Data Sutra

Data-driven posts and tutorials, by me.

[billyehrenberg]

Has a drop in binge drinking really caused a fall in violent crime?

Lots of papers led today on the story of a decline in binge drinking causing a fall in the rates of violence related crime.

But how significant is the drop in crime, and can it be attributed to alcohol?

Firstly it is important to acknowledge that violent crime does seem to be falling. In the sample reviewed by the Cardiff university study, figures fell across all age groups between 2012 and 2013.

The “shock drop in violent crime” that The Times led with is hard to justify, because the data shows that violent crime has been falling for several years. In fact, it fell by less in 2013 than it did in the previous 12 months, by 12% to 14%.

This could be a case of publications sticking two news stories together and assuming correlation equals causation.

The alcohol factor

Binge drinking is declining, and it is logical to assume that this will lead to a fall in the evils related with excessive drunkenness.

The report doesn’t set out to test this link however, and alcohol is just one of several social factors mentioned in the text.

“Reasons for decreases in violence are not clear, but are likely to be multi-factorial and complex. These could include changes in structural factors such as unemployment, poverty and inequality in addition to public health and criminal justice interventions.

“Since 2008, affordability of alcohol has decreased… and UK alcohol consumption levels have decreased from 10.8 (in 2008) to 10 litres per capita (in 2011).  These factors may partly explain the falls in serious violence.”

So rising unemployment (down to 6.9%) could be playing a part too, as could improved living conditions or policing methods.

The gender and age factors

Gender and age make a big difference to the numbers. Males are far more likely to be admitted to hospital as a result of violent crime, as are young adults. The behaviour of these groups, in which alcohol is only one factor, should be considered as a whole.

Of every 1,000 men, 6.07 will become a victim of violence while only 2.3 women will.

The 18-30 age group is most at risk, with the victim rate per 1000 persons rising to 11.54. For 51+ it is 0.96. Incidents also reduced significantly more among women, for unknown reasons.

As a 2011 Guardian article by Cynthia Cockburn and Ann Oakley pointed out:

“In 2009-10, men were perpetrators in 91% of all violent incidents in England and Wales. The figures vary by type of incident: 81% for domestic violence, 86% for assault, 94% for wounding, 96% for mugging, 98% for robbery.”

So as the report itself said, any number of factors could have contributed to this latest fall in a recent history of success. To give all the credit to a reduction in alcohol consumption is probably wishful thinking.

[billyehrenberg]

What skills do you need to survive in the future of data journalism? Interview with The Times's Nicola Hughes

Nicola Hughes

When you think of data journalism it's probably interactive maps or infographics, or perhaps the Vox.com’s of the press world using statistics, maps and charts to better explain the news.

There is another side to data journalism though, which is less overtly glamorous but possibly more important. It is the more traditional side, looking through raw data to find the stories that would otherwise be left untold.

Nicola Hughes (@dataminer), is a data journalist who does just that - and she's pretty good at it. 

Nicola works at The Times, and is one part of the group Chicas Poderosas (which means powerful girls in Spanish), "a movement to bring woman together in technology newsrooms in Latin America" according to its Twitter bio. 

Hughes says that the field of data journalism is changing, reaching it’s tipping point.

It is no longer enough for budding journalists to know the ins and outs of fusion tables or off-the-shelf software.

“This whole taking a press release, putting it into a bar chart and writing about it, I think there’s not so much of a want from the audience for that.”

Instead, Nicola says, data journalists need to return to their journalistic roots. Stories come from numbers and to rely only on government statistics and press releases is no longer enough.

Getting raw data in machine readable format may well be the future, because it is no longer in the government’s interest to cater to journalists by looking for stories and building press releases.

“Now all these sites like data.gov, they don’t care about journalists. The reason to open data is for businesses, is for open-source communities to build things around it.

“The journalist used to be the first in line to ask for information, now we are not.

"If we want to be in that queue, we need to be the people that they are catering for, which are actually now developers.”

What Nicola means is that data is often only available in machine readable format and the amount may be huge, perhaps several gigabytes, and far too much to parse in an Excel spreadsheet or Google sheet.

Computers on the other hand can read pretty much any data you give them, but to tap that power you have to know the nuts and bolts of how a computer works. That means the command lines: the programming beneath the pretty skin of the operating system.

Nicola gives the example of a story on prescriptions that she worked on when she first joined The Times.

“No journalist was able to use it because each month’s data was 2GB and you can’t open that in Excel. Because everything with software you need to open in memory first and most computers, most standard ones, especially in news organisations won’t have the memory for it.

“So I have to go in and do a line by line filter through the command line.

"A computer can read any sort of data, it’s the software that limits you.”

Time to specialise

Does that mean there is no place in journalism for the data lover who wants to work on the visual? On those maps and charts that form the audience facing side of the field?

Not at all, according to Nicola. It is just important to decide what type of journalist you want to be. And with data, you have to love it, you have to want to spend your evenings messing with code or looking at spreadsheets. It something that you’ll constantly be learning and teaching.

“First of all you have to think about where you want to end up, because learning to programme can have multiple end points.

“Just as with developers you have front end and back end. Most people in news organisations are what you call client side. They tend to be Java script HTML, CSS.

“Backend developers tend to be data parsing, data cleaning statisticians, analysis types. What is most coveted is a fullstack developer: someone who doesn't do anything extensively but can take source data, analyse it, structure it put it in a database and then do front end work.

“Before you decide what resources you want to look for, you probably want to decide where you want to end up. Front end, back end, full stack.

“Rather than thinking “this organisation is after this kind of data journalist, this organisation wants that, the best thing to do is to do both things and see which you like more because it’s very much about you own motivations.”

[billyehrenberg]

3.4 million empty homes, falling prices and foreign money: the Spanish housing crisis by numbers

Houses stand empty in the town of Benalmadena, near Malaga on the Costa del Sol. Picture: Billy Ehrenberg 

  Additional reporting by Nassos Stylianou (@nassos_)

Spain has the highest number of empty houses in Europe: 3.4million of them at a time when many are facing evictions for defaulting on their mortgages. House prices in Spain are also at an all-time low, according to Instituto Nacional de Estadística (INE) numbers for December 2013.

What is more with prices so low, record numbers of foreigners have been flocking to buy houses, with numbers tripling since 2009.

These things in themselves are not news; we’ve know this for a while and it’s been well publicised in the press both in Spain and abroad.

What is an interesting exercise is to look at where these empty homes are and how much house prices have dropped in those regions.

We don’t have the data for where exactly expats are buying the most houses, but we can make an educated guess that it will be on the Costa del Sol and the Costa Brava.  

So first lets look at where the empty houses are. I’ve plotted them on a map, with the bigger red bubbles corresponding to larger amounts of empty homes:

  Click on the bubbles to get the stats for each region.

The first thing we notice is that the largest numbers of empty houses are in the Valencian Community and in Andalucía. This figure holds true, even when we look at the percentages:

18.5% of houses in Andalucía are empty - almost a whopping one in five. Valencia isn’t far behind on 14.7%. These are big numbers.

Surprisingly, Cataluña is third with 13% - despite being one of the few regions to have seen a drop in the total number of vacant homes.

As you may have suspected it seems that a large number of houses were built in theses areas because they are popular for second homes, both for Spanish nationals and ex-pats.

So where are the cheapest houses? Or rather, where have the biggest drops occurred?

The INE published the figures, and as you can see from this graph they’ve been dropping steadily since 2007 in some communities, with all areas having experienced large declines.

You can use the drop-down menu in the top-right of the graph to choose which regions you want to include or exclude. Click on the names of the regions in the legend to select the line for that Autonomous Community.

Learn About Tableau

What is interesting is that, despite having the highest number of empty houses (both by volume and %), houses in Andalucía and Valencia have held their prices far better than other regions.

Andalucian properties are going at 73% of their 2007 value and the Comunidad Valenciana is seeing houses sell for 68% of 2007 prices; the third and eighth highest on the list, respectively.

Not so Cataluña. Prices have dropped to 55% of their 2007 level for the Catalans, despite that overall reduction in the number of empty properties.

Why might this be? One possible conclusion is that the number of foreign buyers snapping up cheap properties could well be preventing more pain in Valencia and Andalucía, where the areas around Benidorm and Malaga/Marbella remain favourites for ex pats.

Let’s have a quick look at who it is buying the most properties. Click on the tabs at the top to change between numbers and percentages. 

    By far the biggest buyers are the Brits - which will surprise few - but the figure fails to take into account is that 12.85% of all new house purchases in the fourth quarter were by non-Spaniards. El País has a more in-depth article here.

How long that support will last is anyone’s guess, but the number of foreign buyers is higher even than before the crisis, perhaps showing how any recovery from the crisis is leaving Spain behind.

How long it will last is anyone’s guess.

  Nassos Stylianou is a freelance data journalist working in London. You can read his blog The Data Party here.

[billyehrenberg]

How to scrape like the Data Doctor with OutWit Hub

Scraping is a complicated sounding word which basically means: getting a programme to do any task it would take you ages to complete yourself. Usually the task is repeated, but scraping can just be winnowing the tables you want from a website, for example.

A common problem occurs when you want to get the same piece of information from a bunch of different web pages, or ‘URLs’. It takes ages to go into each page seperately and get the numbers out.

The URL is the address of the page which you want to get information from. Here is one:

http://billyehrenberg.tumblr.com/post/77524260275/of-beer-wayne-rooneys-new-contract-and-data

This URL, incidentally, is for an article I wrote, and the article I’m going to use as an example. The tool I used is called OutWit Hub: the paid for version will allow you to winnow many, many rows of data, but the free version will let you take 100, which isn’t bad.

So first, download OutWit from here.

Now let’s have a look at my article. I wanted to find out (roughly, the data is far from watertight) how many beers a freshly-contracted Wayne Rooney could buy in different countries.

For this I needed the average pint price, and so naturally I went to pintprice.com.

It would have taken ages to copy them all down but I noticed that the URL changed very predictably depending on the country:

http://www.pintprice.com/region.php?/NAME_OF_COUNTRY_HERE/

When you see a URL with only one element changing, think: OutWit Hub.

First stage here is to accept you’re going to have to look at HTML. I know, that’s looking through a page of oddly-spaced gobbledygook, but it is pretty easy if you know what you’re looking for, especially armed with CTRL F.

So let’s venture to PintPrice.com and its Brazil page. But we’ll open it through OutWit, not not our browser.

  Open OutWit and click scrapers on the left hand toolbar

Click the button that says “New”

Name it something

Now we need to find the info we are after. So put the Brazil page URL (http://www.pintprice.com/region.php?/Brazil/GBP.htm) into the bar at the top of OutWit, like so:

  Now what we can see is the webpage as it usually looks, and the source code.

Now we see that the price of a pint there is shown thus:

So when  we delve into the HTML, we’ll look for the word “lager”. We find this:

  Now OutWit works by finding common HTML both before and after the data you want to extract. These bits of code you choose are called markers.

You can’t use Brazil, the, as that’s unique to this page. We want code that will work for Belarus and Barbados and Zimbabwe.

Lets choose this then: </strong> is <strong>£

And this for after: GPB</strong>

Now we need to put this into OutWit and see if it works. At the bottom will be your scraper. Click on the boxes to input the information. “Marker” means those common pieces of HTML we are going to use isolate the data we want. It should look like this when you’re done:

Notice that there is a second URL bar above the scraper: make sure this has www.pintprice.com in it BUT NOT the specific URL.

Now we are ready: press execute, above and to the right of the scraper box. This should happen:

Success! We have scraped a line of data. But what we really want is to get the price for a whole bunch of countries. Here’s how.

First we need to open up an Excel workbook. But note well: Google Docs will not do here, because we need to save it as a webpage later.

Now in this Excel workbook we need to make a list of the URLS that we want to scrape. Lets try five countries worth.

  Brazil

France

Germany

Argentina

Spain

  Before you go any further, remember to check the names on your list are exactly the same as how the countries appear in the URL. If they have an underscore between parts of the name, such as Czech_Republic, put ‘em in. Even a space at the beginning or end will mess this up (Spaces before/after can be removed with the =trim function).

Now in Excel do the following: divide the URL into three cells, with EVERY SINGLE CHARACTER before the country in the first, only the letters in the country’s name in the second, and everything after the country’s name in the third. Voila:

Now underneath Brazil write the names of the other countries, and then drag the first and third cells down by the bottom-right-hand corner until you get this:

It is now time, ladies and gentlemen, for some Excel wizardry to make even the Data Doctor feel “that was pretty easy but cool-ish.”

We need to glue the three cells together. So in the fourth cell enter the following: =CONCATENATE(A1,B1,C1)

Concatenate means “please combine the following with no spaces.” Then drag down (or click on) the bottom-right-hand corner of the fourth cell to get this:

  Wow. We have the exact URLs we want and are nearly there. What we need to do is get rid of the unwanted bits. So select column D, and copy all the info. We want to paste it BUT ONLY THE INFO AS IT APPEARS, NOT THE FORMULAS. If we paste the formulas, we’ll end up with errors when we get rid of the other bits.

So instead of pressing ctrl V, we need to paste special. You can click the clipboard and select values. Or go to edit -paste special - values. Done? Good. Now delete the rest and you should have just the URLs in the first column. Stretch that column out so none of the url protrudes across the border into B.

Now we need to save it in a format that OutWit can use. So go to save as and choose “Save as webpage.”

We’re almost there. Quickly, back to OutWit to import our list of URLs: file - open - your webpage. Easy. You should now have a screen that looks like this:

Now all we need to do is right click on the list of URLs and choose:

  Apply scraper

To highlighted links

  And:

There we have a list of the pint prices in each of the countries, as if by magic. The only thing left to do is to get them into an Excel spreadsheet and ready to use. That’s easy, just choose export and you’re done.

I hope this has helped you scrape something, and that you will use the power for something equally profound as beer. Lovely beer.

[billyehrenberg]

Running mates: Labour vs Tory politicians at the London Marathon

Sunday’s London Marathon is almost upon us and, as well as the elite athletes squaring off there is a bigger battle heating up.

There are nine MPs competing in the 2014 event and Labour and the Tories seem pretty evenly matched:

Labour having the fastest time (Jim Murphy) and the slowest (Ed Balls: Ed Balls).

Rather typically of Cameron’s Conservatives, the Tory runners occupy the centre ground.

As well as the seven in the graph there are two MP debutants: shadow justice secretary and shadow minister for London Sadiq Khan and shadow health secretary Andy Burnham, who is obviously taking his role seriously.

It  seems the Tories are less fit as a party: all Labour athletes are juggling jobs in the shadow cabinet, while the Tories are backbenchers (with the exception of Edward Timpson, the minister for children and families).

So: who will win?

The battle everyone wanted to see, Balls vs Osborne isn’t on with the Chancellor perhaps afraid of accusations of going too far, too fast.

Balls has some work to do if he is to finish ahead of the pack, around one hour and 45 minutes behind the more sprightly Labour MP Murphy.

Murphy however seems to be having some problems with his right leg:

Had first ever acupuncture session today to get my right leg fit for @LondonMarathon Give £10 to 2 great charities >> http://t.co/e2mGHxLvFQ

— Jim Murphy (@jimmurphymp)

April 4, 2014

The way could be set for a Cairns victory with third-placed Jarvis professing to not having done much training, apparently.

.@EveThomas40 haven't done much training, but it's for a great cause @CR_UK !! http://t.co/Bt8UTqRK2J

— Dan Jarvis (@DanJarvisMP)

April 8, 2014

One thing is certain, with the two camps running against each other in the election next year there is more at stake than London's biggest road race.

[billyehrenberg]

Interactive: how bad is pollution in the UK?

Picture by "tatulund", photo reproduced under license.

London is experiencing levels of ambient air pollution over four times those of New York, according to statistics from the World Health Organisation and DeFRA.

One of the most common measures of pollution is to count the number of PM10 particles - pieces of organic and non-organic matter measuring 1/100 of a millimetre or less - in a cubic metre of air.

Some areas of the capital  reported readings in the 90s on Thursday; far higher than the annual average of 29 and dwarfing the numbers recorded for other UK cities.

While this level of pollution is high however, the global context shows that London, even on a bad day, is outside the top echelon of polluted cities. Still, coming in higher than Bangkok and Rio is worth worrying about, for a day or two.

Channel 4 did a great piece on why pollution is so high in some of the cities to the left of this graph.

For the UK however, London (29) is consistently the most polluted city and no other metropolitan area gets within 5 PM10 particles a cubic metre of it. The city with the second highest annual count is Glasgow with 24. Birmingham lags behind on 16.

The map below shows the UK cities featured in the WHO report. By hovering over the cities on the map you can view information about them, including where they rank in the bar chart below.

Learn About Tableau

Despite the panic over the temporary spike, Britain’s overall pollution levels remain low, as we do not rely on coal as a main source of power are are not often at the mercy of airborne flotsam from the Sahara.

In fact, overall levels are actually falling and levels have improved since 2000.

This is good news as extreme smogs are not just an unpleasant inconvenience: ambient air pollution is responsible for 3,732,500 deaths a year, according to more data from the WHO.

The highest mortality rates are in the low-middle income countries in the WHO’s Western Pacific Region, where 120 people per 100,000 capita die from  attributable causes.

Interestingly, Europe’s more impoverished countries have the second highest rate of death from ambient pollution, at 75 per 100,000 capita.

Data was not available by country, for some reason.

[billyehrenberg]

Just how bad is the London smog?

Picture by "tatulund", photo reproduced under license.

With all the talk about the smog affecting London many people will be considering cancelling runs in the lead up to the London Marathon.

As I was eating my eggs and little soldiers this morning LBC was on the radio, quoting professionals claiming exercise is hazardous in such conditions. If you get a cough or a sore throat, abandon the run or walk slowly to the gym.

This blog would never advocate ignoring doctors’ instructions, but I can’t help wondering how bad London’s air pollution is compared with other cities around the world.

So I made this graph using data from the World Health Organisation:

  I chose some famous names like New York and Rio, and then put in the high scorers. 

As you can see, London is suffering today by it's usual standards and, after breathing down Bangkok's neck, for most of the morning had actually overtaken it by 3pm.

But what is the measure? It all sounds very technical PM10 particles per m3.

Well PM10 is the size of the particle, roughly one hundredth of a mm. And so we are looking at the number of these particles per cubic metre. It's a fairly common measure.

So back to running: hopefully the pollution levels will fall by the end of the week, but spare a thought for anyone training for a marathon in Ahwaz, Iran, or even the Mongolian capital: Ulaanbataar is no place for a healthy pair of lungs either.

[billyehrenberg]

How wrong have the Bank of England been on inflation?

Mark Carney: about this far off. Photo from World Economic Forum, used under license. 

As the BoE always seem to be somewhat off the money when it comes to inflation forecasts, I thought I'd put the data together in a chart to show just how far predictions and actual stats diverge:

The projections have been out by over one percent for the majority of this parliament, and until January above the central bank's target rate of 2%.

Similar disparities have popped up in the past regarding employment too, which just goes to show that studying the unpredictable UK economy is far from being an exact science.

[billyehrenberg]

From Islington Now - Men in Islington have fourth worst life expectancy in London

Islington is the fourth-worst borough in London for a long life - if you’re a man. According to a new analysis of life expectancy data from the 2011 census Islington’s average male life expectancy is 77.83, almost two years below the average of 79.63.

The data is given is divided up by NHS Clinical Commissioning group boundaries, which roughly trace London boroughs but group City and Hackney as one are.

Of the 32 jurisdictions only Barking and Dagenham, Newham and Tower Hamlets have lower average lifespans for men.

Women in Islington have a better life expectancy than men - 83.22 years - reflecting the universal trend of women living longer.

Islington is a lowly 11th on the women’s league table however and women in the borough are still living for fewer years than the 83.81 average.

When figures are combined for both genders Islington is 7th at 80.52 years. The average across the capital is 81.71.

The animation below shows maps for these three sets of statistics, just note that the colours represent a third of the range for each data set.

This is because the lowest life expectancy for a woman is higher than the lowest for a man - terrible for any colour-scheme co-ordination.

For the overall average I took a median of both male and female scores, so it will not be 100% accurate.

Red marks the areas with the lowest life expectancy and yellow the highest. 

There also appears to be a correlation between the boroughs with lower life expectancies and those with higher scores on the deprivation index, which would be expected.

Tower Hamlets scores lowest both on the deprivation stakes and life expectancy while Islington scores consistently low: 6th most deprived and 7th lowest life expectancy (4th lowest for men, 11th lowest for women).

Click on the different boroughs in the map to reveal their rank /32 for both deprivation and life expectancy - as above the boundaries are those of the NHS CCGs, to which the life expectancy data applies. 

The indicators used to quantify deprivation "combine a number of indicators, chosen to cover a range of economic, social and housing issues, into a single deprivation score for each small area in England". 

Such figures highlight the importance of a balanced economic recovery and further support the growing clamour for solutions to Britain's dearth of affordable housing.

In case you want more data, here are the interactive maps for women, for men and for both. Here is the ONS dataset I used for my analysis and here is the google spreadsheet I made. Enjoy!

[billyehrenberg]

Is this the year the Six Nations try drought ends?

With all the law changes in Rugby Union the Six Nations has seen it’s tries steadily drying up. Hopefully this year can be the first to really buck the trend.

Despite everyone labelling the all-conquering England team of 2003 boring, they ran in 18 tries that year, and no England team has scored more in a campaign since.

In fact, despite the rise of Welsh rugby the occasional flash of Gallic flair and Brian O’Driscoll, no championship-winning side has scored more than 18 since 2003.

Perhaps it’s time to remind ourselves once more what free-scoring 2003 conservatism looked like, so without further ado, here is 12mins of England vs Wales from the RBS Six Nations Channel.

  Not bad. But not quite as good as what Jason Robinson did when the teams played again in the world cup:

From Official RFU TV.

But I digress.

Actually though, tries had already begun to dry up by then. england had scored 23 tries in coming second to France in 2002 and 29 in winning the year before.

Nor has the average number of tries per game, 4.93 in 2003, been bested since. In 2013 across 15 games the average number of tries was 2.47.

That year the Rugby Championship (formerly tri-nations) saw the whitewash blitzed at a rate of 5.5 tries a game. Up until 2009 the Tri-Nations, famed for its fast pace and attack-minded play, traded try averages with the Six Nations as, the graph shows.

Their highs were higher and their lows lower, but there is a clear zig-zag look to the graph.

Now the good thing is 2014 is bucking all the trends. We are cruising at a rate of 3.55 tries a game which, should it continue, would see another 14 tries to come and a total of 53, a dizzy figure not beaten since 2009.

The tournament has already beaten last year’s drought. 

Ireland helped the figures yesterday, and as O’Driscoll was in show-off mode, I thought I'd sign off with a clip from the official RBS Six Nations Channel.

[billyehrenberg]

Of beer, Wayne Rooney's new contract, and data Journalism

If you’re anything like me you’ll have read with a pair of raised eyebrows the news that Wayne Rooney has been awarded a £300,000-a-week contract (arbitrary link to article) .

You’ll also have struggled to turn that whopping figure into something more tangible, and are perhaps in need of a context that’s easy to comprehend.

So here is a choropleth map showing how much beers cost across Europe and, crucially, how many beers Mr. Rooney could afford on his bumper new salary. If you are reading this on a mobile device, it is definitely worth powering up the PC. The map is really very nice. 

As you can see that is a lot of beer/s. Note that he can buy more beers in Eastern Europe than in Norway, and that some of the countries that have suffered most in the Euro crisis, such as Italy and Greece, don’t actually have very cheap beers, adding insult to injury. 

Here are the 10 most expensive countries to grab a beer in. As top tens go, there are few surprises here.

Who knew? Those expensive Scandinavian countries charge you a lot for beer too. Luckily there are some cheaper options at the other end of the beer spectrum:

This is, I’m told, a good trick in data journalism: get some numbers and then use something that’s easy to comprehend as a measure.

Human brains are wonderful pieces of kit, but really struggle when asked to get to grips with big numbers. Sometimes simple visualisations of big numbers or data put in everyday contexts make everything a little easier to understand.

However there is another lesson here for budding data journalists, such as myself.

The data I used was ostensibly sound (even if we ignore the taxation levied on Wayne, as I did in my number crunching).

After all, what could be a sounder source of beer-based stats than pintprice.com? Surely all they do is collate and disseminate the various prices of beers?

Well yes, but they also rely on crowdsourced data; on tourists reporting the prices of beers. We have no info on how many people contributed. I can also say that after a recent trip to Bruges, I would have drunk the hand off anyone able to provide a pint for £2.97. Still, it’s a pretty map. 

[billyehrenberg]

Interhacktives viewer, this is your mirror

As part of the critically acclaimed Interactive Journalism course at City University London we MA students have to man a website called interhacktives.

I’m hoping it will be a heady mix of interestingly irreverent content and tools and tips for the aspiring journalists. Skills to survive and thrive in the digital world, as one of our tutors once said about something else.

But what will be the profile of our readership, (should we build one)?

If this post makes it to the Interhactives blog, and you aren’t my classmate and you’re still reading, this is who I think you are.

I think you are a university student or recent graduate in your late teens to mid-twenties. You like to be amused online. And you like your content fast. And snappy. Nothing over 300 words unless unavoidable.

You like your online digestion to be humourous, and very referential. You might even share with me that smug satisfaction you get from understanding a fairly highbrow quip. 

Any news content will be better digested if given a pop-culture slant for context. Any story about the US special forces getting bio-mech-suits will be interestingish, but you’ll click harder if its “The U.S. military is building an Iron Man suit.”

You also want useful tips in plain language. You're at home on the internet and just need to refine your use of the tool.

You’ll be tech savvy, use social networks to mine the content you want and then share it with your friends. But, I predict, you won’t tweet this (although you might pass on the Iron Man suit article).

I might be wrong. Maybe you’re reading this, bemused and un-digitally-interested, with the empty, jaded eyes of a fifty-year-old envelope salesman.

[billyehrenberg]

Making money from the Maasai

An article in Bloomberg Business week interested me as an illustration of a meeting of worlds.

Kenyan/Tanzanian Maasai, unaware that their name was being used for marketing by global brands, are planning to take on companies such as Ralph Lauren and Calvin Klein to regain intellectual copyright. 

The revenue made beggars belief:

"Layton [the lawyer representing the Maasai] estimates six companies have each made more than $100 million in annual sales during the last decade using the Maasai name." says BBW. 

The legal ground is uncertain, and success far from assured; there have been battles fought along similar lines before, with varied success.

“'It’s a nice idea, but if it would work, the French deficit would be gone by asking for royalties on French fries,' says Seth Siegel, co-founder of the Beanstalk Group, a trademark licensing agency and consultant."

The Maasai were mostly unaware of the meaning of the vast sums made off the back of their culture. The cost of a Masai (sic) pen, $600, had to be explained to them in terms of cows. Three or four good ones. 

If they were to receive the $10million a year royalties quoted in the BBW article their material requests would be modest. 

“'We’re not going so far as to ask for electricity,' says Saitoti Oloishiro, 42, a chief from northern Tanzania and one of the Maasai behind the project. 'That would be a daydream. What we are saying is, we need maybe some water for our families and our animals, a dispensary. We need schools nearby.'” 

[billyehrenberg]

1 Lunatic 1 Icepick

It’s been an interesting few days for arguing the limits of online press freedom.

The European Court of Human Rights said in its ruling on Delfi AS v. Estonia that websites must keep tabs on their comments sections too. 

News sites, it appears, and more than just passive conduits and are responsible for both welcome comment and the abusive froth that sometimes accumulates beneath some posts. 

The argument has gone a step further however. Tech website The Verge reports that the owner/operator of website Best Gore, a site famed for its visceral content, has been charged after helping to disseminate a video of a real murder, horrifically entitled 1 Lunatic 1 Icepick. 

The site’s owner, Mark Marek, is to go on trial in Canada. He claims merely to have been passing on the information, and that only by people showing the crime online was the killer brought to justice.

This raises an interesting question: is there any difference between a site like Best Gore showing murder footage and conventional media outlets showing images of war or gruesome gang killings in Mexico? Papers in other countries have stronger stomachs for disturbing images than our own.

Do the limits of the public interest argument stop suddenly at the doors of traditional news outlets? 

If sites are liable for the nature of the content they or users post, this could be seen as damaging to freedom of expression. And what if a site doesn’t post a snuff clip but instead links to it, could it too be on dubious moral ground?