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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.

 

 

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Data visualisation and Art

Data is often seen to be as far removed from art as can be. A way of obtaining facts, information and statistics, it is technological rather than personal and beautiful.

Or is it?

Many designers and artists have straddled the line between art and information and used infographics and visualisations to create something that is not only relevant, but beautiful.

Bryan Christie, a New York based designer who regularly produces visualisations for the New York Times, is doing just that. Christie has used a combination of medical text books and MRI scans to reproduce the human hand in a virtual 3D space. The image is both scientific and beautiful.

http://ngm.nationalgeographic.com/2012/05/hands/zimmer-text

Christie said: “The medium I work in is a new form of photography; it is both sculptural and photographic. I model the figures in digital 3D on the computer then use a virtual camera within the computer to take a picture of the piece. There’s an interesting process that occurs in that my work is sculptural and exists in virtual three-dimensional space yet in the end it is viewed in two dimensions much like a photograph.”

Another example of the crossover between data, art and popular culture is Radiohead’s music video for House of Cards from their 2008 album, In Rainbows. The video was created using data visualisations created by Aaron Koblin. It uses 3D plotting technologies to collect information about the shapes and relative distances of objects, including lead singer Thom Yorke’s face, and then visualises the data. The result is eerily beautiful and surprisingly human, with the fragile nature of the lyrics and Yorke’s ethereal vocals perfectly complimented by the ghostly appearance of his face and the disconnected nature of the overall visual image.

http://www.youtube.com/watch?v=8nTFjVm9sTQ

Artists, too, have recognised the potential of data in art. In 2008 the Museum of Modern Art featured an exhibition by Harris and Kamvar, entitled I Want You to Want Me, and which made use of data visualisations to present the pieces. The exhibition, which included a fully interactive 56 inch touch screen installation, chronicles the world’s long-term relationship with romance, and gathered data from a variety of online dating sites in order to give viewers an insight into people’s personal lives. I Want You to Want Me was a beautiful collaboration of computer science, maths and art that uses data to evoke viewers’ emotions at a very personal level. The infographic image further raised questions about the virtual nature of modern relationships regarding dating sites.

http://iwantyoutowantme.org/statement.html

Data visualisations are thus not only an informative way to present a narrative, but can also be a beautiful one. In certain circumstances it can even be considered to be art.

 

 

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Why Data Journalism is Important

After studying Data Journalism for a year at City University I have come to appreciate the importance of having the skillset to make the most out of numbers and statistics. Many aspiring journalists still see data as something that is separate from journalism, and as something that does not interest them. In response, I have compiled some reasons why data is increasingly important:

1.       Make sense of Mass Information

Having the skills to scrape, analyse, clean and present data allows journalists to present complicated and otherwise incomprehensible information in a clear way. It is an essential part of journalism to find material and present it to the public. Understanding data allows journalists to do this with large amounts of information, which would otherwise be impossible to understand.

2.       New Approaches to Storytelling

Able to create infographics and visualisations, data journalists can see and present information in a new and interesting way. Stories no longer need to be linear and based solely on text. Data can be grafted into a narrative which people can read visually. Interactive elements of data visualisations allow people to explore the information presented and make sense of it in their own way.

3.       Data Journalism is the Future

Understanding data now will put journalists ahead of the game. Information is increasingly being sourced and presented using data. Journalists who refuse to adapt to the modern, increasingly technological world will be unable to get the best stories, by-lines and scoops and their careers will suffer as a result.

4.       Save Time

No longer must journalists pore over spread-sheets and numbers for hours when there could be a simpler way to organise the information. Being technologically savvy and knowing the skills to apply to data sets can save journalists time when cleaning, organising and making sense of data. Not making mistakes due to lack of knowledge can also save a journalist time.

5.       A way to see things you might otherwise not see

Understanding large data sets can allow journalists to see significant information that they might otherwise have overlooked. Equally, some stories are best told using data visualisations as this enables people to see things that they might otherwise have been unable to understand.

 6.       A way to tell richer stories

Combining traditional methods of storytelling with data visualisations, infographics, video or photographs, creates richer, more interesting and detailed stories.

7.       Data is an essential part of Journalism

Many journalists do not see data as a specialist and separate area of journalism, but an interwoven, essential and important element of it. It is not there to replace traditional methods of finding information, but to enhance them. The journalist that can combine a good contact book and an understanding of data will be invaluable in the future.

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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.

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Visualisation Analysis #2

Simon Rogers has created a visualisation showing death penalty statistics, country by country, for the Guardian Data Blog.

http://bit.ly/hdFOpa

http://bit.ly/hflX1V

The visualisation uses a bubble graph on a map of the world to depict how many people have been given death sentences and how many people have been executed in 2011. This is then broken down by country, giving users the opportunity to compare and contrast regions.

Continue reading “Visualisation Analysis #2”

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Visualisation Analysis #1

Following on from my earlier post exploring different ways to present data, I have decided to analyse two examples of visualisations from the Guardian Data Store.

http://bit.ly/HsqsLf

The first is a map of UK fuel shortages; ‘The Petrol Panic Mapped’. The map works because it is clear, simple and easy to use. The map is interactive, giving the user control and allowing them to display the information in the way that best suits them, prioritising data that they find most interesting. It also makes viewing the map a more entertaining experience, keeping users on the page for longer.

Continue reading “Visualisation Analysis #1”

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How to do a good visualisation and why it’s important

Visualisations are an important tool when presenting data, and can be used to show patterns, correlations and the ‘big picture’.

Ben Fry has said that visualisations ‘answer questions in a meaningful way that makes answers accessible to others’ and Paul Bradshaw explains that ‘visualisation is the process of giving a visual form to information which is otherwise dry or impenetrable.’

Traditionally stories have been conveyed through text, and visualisations have been used to display additional or supporting information. Recently, however, improved software has allowed journalists to create sophisticated narrative visualisations that are increasingly being used as standalone stories. These can be be linear and interactive, inviting verification, new questions and alternative explanations.

Continue reading “How to do a good visualisation and why it’s important”

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How to find Data

This post is for people who are new to data sourcing, or interested in Data Journalism but unsure of where to begin.

First, it is useful to start with an idea, question or hypothesis. In Story Based Enquiry Mark Lee Hunter emphasises the importance of having an idea of what you are looking for in data.

He said: “We do not think that the only issue is finding information. Instead, we think that the core task is telling the story. Stories are the cement which holds together every step of the investigative process, from conception to research, writing, quality control and publication.”

Data stories and visualisations are part of journalism and, when looking for information, a good starting place is to use traditional journalistic methods. Contacts, tip offs, interviews and research can all point you in the direction of interesting data, and of questions that could be answered by statistics. This is known as Active Data Journalism.

Continue reading “How to find Data”

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Visualisation showing patients detained under the Mental Health Act 1983

Here I have created a visualisation showing patients detained under the Mental Health Act 1983 over the last six years.

I took statistics from the mental health pages of the NHS website and downloaded them into an Excel spreadsheet. I then cleaned the data, taking out any information that was unnecessary and that would confuse the image. I rearranged the columns, data and information and made it easier to understand and clearer, visually.

I then experimented with Many Eyes, Google Docs and Excel graphs to create the visualisation. I tried other ways of presenting the image, in a pie chart and a line graph, but found that the bar chart worked best.

The information is broken down by gender as well as by type of hospital; NHS Facilities and Independent hospitals. The graph shows that more men have been detained under the mental health act than women, on a year by year basis. This is consistent with both NHS Facilities and Independent Hospitals. The number of men detained has also gone up marginally in the last two years, though has stayed relatively consistent over the last six years.

This is interesting because statistics have indicated that more women than men are diagnosed with mental health disorders, such as depression and anxiety. However, when it comes to severe cases, where patients are legally detained due to mental illness, men are significantly more likely to be affected.

 

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Treemap for Comparisons showing Hospital Waiting Times

Visualising data is important because it makes complicated information easy to process and understand.

Here I took some data from the Office for National Statistics website, www.ons.gov.uk. I chose to look at Hospital waiting times because it is an issue that is often reported on and I was curious as to which treatments had the longest waiting time.

I took the data and copied it into an Excel spreadsheet. I then cut it down to get rid of information that I didn’t need and that would confuse the user. I organised the data into columns, the first showing the type of treatment, the second the amount of completed treatments and operations and the third showing the average median waiting time in weeks.

Continue reading “Treemap for Comparisons showing Hospital Waiting Times”