I was once at an OpenStreetMap conference where 6 out of the 8 talks in one day had an image of the John Snow Cholera Map. And no surprise, it is an excellent, relatable, and interesting early example of GIS. The spatial relationship is unmistakable.
Original map overlaid on modern day London:
The site of the Broad Pump is now the location of a pub called the “John Snow”, which is well worth a visit if you are in London.
John Snow location:
A follow up to my previous post: Every Person in Scotland on the Map. Winner of the 2016 OS OpenData Award for Excellence in the use of OpenData from the British Cartographic Society.
The mapping process is pretty straightforward, and not accurate. I don’t know where you live. But I can make an educated guess.
I simply amalgamate the two sets of census data from the NRS (National Records of Scotland) for Scotland (2011 census) and the ONS (Office of National Statistics) for England and Wales (2010 census).
Postcodes were then created based on the ONS Postcode Directory, filtering for postcodes that were live in 2011 (which is the latest census data). The postcode centroids were turned into polygons using voronoi polygons.
Then we simply select all of the buildings in a postcode from Ordnance Survey, Open Map product, filtering out most schools and hospitals. Then we put a random point in a random building for each person in that postcode.
I would have loved to include Northern Ireland, but the Ordnance Survey of Northern Ireland do not have an equivalent open building outline dataset, like Open Map from the Ordnance Survey.
Rendered with: QGIS tile writer python script. Processing done 100% in PostGIS.
The Struve Geodetic Arc is a chain of triangulation stretching more or less down the 26° E line of longitude from near Hammerfest on the Arctic Ocean over 2,820 km south to Izmail on the Black Sea. The survey was carried out between 1816 and 1855 under the guidance of F.G.W. Struve.
Theoretically, a degree of latitude is a constant and would have the same value at the equator as at the pole. But already Isaac Newton believed that the Earth was slightly flattened at the poles. This question of the shape and size of the Earth inspired the astronomer Friedrich George Wilhern Struve to come up with his famous Meridian Arc measurement.
The scheme included 258 main triangles with 265 not and over 60 subsidiary station points.The selection of points involves a total of 34 sites on the Struve Geodetic Arc. In today’s geography. the Arc passes through ten countries, viz. Norway (4 station points), Sweden (4), Finland (6), the Russian Federation (2), Estonia (3). Latvia (2). Lithuania (3). Belarus (5), the Republic of Moldova (1), and Ukraine (4).
All of the points in the Arc were designated as UNESCO World Heritage sites in 2005.
The site at Puolakka is easily accessible from central Finland, for example from Tampere, or especially Jyväskylä.
There is parking at the start of the walk, which is not maintained during the winter. But there is ample space on the roadside for parking. The path itself was in good condition but the road to the start could be difficult after a heavy snowfall.
The walk itself is 1km, all uphill. The path is very well maintained with stairs for the steeper sections. The view is definitely worth the time to visit.
Beginning of the walk.
740 meters to the start and 260 to the lookout tower
Stairs on the path
Lookout tower
Triangulation pillar at the top
View from the tower
View from the tower
Info board at the start of the walk
Path map
Walk path:
In 2018 we started a running club at work.
I created a quick script to parse the data on Strava to a ShapeFile, which can be easily animated with QGIS.
The script only works with Garmin files, GPX, TCX, and FIT.
Source: https://github.com/HeikkiVesanto/Strava-Garmin-Parser
Example:
2018 was a very productive year for OpenStreetMap in Ireland.
Around 70k nodes added, 60k ways added, and 160k polygons added.
Finally a company was formed to facilitate becoming a fully fledged local chapter.
What does that look like day to day:
I ran into a process where I wanted to create copies of rasters in PostgreSQL. While seemingly a simple process this took me a bit of work to figure out.
For my workflow I had three rasters, which all have the same size, and I want to load them into the same PostGIS table with three raster geometry columns. I don’t think this will work for different sized rasters since the rid’s will not match.
Three rasters:
raster1
raster2
raster3
Which I want to copy into:
merged_raster
First to create the merged raster table:
CREATE TABLE merged_raster ( rid serial NOT NULL, raster1 raster, raster2 raster, raster3 raster );
Then to add the rid’s. These are the id’s of the tiles that the raster was split into when loading. If your tile size is large enough then you may only have one.
INSERT INTO merged_raster(rid) (SELECT rid FROM raster1);
Then copying the actual data is straighforward (this assumes the raster column in the raster1 datasets is called rast):
UPDATE merged_raster m SET raster1 = r.rast FROM raster1 r WHERE r.rid = m.rid; UPDATE merged_raster m SET raster2 = r.rast FROM raster2 r WHERE r.rid = m.rid; UPDATE merged_raster m SET raster3 = r.rast FROM raster3 r WHERE r.rid = m.rid;
Now I still have an issue that QGIS will not load these layers. It will always load the initial raster column no matter what is chosen.
I was coming across some errors when installing Anaconda, Miniconda specifically.
https://conda.io/miniconda.html
I think part of the reason is I have quite a few installs of Python due to OSGeo4W.
My error:
Fatal Python error: Py_Initialize: unable to load the file system codec ModuleNotFoundError: No module named 'encodings' Current thread 0x00002554 (most recent call first):
The solution:
Update the activate.bat file that is called when launching from the start menu. For me located in:
C:\ProgramData\Anaconda\Scripts
We need to add in the following to clear out and reset the python environment before launching anaconda:
@SET PYTHONPATH= @SET PYTHONHOME=C:\ProgramData\Anaconda @PATH C:\ProgramData\Anaconda;C:\ProgramData\Anaconda\Scripts;%PATH%
So, editing the file from:
@REM Test first character and last character of %1 to see if first character is a "
@REM but the last character isn't.
@REM This was a bug as described in https://github.com/ContinuumIO/menuinst/issues/60
@REM When Anaconda Prompt has the form
@REM %windir%\system32\cmd.exe "/K" "C:\Users\builder\Miniconda3\Scripts\activate.bat" "C:\Users\builder\Miniconda3"
@REM Rather than the correct
@REM %windir%\system32\cmd.exe /K ""C:\Users\builder\Miniconda3\Scripts\activate.bat" "C:\Users\builder\Miniconda3""
@REM this solution taken from https://stackoverflow.com/a/31359867
@set "_args1=%1"
@set _args1_first=%_args1:~0,1%
@set _args1_last=%_args1:~-1%
@set _args1_first=%_args1_first:"=+%
@set _args1_last=%_args1_last:"=+%
@set _args1=
@if "%_args1_first%"=="+" if NOT "%_args1_last%"=="+" (
@CALL "%~dp0..\Library\bin\conda.bat" activate
@GOTO :End
)
@CALL "%~dp0..\Library\bin\conda.bat" activate %*
:End
@set _args1_first=
@set _args1_last=
To:
@REM Test first character and last character of %1 to see if first character is a "
@REM but the last character isn't.
@REM This was a bug as described in https://github.com/ContinuumIO/menuinst/issues/60
@REM When Anaconda Prompt has the form
@REM %windir%\system32\cmd.exe "/K" "C:\Users\builder\Miniconda3\Scripts\activate.bat" "C:\Users\builder\Miniconda3"
@REM Rather than the correct
@REM %windir%\system32\cmd.exe /K ""C:\Users\builder\Miniconda3\Scripts\activate.bat" "C:\Users\builder\Miniconda3""
@REM this solution taken from https://stackoverflow.com/a/31359867
@set "_args1=%1"
@set _args1_first=%_args1:~0,1%
@set _args1_last=%_args1:~-1%
@set _args1_first=%_args1_first:"=+%
@set _args1_last=%_args1_last:"=+%
@set _args1=
@SET PYTHONPATH=
@SET PYTHONHOME=C:\ProgramData\Anaconda
@PATH C:\ProgramData\Anaconda;C:\ProgramData\Anaconda\Scripts;%PATH%
@if "%_args1_first%"=="+" if NOT "%_args1_last%"=="+" (
@CALL "%~dp0..\Library\bin\conda.bat" activate
@GOTO :End
)
@CALL "%~dp0..\Library\bin\conda.bat" activate %*
:End
@set _args1_first=
@set _args1_last=
Updating the paths as required.
This just clears out the python and windows environmental variables before launching, similar to what OSGeo4W does.
Based on the work by:
Geoff Boeing: Comparing City Street Orientations
Rixx: Street Orientations
The graphs show the percentage of streets that run in a certain orientation. So for a grid based city like Chicago, there will be a heavy bias in north/south and east/west streets. Bearing in mind north and south will be the same (unless there are one-way streets, which only count in the direction they run in).
But for older cities that formed naturally, without modern city planning, the streets should be more varied.
Ireland:
Largest populated places by population. Based on the Ordnance Survey Ireland urban areas. As it is OSI data, Northern Ireland is not included.
Dublin Postcodes:
Some areas are clearly impacted by large motorways running through them.
And for non-Dubliners, a map of the postal district boundaries:
I updated the script by Rixx, so that it would take a ShapeFile as an input with a few caveats (it must be WGS84, it must have an attribute that has the are name and it must be called settl_name).
Check out the script at: GitHub
Stanley Cup Champions Since 1915:
Average Location of Stanley Cup Champions Since 1915:
The average locations were created using a window function in PostgreSQL. We can utilise the geography type to take into account the curvature of the earth and make the calculation on a spheroid.
So for the average location of the last five years:
select id, ST_Centroid(st_collect(geom) over (ORDER BY id ROWS BETWEEN 4 PRECEDING AND CURRENT ROW)::geography, True)::geometry as five_year_cent from nhl.winner_1915
Save as a text file ending in .xml like qgis_scales.xml
These are the scales OpenStreetMap tiles are rendered in for 96 dpi, so the map will look sharp on most monitors. These are the scales for the zoom levels.
The xml file can then be loaded into the project from:
Project> Project Properties…> General> Project scales
<qgsScales version="1.0">
<scale value="1:554678932"/>
<scale value="1:277339466"/>
<scale value="1:138669733"/>
<scale value="1:69334866"/>
<scale value="1:34667433"/>
<scale value="1:17333716"/>
<scale value="1:8666858"/>
<scale value="1:4333429"/>
<scale value="1:2166714"/>
<scale value="1:1083357"/>
<scale value="1:541678"/>
<scale value="1:270839"/>
<scale value="1:135419"/>
<scale value="1:67709"/>
<scale value="1:33854"/>
<scale value="1:16927"/>
<scale value="1:8463"/>
<scale value="1:4231"/>
<scale value="1:2115"/>
</qgsScales>
Example:
1,000,000 (QGIS default):
1,083,357 (OSM wiki):
1,155,584 (From: 3liz):
Scales from:
OSM wiki
Zoom scales.