“It used to be that you would get stories by chatting to people in bars, and it still might be that you’ll do it that way sometimes.
But now it’s also going to be about poring over data and equipping yourself with the tools to analyse it and picking out what’s interesting. And keeping it in perspective, helping out people by really seeing where it all fits together, and what’s going on in the country.”
Tim Berners-Lee, November 2010
By 1967 early morning police raid of a unlicensed bar in Detroit sets off looting, fires and shooting.
By the time it ends six days later:
- 43 people were dead
- hundreds were injured
- more than 7,000 people had been arrested
- entire city blocks destroyed by fire
Enter Philip Meyer:
Afterwards people wanted to know why and there were two theories:
Those who looted and burned buildings were on the bottom rung of society- no money and no education.
Rioters were recent arrivals from the South who had failed to assimilate and were venting their frustrations on the city.
Reading the riots:
Among the findings:
- There was no correlation between economic status and participation in the distance
- College-educated residents were as likely as high school dropouts to have taken part.
- Recent immigrants from the the South had not played a major role; in fact, Northerners were three times as likely to have rioted.
Grievances: police brutality; overcrowded living conditions; poor housing and lack of jobs.
Everything is numbers:
“Any event can be described by fundamental data; latitude, longitude, date and time, and importance. If every piece of content had at least those four pieces of meta information, we could offer consumers a tailored package of news that happened near them in the time since their last connection, for instance.
“Nevertheless, the ‘importance’ filter mentioned above highlights that the whole process demands human subjectivity and cannot be left to computers alone.”
Voices: News organisations must become hubs for trusted data in a market seeking (and valuing) trust.
By Mirko Lorenz, Nicolas Kayser-Bril, and Geoff McGhee
The Inverted Pyramid of Data Journalism:
Interrogating- where’s the story?
Who, What, Why, When, Where, How
- What is the average?
- Who is top? Bottom?
- Time: what has happened since last year? 10 years ago?
- Space: trends in fields/regions?
- What is the context
Above all: use your news sense
“I believe quality and “gourmet” journalism, empowered by data, is not only the watchdog needed to keep our leaders accountable. It is also the hope for the development of new and successful business models for journalism.”
The future according to Tow:
1.) Data will become even more of a strategic resource for media.
2.) Better tools will emerge that democratize data skills.
3.) News apps will explode as a primary way for people to consume data journalism.
4.) Being digital first means being data-centric and mobile friendly.
5.) Expect more robo-journalism, but know that human relationships and storytelling still matter.
6.) Data journalism will be held to a higher standards of accuracy and corrections.
7.) Competency in security and data protection will become more important.
8.) Audiences will demands more transparency on reader data collection and use.
9.) Conflicts will emerge over public records, data scraping, and ethics.
10.) Collaboration will arise with libraries and universities as archives, hosts, and educators.
11.) Expect data-driven personalization and predictive news in wearable interfaces.
12.) More diverse newsrooms will produce better data journalism
Howard, A (2014) The Science and Art of Data-Driven Journalism, Tow Centre for Digital Journalism.