Data Visualisation vs. Text

Simon Rogers has mapped data which ranked 754 beaches around Great Britain for the Guardian Data Store. The visualisation uses a satellite map of the UK, onto which Simon has marked every beach in its correct geographical location. The dots are colour coded to clearly denote the ranking the beach received from the 2012 Good Beach Guide, green representing ‘Recommended’, purple meaning ‘Guideline’, yellow meaning ‘Basic’ and red indicating that the beach failed to reach the Beach Guide’s standards. Users can click on individual dots to get the names of each beach and its ranking.

In this way an enormous mass of information is presented in a small space. It is also presented in a clear and comprehendible way. Users can spend as long as they like ‘reading’ the map and obtain as much or as little information as they wish to from it.

Underneath the map, Simon has written out all 754 beaches, with their ranking alongside it. As he has done so, we can easily compare the use of text to tell a data story with a visualisation. The text takes up significantly more room. It is much harder to find the individual beaches you are interested in and takes more energy and effort to scroll up and down in order to find a particular beach. The sheer mass of information presented in the text makes the story seem like a drag, rather than a fun exploration of the British coastline, as is felt by the visualisation.

However, underneath the map Simon has highlighted key features and findings of the data. He writes: “The report rated 516 out of 754 (68%) UK bathing beaches as having excellent water quality – up 8% on last year. That compares well to 2010, when it rated 421 of 769 beaches as excellent.”

It is not clear from the visualisation alone how many beaches received each rating and it would have been time consuming and difficult for the user to individually count this. Thus text is useful to provide a summary and to highlight key findings alongside a visualisation.

This is therefore a fine example of the way in which visualisations and text complement each other, and demonstrates that, with many data stories, combining visualisation and text creates the richest, most comprehendible and informative narrative.

 

 

VISUALISATION ANALYSIS #3

http://www.guardian.co.uk/news/datablog/interactive/2012/mar/26/office-for-national-statistics-health

Simon Rogers has published a fantastic interactive graphic for the Guardian Datastore that maps teenage pregnancy rates in England and Wales from 1998 to 2010.

The visualisation shows the conception rate of under-eighteen year olds, per 1000 women, in different counties across England and Wales. The interactive map is an ideal way to present the information, as the visualisation contains a large amount of data in a comprehensible way. From the graphic we can derive that the number of teenage pregnancies has declined in the last decade, although this varies by area.

In order to focus on a specific county the user can scroll the mouse over the map and click on a different area, labelled by county at the side of the map. Once you click on a county the line graph changes to show the counties’ change in number of teenage pregnancies by year and how this compares to the England and Wales average. This allows the user to have more detailed and specific information simply by clicking on the infographic. Thus the graphic allows users to see the more personalised, local data.

By using this tool the user can focus on various localised data, and see how they compare with each other. For example, in Wales it is apparent that poorer counties, such as Merthyr Tydfil and the South Wales Valleys, are significantly over the national average regarding the number of teenage pregnancies. In contrast, geographically close but wealthier counties like Monmouthshire and Powys are below the national average. In most cases this has not altered over the decade.

The map thus proves that in certain circumstances seeing only the larger data can give a limited understanding, as it shows a national decline in the number of teenage pregnancies but does not tell us that many individual counties have not changed significantly. In this way a graphic of this kind presents to users the ‘big picture’, in a clearer way than text alone.

The graphic also allows users to ignore information that is not of interest to them and to focus on geographical locations that are. This gives users a certain amount of control over the visualisation, as information is not decided for the user, as would be the case with textual narrative.

The interactive element of the visualisation allows users to find the story or information for themselves with no difficulty. This is more satisfying than simply being told information. At a time when the general public’s trust in journalism is low, visualisations such as this demonstrate that the journalist has not played around and sifted information but presented all of it to the user and allowed them to draw their own conclusions. In this way the user can get a more detailed, accurate and neutral understanding of the issue presented. It also breaks down the barrier between journalist and user and implies trust in the user to interpret and organise the data in an intelligent way.

The graph also uses visual symbols to organise the large amount of data. The map of England and Wales is easily recognisable, as is many of the counties. The counties that are under the national average are a light shade of blue and this gets darker as the percentage increases. The use of blue and purple makes the map visually attractive and the differences in shade easily identifiable. It is apparent that darker areas cluster together and that generally the North of England is darker than the South. In this way the user can obtain information from the visualisation by looking at it alone. The darker shade of purple stands out amongst the generally lighter shades and thus the graphic signals to the reader some of the most dramatic information. Thus, although the user is given control and the freedom to explore the data and draw their own conclusions, visual signals guide them to the most extreme data.

The orange circle that is drawn around a county when it is selected contrasts with the blue, making it clear. It also correlates with the colour of the line graph, making the visualisation easily readable.

By pressing ‘play’ the user can focus on one county and see how it breaks down by each year, as well as how the colours across the UK has changed by year, thus presenting more information.

The visualisation thus works as it presents a large amount of data comprehensibly. It allows the user to interpret and organise the data, but gives them visual signals to guide them. It also gives information for the whole country, as well as localised data, thus presenting the ‘big picture’. It is clear and easy to read and breaks down the barrier between journalist and user. It is therefore an excellent way to present the data.

Infographics in sport: an interactive guide to Super Bowl history

Data journalism has enjoyed increasing exposure both within and without journalistic circles in recent years. One of the most visible examples of this has been the proliferation of infographics – a broad term covering a variety of visual story-telling tools and techniques. The quality of infographics you will find online today is very wide ranging, but increasingly some of the best examples have come from analysis of sporting events.

One such example is this effort, shown below, created by Phil Nottingham. This infographic allows users to view key statistics from every Super Bowl in NFL history, right back to Super Bowl I, where the Green Bay Packers beat the Kansas City Chiefs by 35 points to 10 at the Coliseum in 1967.

Super Bowl XXV saw the Giants overturn a 9 point deficit to beat the Bills

 

Interactivity is often key to the success of an infographic, particularly when it is not being used to communicate a news story. In this example, users can engage with the tool by choosing from which Super Bowl to view key statistics.

If you’re a Colts fan, simply select the team and you can then either dissect the defeat to the Saints, or scour the success over the Bears back in 2007. Alternatively, if you’re a neutral, or just want a more holistic experience, browse by year rather than team, and pore over any match-up from Super Bowl I to last year’s clash between the Packers and the Steelers.

For every year, a comprehensive list of statistics allows the user to see how the respective teams fared in offense and defense, with data shown for a whole host of factors, including rushing, first downs, fumbles and interceptions.

As well as the individual stats, users can get a clear idea of how a match progressed through the infographic’s main panel. Here, the teams’ scores are plotted over the course of the match, which provides a great way of reliving some of the great comebacks. Take Super Bowl XXV for example, where the chart shows the Giants’ yellow line well below the Bills’ line in the second quarter, but then soaring up and overtaking in the dying minutes.

Since the turn of the millennium, the number of people providing statistical analysis of sporting events has grown enormously, and below are two more of the Data Blog’s favourite sports infographics (they’re both from the world of football (soccer), but I assure you this is because of their brilliance, rather than any underlying bias):

  • Using Tableau Public, Graham MacAree created this spectacularly detailed visual analysis of Chelsea FC’s match against Norwich on 27 August last year. Users can see exactly where each Chelsea player directed every one of their passes, at what point in the game each one was played and whether or not it was complete.
  • The guys at Visual Evolution have put together this fascinating infographic illustrating the nationalities of football’s top 100 earners (based on their annual salaries), breaking the figures down to show – among other things – which leagues and clubs have most representatives in the top 100, the average age of the top earners and the number of homes Wayne Rooney could buy in his home district of Croxteth with his year’s pay packet.

Data visualisation: in defence of bad graphics

THE GUARDIAN’S DATABLOG – By 

Well, not really – but there is a backlash gathering steam against web data visualisations. Is it deserved?

Most popular infographics

Most popular infographics by Alberto Antoniazzi

Are most online data visualisations, well, just not very good?

It’s an issue we grapple with a lot – and some of you may have noticed a recent backlash against many of the most common data visualisations online.

Poor Wordle – it gets the brunt of it. It was designed as an academic exercise that has turned into a common way of showing word frequencies (and yes, we are guilty of using it) – an online sensation. There’s nothing like ubiquitousness to turn people against you.

In the last week alone, New York Times senior software architect Jacob Harris has called for an end to word clouds, describing them as the “mullets of the Internet“. Although it has used them to great effect here.

While on Poynter, the line is that “People are tired of bad infographics, so make good ones

Awesomely bad infographicsAwesomely bad infographics from How to Interactive Design Photograph: How To Interactive Design

Grace Dobush has written a great post explaining how to produce clear graphics, but can’t resist a cry for reason.

What’s the big deal? Everybody’s doing it, right? If you put [Infographic] in a blog post title, people are going to click on it, because they straight up can’t get enough of that crap. Flowcharts for determining what recipe you should make for dinner tonight! Venn diagrams for nerdy jokes! Pie charts for statistics that don’t actually make any sense! I have just one question—are you trying to make Edward Tufte cry?

Oh and there has also been a call for a pogrom of online data visualisersfrom Gizmodo’s Jesus Diaz:

The number of design-deficient morons making these is so ridiculous that you can fill an island with them. I’d do that. And then nuke it

A little extreme, no?

There has definitely been a shift. A few years ago, the only free data visualisation tools were clunky things that could barely produce a decent line chart, so the explosion in people just getting on and doing it themselves was liberating. Now, there’s a move back towards actually making things look, er, nice. [Read more…]

 

Twitter: Is It All About Timing? [Infographic]

SOCIALMOUTHS (Original post can be found here)

Yes, it feels good to talk about Twitter after spending a full week discussing Facebook’s major announcement. And why not, a little bit of Google+ too.

By the way, I was just reading today’s post from Jeff Bullasand I’m happy to see that even when Facebook hijacked the Internet, Google+ was able to grow nearly 9 million in 2 days. That’s pretty impressive.

But like I said, let’s please talk about Twitter for a change.

This infographic from Lemon.ly talks about Twitter timing and I thought I’d share it with you because it seems to be a regular concern. One of the questions I often get from clients and readers is “What is the best time to Tweet?” and while I think there is no one-size-fits-all kind of answer for this, at least this data lets you visualize a trend.

For example in my case, I agree with the AM timeframe but in the PM, I find that I get the most activity and best results between 3 and 5pm. I also agree with the usage percentage by day of the week although one of my favorite days to spend on Twitter are Fridays and according to this analysis it doesn’t get the action Tuesdays get. Like I said, that’s just me, the point is that you might have your own preferences.

The important thing is that you allow yourself to test the waters.

That’s not all on the infographic, there are other interesting numbers like how many Tweets happen per second or what was the hottest event. Let’s take a look at it and share your thoughts in the comments section.

Twitter timing infographic

Infographic courtesy of Lemon.ly

Over To You

What do you think? What are YOUR best times to tweet? Are you more active on certain days of the week? Share your comments!

4 Simple Tools for Creating an Infographic Resume

Editor’s note: As data journalists, designers or other data enthusiasts, what a better way to show off your skills than with an infographic resume? Here is a very useful article by Mashable’s  introducing four very interesting tools to make your profile stand out! Show us your infographic resume in our Data Art Corner. The best examples will be featured in the DJB’s front page next month!

MASHABLE – By 

As a freelancer or job seeker, it is important to have a resume that stands out among the rest — one of the more visually pleasing options on the market today is the infographic resume.

An infographic resume enables a job seeker to better visualize his or her career history, education and skills.

Unfortunately, not everyone is a graphic designer, and whipping up a professional-looking infographic resume can be a difficult task for the technically unskilled job seeker. For those of us not talented in design, it can also be costly to hire an experienced designer to toil over a career-centric infographic.

Luckily, a number of companies are picking up on this growing trend and building apps to enable the average job seeker to create a beautiful resume.

To spruce up your resume, check out these four tools for creating an infographic CV. If you’ve seen other tools on the market, let us know about them in the comments below.


1. Vizualize.me


 

 
 

 

Vizualize.me is a new app that turns a user’s LinkedIn profile information into a beautiful, web-based infographic.

After creating an account and connecting via LinkedIn, a user can edit his or her profile summary, work experience, education, links, skills, interests, languages, stats, recommendations and awards. And voila, astunning infographic is created.

The company’s vision is to “be the future of resumes.” Lofty goal, but completely viable, given that its iteration of the resume is much more compelling than the simple, black-and-white paper version that currently rules the world.


2. Re.vu


 

 
 

 

Re.vu, a newer name on the market, is another app that enables a user to pull in and edit his or her LinkedIn data to produce a stylish web-based infographic.

The infographic layout focuses on the user’s name, title, biography, social links and career timeline — it also enables a user to add more graphics, including stats, skill evolution, proficiencies, quotes and interests over time.

Besides the career timeline that is fully generated via the LinkedIn connection, the other graphics can be a bit tedious to create, as all of the details must be entered manually.

In the end, though, a very attractive infographic resume emerges. This is, by far, the most visually pleasing option of all of the apps we reviewed.


3. Kinzaa


 

 
 

 

Based on a user’s imported LinkedIn data, Kinzaa creates a data-driven infographic resume that focuses on a user’s skills and job responsibilities throughout his or her work history.

The tool is still in beta, so it can be a bit wonky at times — but if you’re looking for a tool that helps outline exactly how you’ve divided your time in previous positions, this may be your tool of choice.

Unlike other tools, it also features a section outlining the user’s personality and work environment preferences. Details such as preferences on company size, job security, challenge level, culture, decision-making speed and more are outlined in the personality section, while the work environment section focuses on the user’s work-day length, team size, noise level, dress code and travel preferences.


4. Brazen Careerist Facebook App


 

 
 

 

Brazen Careerist, the career management resource for young professionals, launched a new Facebook application in September that generates an infographic resume from a user’s FacebookTwitter and LinkedIn information.

After a user authorizes the app to access his or her Facebook and LinkedIn data, the app creates an infographic resume with a unique URL — for example, my infographic resume is located atbrazen.me/u/ericaswallow.

The infographic features a user’s honors, years of experience, recommendations, network reach, degree information, specialty keywords, career timeline, social links and LinkedIn profile image.

The app also creates a “Career Portfolio” section which features badges awarded based on a user’s Facebook, Twitter and LinkedIn achievements. Upon signing up for the app, I earned eight badges, including “social media ninja,” “team player” and “CEO in training.” While badges are a nice addition, they aren’t compelling enough to keep me coming back to the app.