Tag Archives: Data

Fatalities on the road

Crushed car RTA by Emilian Robert Vicol

Crushed-car by Emilian Robert Vicol, Flickr Creative Commons 

Ever so often, usually when stuck in traffic, I contemplate just how dangerous are roads in the UK compared to other countries. I suppose you do too. Probably at a similar time, whilst at a standstill on a motorway, when the police and ambulances go shrieking past.

In 2013 the World Health Organisation (WHO) published its Global Status Report on Road Safety, which again highlights that road traffic accidents (RTAs) are the leading cause of death for young people (aged 5-29), killing more people than malaria.

RTA chart

Chart showing fatalities from RTAs across different countries adjusted for population

Each year around 1.25m people are killed in traffic accidents globally. WHO Director-General, Margaret Chan, has previously said, “Road traffic crashes are a public health and development crisis,” adding,

The vast majority of those affected are young people in developing countries.

We are in the UN decade of Action for Road Safety. There is an ongoing drive (excuse the pun) to reduce deaths on the road by 50% by 2020, with experts estimating that five million lives could be saved. Currently, annual deaths are predicted to rise to 1.9m by the end of the decade.

So where are the world’s most dangerous roads? Using infogr.am to make a tree-map, I’ve highlighted data pertaining to certain key countries at both ends of this fatal scale.

RTA fig

Road fatalities by country – Picture by Namal Perera on infogr.am

 

Key points from WHO data:-

  • middle-income countries account for around 80% of RTA deaths but are home to only around 50% of the world’s registered vehicles: they therefore have a disproportionately high burden of deaths.
  • Eritrea is estimated to have the highest number of road deaths (48.4 per 100,000 people). This is taken from 2009 data however.
  • The world’s most populous countries, China and India, have the highest absolute number of recorded road deaths (275,983 and 243,475 respectively) but lie mid-table when adjusting for population.
  • In Africa, Nigeria has the largest population and buys the most cars. South Africa has the highest car ownership per capita. Both are in the top 10 (7th and 8th) when it comes to road fatalities (34 and 32 deaths per 100,000 population).
  • San Marino has the best record according to the WHO, with zero fatalities on its roads (2010 data). However, the tragic deaths of Formula 1 legend Ayrton Senna and Roland Ratzenberger during the 1994 grand prix weekend are more than enough for this tiny enclave to cope with.
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Introduction: one doc’s issues with data

I thought I might struggle with data journalism. And not just because of time constraints, or my own IT limitations. I guess it’s because I didn’t know what to expect.  Having learned the fundamentals of using data and a reasonable grasp of basic stats (during research at school, university and work), I enter the field of data journalism with an open mind but a little skepticism.

Sean MacEntee

Data Recovery.  Picture by Sean MacEntee, Flickr Creative Commons

I’ve often been irritated by stories in the press, which don’t do justice to the actual figures. “Data” seems to be thrown around in the media to add authority, a buzz-word to proclaim truth. I believe that the majority of journalists, like scientists, do a decent job when it comes to the numbers behind the stories. However from time to time, there appears to be a woeful lack of insight during the interpretation of these numbers.

Ben Goldacre’s Bad Science column / blog / book and Paul Bradshaw’s Online Journalism Blog are excellent resources for de-bunking scientific or medical myths. They highlight how journalists, politicians and scientists can mislead the public at almost every stage of research – from the methodology, to the interpretation of results.

So, where to start? Taking the advice of my journalism tutor and Dr Goldacre, I just started writing – if only to vent some of my frustrations. These mainly stem from several data stories that appear (to me, on closer reading) incomplete, misrepresented or overestimated in value.

Even the word itself – data, singular datum – causes contention. Let’s be clear: as a mass noun to signify information, it is perfectly acceptable to use data in the singular, although the (more pedantic?) academic types often prefer to acknowledge the Latin roots of the word and would say “these data show” as each piece of information is a datum.

I want people to see things as they truly are, through the objectivity that data offers. This requires both reliable sources and recording of data, and accurate interpretation. Data journalists will have their own styles and opinions, but robust data analysis should yield clear and consistent meanings.

So, here are a few things I’d like to cover: sources of data, presentation and basic analysis. The latter will not involve much in the way of statistics. I also aim to critique some data stories as well as try out software and online tools for my own data stories.

Lastly, for my blog-posts I’d like to invoke my own extension of the “KISS” acronym – now “KISSASS” =

keep it short, sweet, and simple, stupid

Short: I see that around 500 words is recommended for blog-posts, although I can’t say with any conviction what the ideal word is. “Sweet” really means selective and stimulating – one post for one (interesting) idea. And simple: where possible it should be understandable to almost everyone.

I hope it works out and I’m keen to hear your comments.

by bixentro

Picture by bixentro, Flickr Creative Commons