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Here is my first attempt at LEGO cartography – a 3D relief map of Scotland in LEGO form!

 

The map is based on Ordnance Survey open data, in this case their Terrain 50 DEM. To be honest, this DEM is total overkill for a model of this resolution, however I wanted to use it so that I can create more detailed maps in the future and don’t have to go about processing loads of new data.

 

So the workflow for this model is as follows:

 

The DEM was originally processed in QGIS, which for those who don’t know, is a powerful piece of open source GIS software. The output from this was a high-res PNG, with a discreet colour ramp using the RGB codes for official LEGO colours. The PNG was then input into a piece of software called Brickaizer, which was used to create a LEGO mosaic pattern of the image. The use of official LEGO colours in the PNG meant that creating an accurate pattern required a lot less tinkering during this stage of the process. The output for this is an Excel sheet which provides a parts list and pattern to be recreated in LEGO. The problem with this of course is that the pattern it creates is only one layer, and my map needed to be 3D. This meant an additional step was needed, so used the pattern to build a 3D model in Stud.io. I chose Stud.io because its integration with Bricklink meant ordering parts was a breeze. The final part was of course building it in real LEGO!

 

I also commissioned the creation of custom printed parts to set out things such as Longitude / Latitude, the legend, scale bar and copyright statement. This work was carried out by United Bricks, who are based in Castle Douglas. They did a great job!

 

Finally, the model was framed for hanging by We Frame It in Inverness.

  

The 10,000 biggest buildings in Paris, sorted by area. I used OSM data and selected all buildings within an 8km radius of the Louvre. Wrote a python script and used postgres/postgis to place the buildings.

 

The scan order is right-to-left, top-to-bottom, so the top-rightmost building is biggest.

 

The colouring is based on area - each band of colours represents one of 5 jenks breaks in the area data.

 

I can't claim to have invented this - I remember seeing a similar poster a few years back, but I can't find a link.

 

I quite like the way it looks a bit like ancient script, or the Rosetta stone.

Urban Fabric Model of Vienna - Baukörpermodell der Stadt Wien. Created with QGIS and Blender. Data from OGD Vienna.

Colombia interpreted in the world of Avatar (Nikelodeon). I used texture fill to the sea and a SVG image to do the frame and symbols on top. Special symbology as mountains and volcanoes where made using inkscape and put there using random points fill. At the end I added a multiply layer with a paper texture.

mapping Edinburgh in the style of Ordnance Survey maps from the early 20th century, using contemporary OpenStreetMap data.

 

Inspired by the wonderful georeferenced maps from the National Library of Scotland, in particular the early 1900s Ordnance Survey Maps.

 

Using QGIS 2.18. The hardest part is the street labelling and typography. Edinburgh is mapped in great detail in OSM; I had to reduce the detail and shrink the building outlines to get the feel of the originals.

 

Added a bit of grunge using blending mode, transparency, and a texture (a photo of a plaster wall I took during house renovation)

 

Based on Environment Agency 1mtr DTM/DSM Lidar layers.

The choice of that comedy colour ramp is deliberate, and intended to easily show the extent of the main pit.

 

Kinda doesn't need a feature key, but just to help a bit:

The contours are 1mtr.

Grey lines are roads, the brown dotted line is a greenlane / farmtrack.

 

100% QGIS!

Made with Qgis

Basemap and Subregions layer from www.naturalearthdata.com.

 

visualising blending modes in QGIS.

Realizado con Qgis.

Las curvas de nivel se han obtenido a partir del archivo MDE PNOA_MDT200_ETRS89_HU30_Toledo descargado del www.ign.es

 

El río Tajo está extraído del archivo Relación Geográfica completa de Ríos de Castilla-La Mancha de arcgis.com

Using QGIS Nødebo and PostGRES / PostGIS, and using 2011 Census data.

 

The Blue area contains the same population as Scotland (5.3 million).

 

The Pink area has the same population as Scotland, Wales and Northern Ireland combined (just over 10 million).

 

The population of Scotland would reach about half-way to the M25

 

In case you're wondering, the reason why the pink area is much larger than double that of blue, it's because as you go out, population density decreases. This means Scotland's area appears smaller in relation to the combined area than a simple 1:2 ratio would suggest.

 

The combined population of Scotland, Wales and Northern Ireland would extend just past the M25.

 

How it was done : Based on cumulative population of output areas, which were sorted by ascending distance from Westminster based on centroids.

 

QGIS features used : print composer, buffer/dissolve, draw effects/drop shadow, QuickMapServices plugin.

 

See also this map comparing some smaller european countries with Edinburgh

Inspired by www.reddit.com/r/MapPorn/comments/esq9ay/all_roads_in_sco..., here's all the roads on the island of Ireland

 

• OpenStreetMap data downloaded as PBF files from download.openstreetmap.fr/extracts/europe/

○ Ireland

○ Northern Ireland

• OGR2OGR used to convert and combine the two PBF files into one SQLite database

• The "Lines" table from the resulting SQLite database was imported to QGIS

• The data were filtered so that only lines where "highway" is NOT NULL were included

• Separate layers were created for the following "highway" types and styled with different thicknesses of lines (from thicker at Motorways to thinner at Tracks)

○ Motorways

○ Trunk and primary roads

○ Secondary roads

○ Tertiary roads

○ Tracks

• From the remaining layer the following "highway" types were excluded

○ abandoned

○ demolished

○ disused

○ footway

○ cycleway

○ gallop

○ proposed

○ planned

○ bus_stop

○ bridleway

○ path

○ razed

○ raceway

○ rest_area

Bing aerial imagery overlaid on ESRI's multi-directional hillshade. Edited in Photoshop.

While waiting around for my internet hotspot to recharge this morning I played around some more with #LiDAR DEMs in #QGIS. Love the meander belt of the Sawmill River cutting through floodplain deposits of the Connecticut River in #montaguema

 

(Joemaps.com is my nascent cartography business. Stay tuned!)

the OS recently discovered that Ben Nevis - the highest point in the UK - was a few cm higher than their previous reading, which means it's been rounded up to 1345m.

 

Thought it would be interesting to see how much of the world is higher than Ben Nevis.

 

QGIS 2.14 Essen. Elevation data from ETOPO1, scaled down to 1/4 size for speed. (This is the version which includes Ice, rather than the bedrock version)

 

Unfortunately the scaling seems to have underestimated the heights in the UK slightly, so results are approximate.

 

Used a normal graduated blend, but also used a black/white mask with Darken blend to give finer control of the visibility of the offshore areas. Used YlOrRd5 ColorBrewer palette.

 

Interesting factiod - there are 140 countries/territories with at least some land above 1345m (Zonal Statistics)

I've used Blender with QGIS many times before, but it's always been a one-way process before now. QGIS into Blender.

 

This time i was able to get the Blender render back into QGIS 3.6 for cartography (labels, grids) and page layout.

 

Used a geographic projection (ESPG:27700) with a 1:1 x/y cell ratio for simplicity.

 

Data is from Ordnance Survey Open Data (Open ZoomStack). This has the whole of the Great Britain in Geopackage format.

 

The trick is to export the heightmap from QGIS to png, really high resolution (I use 720dpi). Blender uses this to extrude the surface using a 10k x 7k raster, using Micro-displacements. I make sure the Blender mesh has the same aspect ratio as the QGIS output, and use an overhead ortho camera in blender. The camera is positioned exactly in the z-axis so that it matches the edges of the mesh.

 

I then use the generated pgw file - the world file - and copy it alongside the rendered Blender image, tweaking the pixel sizes according to the export scaling, and any adjustments to the blender output resolution. This lets me bring the rendered png file back into QGIS without having to manually georeference it.

A map I made completely with QGIS 2.10, depicting the Persian (Achaemenid) Empire and the ancient Mediterranean World about 500 BCE. For some background info about the map have a look at my homepage: www.tabulae-geographicae.de/english/dawn-of-the-classical...

A dot density map estimating number of Cod landed from around Scotland during 2018.

 

Data from www2.gov.scot/Topics/Statistics/Browse/Agriculture-Fisher.... Average weight of 4kg per Cod was used to convert tonnes to numbers. (4kg is the threshold value between size 2 and 3 grades, the most common grades landed in 2018).

 

Tutorial on creating dot density maps in QGIS v2x learngis.uk/creating-statistical-dot-density-map-qgis/

Handfasting ceremony for Dox and I .Thankyou to Taralyn Gravois for the beautiful blessings and to Tony Stark for documenting the day.

www.youtube.com/watch?v=HHN_qgIS-g4

Tinkering. Not much different. Warmer. Reflections sorted. Mountains in distance more accurate and some DOF.

The Silhouette And The Spider by Daniel Arrhakis (2014)

 

With a music suggested by Pifou (Gérard) ! Thank you my great friend ! : )

 

www.youtube.com/watch?v=QGi-Eq5MBbg

 

This shot was take in Queluz National Palace , near Lisbon, Portugal.

For a bigger image farm6.staticflickr.com/5595/14826605315_9ebefccf58_k.jpg

  

My little map company, JoeMaps.com, is now live! I'll be producing several more maps of rivers like this over the next few weeks and in 2019. Prints of this map, in several sizes, are for sale on the site.

… with all this lovely weather I should actually be outside taking photographs of landscapes!

 

I quite like the 'form' of this one and I may decide to persevere with it to try and get a bit more realism. Clearly the textures I'm using still lend themselves to a more painterly unrealistic (but nevertheless not wholly unpleasing??) image. Keeps me out of the sun anyway!! (I hate the sun!) Looks better from a distance!!

 

I’ve been working on a commission for that might be asking for some hill shading… After seeing the amazing work Scott Reinhard, Daniela Huffman, and several other folks are doing using #blender, I decided to give it a try– the results are phenomenal!

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#qgis #blender #holyokerange #cartography #westernmassachusetts @joemapcompany

I've been having a blast making custom maps for folks via my business at joemaps.com-- I had a blast delivering several commissions to clients this past week: Dear friends had been discussing a desire to decorate with art that echoed their deep love of the rivers that flow through and near their town, which led to their commissioning this custom triptych of the Green River's historic floodplain in Western Massachusetts. I'm thrilled to see one of my own favorite rivers finally hanging in their home.

#westernmass #greenfieldma #lidar #watershed #rivermaps #localart #homedecor #gis #pleistocene #floodplain #bedroom #triptych

From last Wednesday's ride.

I'm not super happy with the labels here, (not enough water for my liking), hence the kinda neither-one-thing-nor another boxes I've ended up using.

This visualisation was produced in QGIS 2.12.1, with a little help from postgres/postgis (could also be done in QGIS only).

 

Areas of each country's circle are in proportion to population.

 

How it was done

 

First of all, reprojected the Natural Earth data to epsg:3410 (a cylindrical equal area projection in meters). This is important; doing it in epsg:4326 makes countries appear bigger the further they are from the equator (areal distortion). Because degrees.

 

Next, used postgis to find the centroids and buffer them, so that the circle area is in direct proportion to population. Brought these in as CSV delimited files after exporting from pgAdminIII. The query sorted images in descending order of size, so smaller countries appear on top of larger countries.

 

Unfortunately, this projection keeps areas in scale, but distorts shapes. Some countries start to look like eggs. Oh noes!

 

Got round this by scaling the map down to a small size (a few square kilometers) and centred on null island, which removed the shape distortion, but kept the equal-area. Turned off OTF projection.

 

Flag images came from CIA World Factbook, and were used as raster Image fills. Used the FIPS code from the Natural Earth data to build the filepath to the CIA factbook images like this

 

'/tmp/factbook/flags/' || "fips" || '-lgflag.gif'

 

Because you can only scale the image width at present (I used bounds_width($geometry) in map units) I had to convert each flag into a square format using ImageMagick. Most flags are wider than they are tall, this avoids tiling the flag in each circle, at the expense of corner detail.

 

Apologies in advance to Norway - not sure what happened there. Some manual tweaking may be needed :D

hexbin map showing distribution of 4 common place name patterns. Shows the influence of 4 languages; Gaelic, Old Norse, Brythonic and Old English. You can see some more patterns on my blog.

 

The "-by" suffix shows the extent of Danelaw, the diagonal band from Cumbria to Lincolnshire.

 

Interesting to see the use of '-ton' seems to be minimal in the areas where Gaelic and Welsh are strongest.

 

Done in QGIS 2.12.2. Used mmqgis to create the hexagonal grid, and multi ring buffer plugin to get the outline effect.

 

Data from Ordnance Survey, crown copyright and database rights,

Just a bit of fun (for me!). Hopefully superficially recognisable as Catbells, if only for the topography (I realise CB isn't as rocky as this, but I quite like the textures). Some other bits of artistic license too...

 

I think this is a massive improvement on some of my early 3D landscapes... but still a long way from photorealism (TBH I don't know how to really achieve that either... I think the best bet is to go the other way and make them look like paintings( Its still art, afterall!

Crater Lake, rendered using Blender. Elevation data from SRTM. Prepared raster in QGIS.

 

Protip / memo-to-self: Blender will happily use geoTIFFs as heightmaps if you use "non-color data". This is far better than using PNGs (which at the moment seem to be limited to 256 values, so you get banding).

 

Using micro-displacements (adaptive rendering/subdivision surface) in Blender 2.79. DEM was 6000x6000; before adaptive rendering I could only get 2000x2000 with 8Gb RAM, if I was lucky (and patient).

 

Lit from a low angle, using Blackbody of 3500K to give a 'sunset' feel to the lighting.

 

The lake surface was added manually so the level might be a bit off ;-)

Because of the height and low resolution (and, for some reason I'm not sure about my land has a grungy element I can't seem to isolate) this has come out with a sort of painterly effect which I quite like. I like my autumnal tree blobs (they look horrendous close up!)

 

The real challenge now is to use proper textures so you get grasses and rocks 'n stuff... think I'll need something a little simpler than this for that)

Here is my first attempt at LEGO cartography – a 3D relief map of Scotland in LEGO form!

 

The map is based on Ordnance Survey open data, in this case their Terrain 50 DEM. To be honest, this DEM is total overkill for a model of this resolution, however I wanted to use it so that I can create more detailed maps in the future and don’t have to go about processing loads of new data.

 

So the workflow for this model is as follows:

 

The DEM was originally processed in QGIS, which for those who don’t know, is a powerful piece of open source GIS software. The output from this was a high-res PNG, with a discreet colour ramp using the RGB codes for official LEGO colours. The PNG was then input into a piece of software called Brickaizer, which was used to create a LEGO mosaic pattern of the image. The use of official LEGO colours in the PNG meant that creating an accurate pattern required a lot less tinkering during this stage of the process. The output for this is an Excel sheet which provides a parts list and pattern to be recreated in LEGO. The problem with this of course is that the pattern it creates is only one layer, and my map needed to be 3D. This meant an additional step was needed, so used the pattern to build a 3D model in Stud.io. I chose Stud.io because its integration with Bricklink meant ordering parts was a breeze. The final part was of course building it in real LEGO!

 

I also commissioned the creation of custom printed parts to set out things such as Longitude / Latitude, the legend, scale bar and copyright statement. This work was carried out by United Bricks, who are based in Castle Douglas. They did a great job!

 

Finally, the model was framed for hanging by We Frame It in Inverness.

  

Experimenting with cartography of the human form.

 

Based on a 3d model I found on Thingiverse. Used Blender to reverse-engineer a heightmap (DEM) from a 3D STL model of a scanned head, and QGIS/GDAL to do the cartography (using contours, hillshading and blend modes/transparency).

 

Traced the original 3D model back to SampleAndHold under a CC-BY-NC licence.

 

I used PNG-16 rather than TIFF for the workflow. PNGs keep the metadata in a separate file (.pngw), which can be hand-written if required.

 

If you use geoTIFFs some graphics packages will not accept the geo metadata or will strip it out :-/

 

If you use PNG-8, you get Tanaka contours for free, as the elevations are quantized to 256 levels :D

Here is my first attempt at LEGO cartography – a 3D relief map of Scotland in LEGO form!

 

The map is based on Ordnance Survey open data, in this case their Terrain 50 DEM. To be honest, this DEM is total overkill for a model of this resolution, however I wanted to use it so that I can create more detailed maps in the future and don’t have to go about processing loads of new data.

 

So the workflow for this model is as follows:

 

The DEM was originally processed in QGIS, which for those who don’t know, is a powerful piece of open source GIS software. The output from this was a high-res PNG, with a discreet colour ramp using the RGB codes for official LEGO colours. The PNG was then input into a piece of software called Brickaizer, which was used to create a LEGO mosaic pattern of the image. The use of official LEGO colours in the PNG meant that creating an accurate pattern required a lot less tinkering during this stage of the process. The output for this is an Excel sheet which provides a parts list and pattern to be recreated in LEGO. The problem with this of course is that the pattern it creates is only one layer, and my map needed to be 3D. This meant an additional step was needed, so used the pattern to build a 3D model in Stud.io. I chose Stud.io because its integration with Bricklink meant ordering parts was a breeze. The final part was of course building it in real LEGO!

 

I also commissioned the creation of custom printed parts to set out things such as Longitude / Latitude, the legend, scale bar and copyright statement. This work was carried out by United Bricks, who are based in Castle Douglas. They did a great job!

 

Finally, the model was framed for hanging by We Frame It in Inverness.

  

starting in Westminster; population areas equivalent to England (pink), Wales (red), Scotland (blue) and NI (grey)

Only two and half years after the last one... this takes the data I created a couple of years ago mapping all routes to Derby from a grid of points across the mainland (I have the data but can't remember how I created it!). The thicker the line = the more routes that connect onto that particular road.

This has now been overlaid onto a 3D height map of the country (taken from some new DEM satellite data from Germany). Then played with in Blender and PS to get some fog glow. The hills are there is you look closely enough.

This is quite low res, but has some potential for more playing and another raft of similar images ;)

blender and qgis 3.6

 

using data copyright OpenStreetMap and contributors

 

height is based on hierarchy (motorway = trunk > primary > secondary > tertiary > residential).

 

rendered roads in QGIS using various shades of grey to get a heightmap. Used "symbol levels" to avoid 'gaps' at intersections; the most important road wins at any given pixel.

 

some key roads are 'unclassified' (like Princes Street) so I had to miss them out

I've used Postgres/PostGIS and QGIS to create a strip map of the recently opened Borders Railway, this shows the section between Edinburgh Waverley station and Newcraighall.

 

This map straightens out the railway line - shown as the thick white line in the middle.

 

Points on surrounding roads have been moved so that their vertical position is based on their perpendicular distance from the railway line.

 

This means that 'up' can be North, North-East, East or even South-East, depending on the direction of the train ;-)

 

It's surprisingly difficult to tell whether a point is to the left or right of a long line which meanders :)

 

Blog post on how this was done.

 

Grid was created using this plugin (now available in the plugins repo)

 

Using data copyright OpenStreetMap contributors.

This is Derby City Centre (sans roads, parks, rivers, etc etc). The buildings are the result of some heavy manipulation in QGIS using OSM buildings and EA 1m LIDAR data.

 

This has given the buildings a very pixelated and strange look, but I'm not going for realism, just a pleasant image.

 

Isn't quite the Derby of today. I think the LIDAR data may be 2009-ish and the building outlines are up to date. Therefore, you get some anomalies (for example the Council House is the old Council House with the chamber in the middle, new buildings on Castleward won't be correct and other bits and bobs will be missing).

The processing has also obliterated some of the walls on some buildings, but I still quite like the effect.... bit like a more advanced minecraft.

 

Now for the layering.

Here is my first attempt at LEGO cartography – a 3D relief map of Scotland in LEGO form!

 

The map is based on Ordnance Survey open data, in this case their Terrain 50 DEM. To be honest, this DEM is total overkill for a model of this resolution, however I wanted to use it so that I can create more detailed maps in the future and don’t have to go about processing loads of new data.

 

So the workflow for this model is as follows:

 

The DEM was originally processed in QGIS, which for those who don’t know, is a powerful piece of open source GIS software. The output from this was a high-res PNG, with a discreet colour ramp using the RGB codes for official LEGO colours. The PNG was then input into a piece of software called Brickaizer, which was used to create a LEGO mosaic pattern of the image. The use of official LEGO colours in the PNG meant that creating an accurate pattern required a lot less tinkering during this stage of the process. The output for this is an Excel sheet which provides a parts list and pattern to be recreated in LEGO. The problem with this of course is that the pattern it creates is only one layer, and my map needed to be 3D. This meant an additional step was needed, so used the pattern to build a 3D model in Stud.io. I chose Stud.io because its integration with Bricklink meant ordering parts was a breeze. The final part was of course building it in real LEGO!

 

I also commissioned the creation of custom printed parts to set out things such as Longitude / Latitude, the legend, scale bar and copyright statement. This work was carried out by United Bricks, who are based in Castle Douglas. They did a great job!

 

Finally, the model was framed for hanging by We Frame It in Inverness.

  

Urban Fabric Model of Vienna - Baukörpermodell der Stadt Wien. Created with QGIS and Blender. Data from OGD Vienna.

using QGIS and Blender.

 

Used three lamps to give a warm light from the NW, a very warm light from the SW and a cool blue light from the SE.

 

DEM is OS Open Terrain 50, Crown Copyright and database right 2016.. Overlaid map from OpenStreetMap, copyright OpenStreetMap and contributors.

 

The slightly weird perspective is down to using an Orthographic camera.

QGIS elevation masking.

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