Based on the work by:
Comparing City 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.
Largest populated places by population. Based on the
Ordnance Survey Ireland urban areas. As it is OSI data, Northern Ireland is not included.
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:
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:
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 >
1,083,357 (OSM wiki):
Some of the outputs from my Data Driven Cartography workshop at the
2nd OSGeo Ireland conference.
I created a couple of OSM visualisations for my talk at the OSGeo Ireland conference.
History of OpenStreetMap in Ireland
These are pretty easy to make, but take a fair bit of time. I did mine for Ireland, but should work with any part of the world.
This is the trickiest part, installing osmium-tools:
An OSM full history export. The best source for these is GEOFABRIK.
Due to GDPR, you will have to log in with an OSM id to download the full history extracts. User ID’s are personal data.
The workflow is pretty simple. Osmium-tools provides pretty easy API access to the history files, where you can provide a data, and it will extract what OSM was like at that date. We simply need to loop through the desired dates we want to extract, and pipe the results into a workflow that loads the data into PostgreSQL. The final step is simply rendering in QGIS using the time manager plugin.
The tables in the database will be:
Each feature will be tagged with the date it is associated with.
To visualise the data in QGIS we use simply use the excellent
time manager plugin, filtering on the load_date field and with a monthly interval.
This entry was posted in
All, Data, Ireland, Open Data, OpenStreetMap, OSGeo, PostGIS, PostgreSQL, Python, QGIS, Tutorials on . 06/06/2018