was to disseminate these data beautifully … in order to change the world.

A difficult task. In an interview in the Guardian, in 2013: “It’s that I became so famous with so little impact on knowledge,” he says, when asked what’s surprised him most about the reaction he’s received. “Fame is easy to acquire, impact is much more difficult. …. He’s similarly nonplussed about being a data guru. “I don’t like it. My interest is not data, it’s the world. And part of world development you can see in numbers.”  (Taken from the Guardian interview 2013).

And that’s why statistics and the world need more people like Hans Rosling – more than ever!

Reading a Picture

Visual storytelling

Visualising data helps understanding facts.
Sometimes it’s very easy to understand a graph; sometimes it’s necessary to read it and to study it to discover unknown territory.

Such graphs are little masterpieces. Here’s one of these and I am sure the authors had more than one iteration and discussion while creating it.
The graph tells the story of the average disposable income and savings of households in Switzerland, published by the Swiss Federal Statistical Office FSO.

snip_disposable-income2

The authors kindly give a short explanation:

How to read this graph.
In one-person households aged 64 or under, the upper-income group has a disposable income of CHF 8487 per month and savings of CHF 2758 per month. Representing 4.0% of all households, this income group corresponds to a fifth of one-person households aged 64 or under (20.1%)

There’s another nice graph, a little bit less elaborated, also explained by the authors:

snip-povertyrates

Statistics ♥

But there’s one thing that is not explained:

snip_poverty-cithe confidence interval!

‘A confidence interval gives an estimated range of values which is likely to include an unknown population parameter, the estimated range being calculated from a given set of sample data,‘ and the above poverty data are from a sample of ‘approximately 7000 households, i.e. more than 17,000 persons who are randomly selected…’.
Or:
The confidence intervals for the mean give us a range of values around the mean where we expect the “true” (population) mean is located (with a given level of certainty, see also Elementary Concepts). ….. as we all know from the weather forecast, the more “vague” the prediction (i.e., wider the confidence interval), the more likely it will materialize. Note that the width of the confidence interval depends on the sample size and on the variation of data values…..’

Khan Academy gives lectures about topics like confidence intervals, sampling, etc.

snip_20161129160845.

Which one ?

The above graphs use just one of multiple possibilities for visualising data.

snip_graph-catalogue

Severino Ribecca’s Data Visualisation Catalogue is one of many websites trying to give an overview. And there’s the risk to get lost in these compilations.

snip_swimring                            © listverse.com

Next Step in OGD Websites

What DataUsa is doing could be – I guess – the next step in the evolution of Open Government Data websites. It’s the step from offering file downloads to presenting data (and not files) interactively. And it’s a kind of presentation many official statistical websites would surely be proud of.

César A. Hidalgo from MIT discusses the philosophy behind this. More at the end of this post; at first a short look at this website.

snip_datausahome

Bringing data together

Merging data from different sources may have been the most expensive and challenging task and the conditio sine qua non for the existence of this website. And perhaps it’s more an organizational than a technical challenge.

Seven public data sources are accessible via DataUsa

snip_datausa-datasources

Presenting data

Adapting to what internauts normally do, the main entrance is a search bar;

snip_datausa-homesearch

 

Thematical and geographical profiles are available, too. But in a hidden menu.

The presentation of the data is a mix of generated text and various types of graphs.

snip_datausa-graph3

snip_datausa-graph

 

The option above every graph allows to share, embed, download, get a table and even an API for the data.

snip_datausa-data3

 

And finally thematical maps provide other views and insights:
snip_datausa-map

Storytelling

But the fascinating part is Stories
snip_datausa-storiessnip_datausa-stories2

Various authors write stories focussing on special topics and using the presentation techniques of the site.

Background

A glossary explains technical terms and the About Section presents the authors and their aim:
‘In 2014, Deloitte, Datawheel, and Cesar Hidalgo, Professor at the MIT Media Lab and Director of MacroConnections, came together to embark on an ambitious journey — to understand and visualize the critical issues facing the United States in areas like jobs, skills and education across industry and geography. And, to use this knowledge to inform decision making among executives, policymakers and citizens.’

And this leads to the
Philosophy behind 

César A. Hidalgo, one of the websites’ authors explains why they did what they did in a blog post with the title ‘What’s Wrong with Open-Data Sites–and How We Can Fix Them.’

Here’s the design philosophy in a visual nutshell:

snip_datausa-design

 

‘Our hope is to make the data shopping experience joyful, instead of maddening, and by doing so increase the ease with which data journalists, analysts, teachers, and students, use public data. Moreover, we have made sure to make all visualizations embeddable, so people can use them to create their own stories, whether they run a personal blog or a major newspaper.’

And:

‘After all, the goal of open data should not be just to open files, but to stimulate our understanding of the systems that this data describes. To get there, however, we have to make sure we don’t forget that design is also part of what’s needed to tame the unwieldy bottoms of the deep web.’