Tag Archives: maps

Dataviz of the week, 17/5/2017

nextstrain.org is a website that offers real-time tracking of pathogens as they evolve (flu, ebola, dengue, all your favourites are here). Data gets pulled in from various monitoring systems worldwide and represented with interactive content in several pretty ways:

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They have their own libraries called fauna, augur and auspice, the last of these doing the dataviz stuff, and as far as I could tell built on D3. I don’t pretend to understand the genetic and genomic work that has to go on to process the raw data but that is clearly substantial.

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Dataviz of the week, 3/5/17

I’ve occasionally asked myself odd superimpose-geographies questions like “how far is it from A to B if they were in Winchester?” (because I can feel those distances better) or “would the West Kennet Long Barrow fit inside the Broadgate Centre?” (I’m sure we’ve all thought that). Hans Hack has made an online map like that, with a serious purpose, which superimposes Aleppo and the destroyed parts onto London.

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It’s all done in leaflet.js and weighs in at 800 lines of code with a lot of generous — luxurious one might say — spacing, so it is well with your grasp to do something like this. It’s also just pretty, with sparing colour and layering of information with simple controls. There is also a Berlin version. I suppose you have to know the host city for it to hit home but then it’s a powerful message about the scale of it all.

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Dataviz of the week, 26/4/17

This chart of population density across Europe by Henrik Lindberg has been very popular online this last week.

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Long-standing readers will recall my stab at this but nowadays everybody just does it in ggplot2. It’s good to have options. While you’re at his Gist page, checkout his other stuff too.

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Dataviz of the week: 1/3/17

Scribbly states” is not done with felt-tip pens but with some sweet use of D3 Javascript by Noah Veltman. I admire his attention to the little details, making it more human-like and commenting on the situations where it doesn’t work. Turns out if you follow the links, that the method came out of Apple, who patented it way back. Didn’t someone like @inconvergent have a script to make coffee rings? You could chuck that on top for extra authenticity.

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Dataviz of the week, 25/1/17

We had guideline-bustin’, kiddie-stiflin’, grandparent-over-the-threshold-usherin’ pollution in London at the beginning of the week. This is fairly standard nowadays, sadly. It’s not quite so bad out where I live in the Cronx, but in town it’s the worst in Europe. At the same time, Cameron Beccario pointed out the Beijing effect in his wonderful globe of carbon monoxide levels – far worse than anywhere else in the world, though there are some petrochemical hot spots. I’ve praised this live viz before, but that was before I started having a pick of the week on my office door (then, when the door went, here on the blog), so I’ll mention it again. Nice.

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Noise pollution map of London (part 1)

I’m working on a noise pollution map of central London. Noise is an interesting public health topic, overlooked and of debatable cause and effect but understandable to everyone. To realise it as interactive online content, I get to play around with Mapbox as well as D3 over Leaflet [1] and some novel forms of visualisation, audio delivery and interaction.

The basic idea is that, whenever the need arises to get from A to B, and I could do it by walking, I record the ambient sound and also capture a detailed GPS trail. Then, I process those two sets of data back at bayescamp and run some sweet tricks to make them into the map. I have about 15 hours of walking so far, and am prototyping the code to process the data. The map doesn’t exist yet, but in a future post on this subject, I’ll include a sketch of what it might look like. The map below shows some of my walks (not all). As I collect and process the files, I will update the image here, so it should be close to live.

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I’d like it to become crowd-sourced, in the sense that someone else could follow my procedure for data capture, copy the website and add their own data before sharing it back. GitHub feels like the ideal tool for this. Then, the ultimate output is a tool for people to assemble their own noise-pollution data.

As I make gradual progress in my spare time, I’ll blog about it here with the ‘noise pollution’ tag. To start with, I’ll take a look at:

The equipment

Clearly, some kind of portable audio recorder is needed. For several years, when I made the occasional bit of sound art, I used a minidisc recorder [2] but now have a Roland R-05 digital recorder. This has an excellent battery life and enough storage for at least a couple of long walks. At present, you can get one from Amazon for GBP 159. When plugged into USB, it looks and behaves just like a memory stick. I have been saving CD-quality audio in .wav format, mindful that you can always degrade it later, but you can’t come back. That is pretty much the lowest quality the R-05 will capture anyway (barring .mp3 format, and I decided against that in that I don’t want it to dedicate computing power to compressing the sound data), so it occupies as little space on the device as possible. It will tuck away in a jacket pocket easily so there’s no need to be encumbered by kit like you’re Chris Watson.

Pretty much any decent microphone, plus serious wind shielding, would do, but my personal preference is for binaurals, which are worn in the ear like earphones and capture a very realistic stereo image. Mine are Roland CS-10EM which you can get for GBP 76. The wind shielding options are more limited for binaurals than a hand-held mic, because they are so small. I am still using the foam covers that come with the mics (pic below), and wind remains something of a consideration in the procedure of capturing data, which I’ll come back to another time.

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On the GPS side, there are loads of options and they can be quite cheap without sacrificing quality. I wanted something small that allowed me to access the data in a generic format, and chose the Canmore GT-730FL. This looks like a USB stick, recharges when plugged in, can happily log (every second!) for about 8 hours on a single charge, and allows you to plug it in and download your trail in CSV or KML format. The precision of the trail was far superior to my mobile phone at the time when I got it, though the difference is less marked now even with a Samsung J5 (J stands for Junior (not really)). There is a single button on the side, which adds a flag to the current location datum when you press it. That flag shows up in KML format in its own field, but is absent from CSV. They cost GBP 37 at present. There are two major drawbacks: the documentation is awful (Remember when you used to get appliances from Japan in the 80s and none of the instructions made sense? Get ready for some nostalgia.) and the data transfer is by virtual serial port, which is straightforward on Windows with the manufacturer’s Canway software but a whole weekend’s worth of StackOverflow and swearing on Linux/OS X. Furthermore, I have not been able to get the software working on anything but an ancient Windows Vista PC (can you imagine the horror). Still, it is worth it to get that trail. There is a nice blog by Peter Dean (click here), which details what to do with the Canmore and its software, and compares it empirically to other products. The Canway software is quite neat in that it shows you a zoomable map of each trail, and is only a couple of clicks away from exporting to CSV or KML.

Having obtained the .kml file for the trail plus starting point, the .csv file for the trail in simpler format, and the .wav file for the sound, the next step is synchronising them, trimming to the relevant parts and then summarising the sound levels. For this, I do a little data-focussed programming, which is the topic for next time.

Footnotes

1 – these are JavaScript libraries that are really useful for flexible representations of data and maps. If you aren’t interested in that part of the process, just ignore them. There will be plenty of other procedural and analytic considerations to come that might tickle you more.

2 – unfairly maligned; I heard someone on the radio say recently that, back around 2000, if you dropped a minidisc on the floor, it was debatable whether it was worth the effort to pick it up

 

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Dataviz of the week, 22 November 2016

This week I was impressed with this blog post by John Nelson, cartographic craftsman, that sets out the design principles and how to make “firefly maps”. They look like this:

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(c) John Nelson / adventuresinmapping.com

Wow, said the owl. I really want to make one, and I suspect you do too.

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