I came across this post on Twitter just in the nick of time. It describes a challenge for November to create a map each day following some set themes. It was hard to resist. So this page will show my progress as I take on each day.
Day 1 - Points
I decided to begin by using an open data set of points that we use every day within our business. OS Opennames. Showing the entire set of points on the map would have been boring and also a little bit dense. So why not narrow them down? I wanted a subset that would show something interesting and was suddenly inspired by the TV programme Countdown. Why not find all places whose names consisted entirely of vowels or consonants? A reasonably simple bit of SQL:-
SELECT * FROM data_os_opendata.opennames WHERE local_type IN ('City', 'Hamlet','Town', 'Village', 'Other Settlement', 'Suburban Area') AND name1 ~* '^[^aeiou ]+$' OR name1 ~* '^[aeiou ]+$'
This gave me a nice little point set which I plotted on a base map of the UK
As you can see there is a definite bias towards Wales. But there's also a problem as highlighted by some of my Welsh brethren. "W" and "Y" are vowels in Welsh! I was going to re-do the map but decided to stick by the rules of Countdown.
Day 2 - Lines
For the second challenge I decided to stick to my own locality. I live in Brixham by the sea. What could I show on the map in my area using lines? I was going to add my cycling and walking tracks but thought those might be useful for later challenges. Then I thought "why not keep it simple?". The challenge is "lines" so just show the linestrings.
I fired up QGIS and loaded the OS Zoomstack vector data. I zoomed into my area and simply rendered each vector layer of type linestring. Yes! contours are linestrings, not polygons before you start complaining. This is the result. My skeleton map of the Brixham, Dartmouth, Kingswear area.
Day 3 - Polygons
I decided that for day three I needed to brighten up my maps a little and create something with a bit more aesthetic. I've got lots of polygon data sets and one of the least used is green spaces. This is terrible as green spaces are ace and we (Nautoguide) run a sophisticated feedback system on the data set for Ordnance Survey.
So I focused in on London to see how many of them there are. Surprisingly, a lot of them. This in turn made me wonder what the ratio of green space to building looked like. I created this render in QGIS and found the result strangely pleasing.
Day 4 - Hexagons
Hmm hexagons what to do? What do hexagons represent? The first thing that came to mind was "bees". So I had a scout around for some data concerning bees and found a nationwide set analysing the propensity of pollen bearing plants that bees could use to create nectar.
I decided to zoom in on Cornwall and find the best place for bees to get the widest feeding choice. And bing this is what we get:-
Day 5 - Rasters
Rasters. Every time I hear that word I can't help thinking of dreadlocks and cricket. So inspired by Rastas I went looking for some data surrounding Jamaica. I quickly found a Natural Earth data set at a resolution large enough for me to download on a poor internet connection. I loaded the raster into QGIS, applied a quick yellow-to-red graduation to it and bingo, a bright and vibrant Jamaica.
Day 6 - Blue
I resisted the temptation to play with water and looked at the boy band "Blue" instead. Here is every concert they ever played, geocoded and linked in order on a nice blue globe. If I'd had more time I'd have animated it.
Day 7 - Red
I found all of the "Red" places in Great Britain and linked them up with the Travelling Salesman algorithm. Before you complain..only places with a distinct "Red" allowed.
Day 8 - Green
Here we have every country in the world with the colour green appearing on its flag. Note the density in Africa. Let's see if I have missed any!
Day 9 - Yellow
For yellow I picked the Chinese "Yellow" mountains, or Huangshan as they call them natively. I've exaggerated their height a bit and added a nice yellow tint. Hope this helps with the Saturday hangover ;-)
Day 10 - Black and White
Black and white made me think of old films. I scraped every British black and white film title from Wikipedia and turned them into a geowordcloud. [please note, NO inset box for the Orkneys ;-)]
Day 11 - Elevation
I have elevated the largest mountain in Wales, Snowdon or Yr Wyddfa to give it its proper name. Visualised in elevation numbers wrapped to contours with height dictating font size and opacity.
Day 12 - Movement
Movement. I am returning to my home town for this one. I wondered what would be the shortest route to ride my bike on every road in Brixham. One Chinese postman later and we have this animation.
Day 13 - Tracks
What's the longest track you've ever ridden? Mine was 2731 miles racing the Tour Divide from Canada to Mexico earlier this year. Here's the route overview and elevation profile.
Day 14 - Boundaries
Day 14 - Boundaries.
Boris wins the election, his mate Donald tells him that he needs to build WALLS! So Boris orders every county to get on with it. Who has to build the longest wall??
Day 15 - Names.
I took the @OrdnanceSurvey Opennames dataset and found all towns and cities. I then "twinned" them with their British namesakes. Finally I made a network map connecting those places that share the same name.
Day 16 - Places
Damn I misheard that as PLAICE Sand made a map of the density of plaice catches from ICES open data ...(OK I'll get my coat)
Day 17 - Zones
Imagine you went back to the 70's, ingested a large amount of LSD and then tried to figure out where the underground zones of London layer geographically. Well, no need, as I have done it for you.
Day 18 - Globe
If I ruled the world? Maybe I'd try and reshape it in my image? So for today's challenge I took an image of myself, polygonised it and slapped into onto a globe. Hmmm not very recognisable
Day 19 - Urban
I've tried to illustrate the urban decay of England and Wales by punching out the areas classified as urban and colouring the remainder with darker green being more rural
Day 20 - Urban (again .. whoops)
I imagined the urban areas as the organs of an industrialised country connected by arterial trunk roads as veins. This is the closest I was able to get with my limited styling skills. Gunther von Hagens eat your heart out
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