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#30DayMapChallenge 2020

It happened again.

Topi Tjukanov (@tjukanov) once again got the geo-community’s creative juices flowing throughout the whole month of November. The rules remained the same: create a map based on a specific topic for each day of the month.

You can find more details, and plans for 2021, on the 30DayMapChallenge here.

With fewer time to spare this year, I attempted to keep most of my entries to #30MinuteMaps – either by rekindling old dormant projects or doing rapid prototypes. Of course, with a sense of inevitably, there were one or two which sucked me in meaning I perhaps spent several hours over (Blender I am looking at you).

As with last year’s, I attempted all 30 map and you can see a selection of some of my entries on this page. Hi-res versions available upon request.

Day 1: Points

This was a rather quick and crude visualisation of several hundred thousand LIDAR points in my neighbourhood.

Tools used: QGIS; LIDAR View. Data: DEFRA.

Day 2: Lines

Based on a mini-project I previously started but never finished. I extracted polylines data from OpenStreetMap for rapid transit train lines (aka tube lines) for some of the largest networks in the world.

Tools used: QGIS; Aerialod. Data: OSM; Wikipedia.

Day 3: Polygons

I extracted polygons for football stadia and put them in a Mapbox ‘story-map’ style application. Web-mapping is not one of major strengths so this is largley based on a Ordnance Survey tutorial I once attended that showed Brewdog pub locations.

Tools used: QGIS; Mapbox. Data: OS Zoomstack; OSM.

Day 5: Blue.

Hillshade of Wales colorised in blue. Blue Wales. Terrible pun I know but I was lacking ideas and experimenting with hillshade techniques.

Tools used: QGIS; Aerialod. Data: DEFRA.

Day 4: Hexagons

This was a very quick set of maps, put together using semi-automated procedures in an FME workbench, to show relative number of football pitches vs rugby pitches across the British Isles.

Tools used: FME Desktop. Data: OSM.

Day 5: Red

This was my attempt to create a map of Canary Wharf in the style of a Doom level map. (by which I mean classic 90s Doom, not the gorefests of recent years as fine as they are too.)

Tools used: QGIS. Data: OS Zoomstack.

Day 7: Green

I wanted to do a hillshade of Greenland but was unable to find suitable data in the time-frame other than this tiny little snippet.

Tools used: QGIS; Aerialod. Data: SRTM.

Day 8: Yellow

A map of Naples simply based on the road and rail network. All lines are yellow with different transparency and blending levels.

Tools used: QGIS. Data: OSM.

Day 9: Monochrome

I have recently become quite fascinated with the levels of detail captured in some LIDAR data, especially of the urban environment. I thought that this grey, desolute image of a deserted theme park in Northern England (Blackpool Pleasure Beach) seemed rather apt for 2020.

Tools used: QGIS; Aerialod. Data: SRTM.

Day 10: Grids

Many cities in the UK and indeed most of Europe have, I presume, grown in a somewhat organic and chaotic manner. Less so in the USA, with many cities famed for their grid plans.

This was a quick and dirty extract of OpenStreetMap data visualised in QGIS, and then rendered in Aerialod to give it a 3D-ish look and accentuates the gridded network. I also like to think that it gives a rather pleasing aesthetic.

Tools used: QGIS; Aerialod. Data: OSM.

Day 11: 3D

A couple of years ago I visited the Bay of Kotor in Montenegro and was awestruck by the natural surroundings which comprised of Fjord-esque vistas.

Alas, my attempted 3D rendering is unable to do it the justice it deserves as I continue to get grips with Blender GIS.

Tools used: QGIS; Blender. Data: OSM, Google Maps.

Day 13: Raster

Smithfields Market, London.

This is what I nearly used for Day 9 (monochrome) but I didnt want it to go to waste and it still seemed apt for the raster theme of the day. Again, impressed by the level of detail in the LIDAR data and lighting options in Aerialod give this an almost photo-like look.

Tools used: QGIS; Aerialod. Data: SRTM.

Day 16: Islands

Using some high quality LIDAR data in Blender to achieve some nice renders. Also threw these back into QGIS and use as orthographic basemaps.

Tools used: QGIS; Aerialod. Data: DEFRA, OSM.

Day 17: Historical

Lake Champlain, Vermont.

Mastered and popularised by Scott Reinhard and Sean Conway, rendering vintage maps with modern DTM elevation data seems to be all the rage at the moment. I have attemped it before so gave it another go here and quietly please with this one.

Tools used: QGIS; Blender. Data: USGS, SRTM.

Day 18: Landuse

This was just a simply cartogram map composed in QGIS based on land use statistics for England – basically anywhere outside the main cities are comprised of a lot of agricultural land.

Tools used: QGIS. Data: Ministry of Housing, Communities & Local Government

Day 19: NULL

Maps of Null Island were, unsurprisingly, a rather popular choice for this day. I just put this together very quickly in QGIS – added in a some land areas, some graticules and a big point at zero degrees latitude and zero degrees longitude.

Tools used: QGIS. Data: Natural Earth.

Day 20: Population

This one was a bit of a cheat as this was somehting I originally did in last year’s map challenge which I had already updated in April 2020 – so this was essentially a re-post of a map I had already done.

Still I quite like this animated maps to show changes over time – follow this tutorial by Alasdair Rae to learn how to put one together.

Tools used: QGIS. Data: GLA, Office for National Statistics.

Day 21: Water

Another day another map and yet another chance to put a tutorial by Alasdair Rae into practice!

Tools used: QGIS; Aerialod. Data: Office for National Statistics.

Day 22: Movement

The GLA Datastore has some great datasets included animal rescue incidents from the London Fire Brigade. So I though I would use this data to try and learn Mapbox JS. The interactive map is available here.

Tools used: Mapbox. Data: London Datastore

Day 23: Boundaries

I did not have much time nor inspiration on this day. For some reason Boundary Park – the home of Oldham Athletic Association Football Club – was the first thing that popped in my head. So that is what I made a very quick map of.

Tools used: QGIS. Data: Ordnance Survey, ESRI World Imagery

Day 24: Elevation

I had a bit of fun with this one as I mapped Corsica ‘three ways’: an exaggerated relief hillshade map made in Blender; a joyplot made in QGIS; and a sort of elevation profile view made in Aerialod.

Tools used: QGIS; Aerialod; Blender. Data: SRTM.

Day 25: COVID-19

In many ways I was happy to participate in the the 30DayMapChallenge to get away from COVID, but I guess it was inevitable and right that there should be a COVID map at some point in this year’s challenge.

I had been mapping COVID outbreaks and statistics on a daily basis as part of my day job working in pubic health (you can read about some of that work in this Ordnance Survey Blog), but this was an opportunity to refine this series of ‘sparkline cartograms’ – these were my takes on similar approaches from Datagistips and John Nelson.

These were all done in QGIS and the geometry generator was used to draw each sparkline chart.

I was also pretty chuffed to see this one get retweeted by the British Cartographic Society and even receive praise from Kenneth Field.

Tools used: QGIS. Data: Office for National Statistics,

Day 26: Map with a new tool

A quick map and visualisation made using Kepler which I had been meaning to try out for some time. It seems great for visualising large datasets and I am sure this is a lot more it can do.

Tools used: Kepler. Data: wikipedia

Day 27: Big or small data

Another map, another hillshade and another rendered at the end in Blender. This time of ‘Big’ Sur. This time I applied all the hillshade colours within Blender itself before popping the render back into QGIS where I used it as the basemap.

Tools used: QGIS, Blender. Data: SRTM, Natural Earth, UC Berkeley Geodata Repository

Day 28: Non-georaphic map

Again pushed for time and I guess this could be considered a bit of a cheat….but these are series of maps that were ‘created’ by my ‘team’ in Football Manager.

Tools and data used: Football Manager.

Day 29: Globe

I had never actually done a globe map before….so this was a first for me. This was put together using Globe Builder and Temporal Controller in QGIS with some data from Global Terrorism Database. After posting I think I noticed the animation may be out of synch slightly…

Tools: QGIS. Data: Global Terrorism Database

Day 30: A map

To finish off the challenge, the last theme was simply ‘a map’. I decided to pay tribute to where it all started for me, and recreated the very first map I completed using GIS at Liverpool University. This is an intentional ‘bad’ map as I tried to replicated those little mistakes we probably all did as a beginner (keeping the default colours, fonts and other options, badly fromatted legend, huge North arrows and scale bars etc!)

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