2013-04-20
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brief guide to canals and waterways by maraid on Flickr.
Great subtitle: “brief, pithy, and what you really want to know”.
2013-04-09
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Five years ago today, Flickr announced its video feature. Some users weren’t happy.
(I’m actually kind of happy I made these posts now, as they’ve all been taken down. One of the users has now left Flickr entirely. I’ve been through Flickr so I know that users own their images, but on the other hand I like that these otherwise ephemeral protests escaped their creators clutches.)
2013-03-14
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Space Suit Lining: Replacement Kit by chriswoebken on Flickr, from the 99¢ Futures project:
It’s been at least three years since I last went in to space. I checked the old space suit and transgenic moths had eaten all of the lining in the helmet.
2013-03-05
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Cover of Pig Housing by David Sainsbury*, posted by Dr R Charles on Flickr.
* any relation?
2013-03-04
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Dear Diary (project by Henrik Pettersson, Tom Leitch and David Vella, photo by Alice Bartlett, via iamdanw).
2013-02-22
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Gemini ‘off the pad abort’ by Dr R Charles on Flickr.
A page from ‘Manned Spacecraft’ by Kenneth Gatland.
2013-01-21
London Snow via the medium of Flickr
I wondered this morning how common snow’s been recently in London. After all, this year’s looking like having a week or so of snow, and I remember my last winter there, 2010, being fairly white too.
I was also reminded of this by Boris Johnson’s recent (generally awful) Telegraph column, where he says
By my calculations, this is now the fifth year in a row that we have had an unusual amount of snow; and by unusual I mean snow of a kind that I don’t remember from my childhood: snow that comes one day, and then sticks around for a couple of days, followed by more.
OK then. I suppose I could double check by looking at the Met Office’s UK climate summaries, but that would require some reading comprehension, and it’s a Sunday. Instead, I thought I’d do a tiny bit of data mining. (Actually this hardly qualifies, but what the hell, big data’s sexy, right?)
Flickr have an API, and one of the core methods is flickr.photos.search, and one of the parameters is the date taken. So it’s pretty trivial to write a small Python script that will do the search, return the total count for a search for, say, ‘snow london -ontario’, compare it with a baseline of ‘london -ontario’, and get this:
2001 34 20505 0.165813 2002 206 46747 0.440670 2003 419 90416 0.463414 2004 763 187478 0.406981 2005 1879 515875 0.364236 2006 2551 1130056 0.225741 2007 15227 1838767 0.828109 2008 12192 2027861 0.601225 2009 64871 2326955 2.787806 2010 34149 2305502 1.481196 2011 7429 2322795 0.319830 2012 14241 2449517 0.581380 2013 4872 63543 7.667249
Only three years reach over 1% of ‘snow’ photos, by this (admittedly handwaving) method: 2013, 2010, and 2009 (which was actually snowier, by this measure). By contrast, 2011 and 2012 look far less snowy.
(Of course, 2013 is pretty biased, because we haven’t had the non-snowy months that a full year has.)
Now I’ve produced this, I should actually go and do the hard work of comparing it to the aforementioned summaries to see if it’s actually worthwhile or not.
Edit: hugovk suggested looking for winters rather than years, so I changed the start/end of the timekeeping period to be in September of the year shown. Now the results look like:
2001 119 35647 0.333829 2002 443 73337 0.604061 2003 594 153578 0.386774 2004 1587 377460 0.420442 2005 2027 898777 0.225529 2006 15329 1671273 0.917205 2007 10903 1989473 0.548035 2008 60843 2250467 2.703572 2009 20579 2295751 0.896395 2010 25089 2316599 1.083010 2011 14916 2502527 0.596038 2012 6921 764047 0.905834
This looks better for the year starting in September 2012, and also makes 2006/2007 and 2009/2010 come up towards that 1% limit. Better.
