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.

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”

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”

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”

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.

 

75+ Tools for Visualizing your Data, CSS, Flash, jQuery, PHP

Editor’s note: Back in 2009, Tripwire magazine published this comprehensive article about data visualisation tools. Although technology has come a long way since then, most of their tips are still relevant so we thought you’d like to take a pick. You’ll find some old school know-how to bring your data to life. Enjoy!

TRIPWIRE MAGAZINE – By LARS

Most people would agree that the old adage “A picture is worth a thousand words” is also true for web based solutions. There should be no discussion – Charts and Graphs are ideal to visualize data in order to quickly deliver an overview and communicate key messages. Whatever type of data presentation you prefer or suits you data (pie charts, bubble charts, bar graphs, network diagrams etc.), there are many different options but how do you get started and what is technologically possible? In this article tripwire magazine present more than 75 Tools for Visualizing your data on a website and most of the options available will be covered. If you are aware of a tool, script etc. that deserves to be added to the list I would kindly ask you to leave a comment to everyone’s benefit.

Introduction

Images says more than a thousands words. It is common sense and wise people has followed this rule for centuries by creating illustrations of thier ideas and thoughts. Today it is easier than ever as the technology for presenting nearly any type of information as a graph or chart on a web page is getting really mature. Reading through this article you will be faced with the problem on what technology and specific implementation you should use. It is not a trivial question and I recommend that you use comments on this article to share your ideas, concerns etc. with peer readers. This way you may get the input from the community that you need to create the optimal solution.

The article has been organised into the following sections.

Section 1: How to visualize you data using Javascript-based solutions

Section 2: How to visualize you data using CSS

Section 3: How to visualize you data using Server-side Solutions

Section 4: How to visualize you data using FLASH-based solutions

Section 5: How to visualize you data using Online Tools and Services

How to visualize you data using Javascript-based solutions

jqPlot Charts and Graphs for jQuery

The feature rich jqPlot is a plotting and charting plugin for the jQuery Javascript framework. There are plenty of hooks into the core jqPlot code allowing for custom event handlers, creation of new plot types and adding canvases to the plot.

jQuery

flot – Attractive Javascript plotting for jQuery

Flot is a pure Javascript plotting library for jQuery. It produces graphical plots of arbitrary datasets on-the-fly client-side. It has been developed with focus on simple usage (all settings are optional), attractive looks and interactive features like zooming and mouse tracking.
The plugin works with Internet Explorer 6/7/8, Firefox 2.x+, Safari 3.0+, Opera 9.5+ and Konqueror 4.x+ with the HTML canvas tag (Internet Explorer where the excanvas Javascript emulation helper is used).

jQuery

jQuery Sparklines

This jQuery plugin generates sparklines (small inline charts) directly in the browser using data supplied either inline in the HTML, or via javascript all with a single line of code.
The plugin is compatible with most modern browsers and has been tested with Firefox 2+, Safari 3+, Opera 9, Google Chrome and Internet Explorer 6, 7 & 8.

jQuery

Flotr Javascript Plotting Library

Flotr is a javascript plotting library based on the Prototype Javascript Framework and has been inspired by Flot (above). [Read more…]

How does Google make money? [INFOGRAPHIC]

So Google is pretty much in every part of our online existence now. It helps us find information about random things, access our emails, watch videos online, sort our friends into circles, get analytics for our websites. It even helps us do data visualisations thanks to Google Fusion Tables.

There is no doubt that Google is making a lot of money but what is the actual break-down? Well, here is an awesome infographic found on the Awesome Inforgaphics website that answer that question. What do you think?

Nato operations in Libya: data journalism breaks down which country does what

THE GUARDIAN’S DATA BLOG – By 

How many Nato attacks took place over Libya – and what did they hit? Here’s the most comprehensive analysis yet of who did what
• Get the data

Nato in Libya graphic

 

Nato‘s Libya operations have cost millions and involved thousands of airmen and sailors. But who’s contributed to Operation Unified Protector? That’s the official name for the attacks on the Gadaffi regime’s bases and tanks by Nato aircraft and ships, plus the enforcement of the no-fly zone and the arms embargo.

We have been monitoring the Nato situation updates which are released each day and give details of the operations – key targets hit, sorties flown and ships boarded.