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We’re all acquainted with the perks and limitations of using Google Fusion, Tableau, Many Eyes and Open Street Map but I was wondering what other visualisation and infographic tools are readily accessible and available for wannabe data journalists. And more specifically, wannabe data journalists with no graphic design experience (like me).
Continue reading “Simple infographic and visualisation tools”
This heat map, produced on Google Fusion Tables presents the levels of overcrowding in London, as in 2010. The data was published by the London datastore in March 2012 and presented figures for both the base figure (number of houses overcrowded) and that number as a percentage of the whole borough. The information mapped here is the percentage figures, however, the map is interactive, and clicking on each borough will give you the base figure information.
Overcrowding for the purposes of this data set is defined by the ‘bedroom standard’, explained on the London Datastore website:
‘Bedroom standard’ is used as an indicator of occupation density. A standard number of bedrooms is allocated to each household in accordance with its age/sex/marital status composition and the relationship of the members to one another. A separate bedroom is allocated to each married or cohabiting couple, any other person aged 21 or over, each pair of adolescents aged 10 – 20 of the same sex, and each pair of children under 10. Any unpaired person aged 10 – 20 is paired, if possible with a child under 10 of the same sex, or, if that is not possible, he or she is given a separate bedroom, as is any unpaired child under 10. This standard is then compared with the actual number of bedrooms (including bed-sitters) available for the sole use of the household, and differences are tabulated. Bedrooms converted to other uses are not counted as available unless they have been denoted as bedrooms by the informants; bedrooms not actually in use are counted unless uninhabitable.
Overcrowding figures include households with at least one bedroom too few.
The map clearly shows that the borough of Newham has the worst overcrowding situation with 17.9 per cent of households in the borough overcrowded; a figure of 506 households. However, the borough with the highest base rate figure was Enfield, with 670 households being overcrowded, 7.1 per cent of the borough.
In July 2011, Shelter Housing charity announced that their analysis of the English Housing Survey revealed that 391,000 children (24 per cent) in London suffered from overcrowding, which they said was an 18 per cent rise since 2008. Unfortunately the data provided by the Datastore did not break down the data into person specification categories, however, it is clear from the figures that overcrowding in London is a growing problem.
The Fusion Map was created by importing data from a spreadsheet into Google Fusion Tables and then merging this data with KML shape files for London, to get the heat map effect. Each Borough has an identification code, which my original data did not have, so I had to input this manually for each borough in my original data set before merging the two files – this provides a point of reference for the merge, so there are two identical pieces of information for each row to match up with.
I then chose the column I wanted the map to represent, in this case the percentage data. To get the map to show a range of colours related to the data, I set the map to have what are called “buckets”, this is the range of numbers represented by each colour. I then modified the colours I wanted to use with a system called colorbrewer, which allows you to customize colours showing specific colour ranges for heat maps. Finally I modified the information windows for each borough to show specifically the information I wanted, and in the style I wanted – this takes a small amount of HTML know-how.
I hope you enjoy this data map, and are inspired to create your own maps using Google Fusion in the future.
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.
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”
The Guardian is one of the most respected newspaper when it comes to data journalism and data visualizations. Their website has a section dedicated to data where people can enjoy beautiful infographics made by the likes of David McCandless and other data visionaries.
We met with Simon at his Guardian’s desk to talk about the Data Blog and the impact of Wikileaks on journalism. Look out for his tips on good data visualizations!
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