Work in progress... #OpenStreetMap data as usual #osmnx Code at: https://github.com/villares/sketch-a-day/tree/main/2025/sketch_2025_03_12
More sketch-a-day: https://abav.lugaralgum.com/sketch-a-day
If you like this, support my work: https://www.paypal.com/donate/?hosted_button_id=5B4MZ78C9J724 #Processing #Python #py5 #CreativeCoding

I recently moved house, so I didn’t have time to take part in #30DayMapChallenge in November. Now that I’m getting settled, I want to start exploring my new neighbourhood and get back to mapping.

So here is Day 1 - Points showing council maintained trees in Abbey Wood, southeast London.

Made in #python #matplotlib. Basemap data from #OpenStreetMap via #OSMnx (finally updated to v2.0). Tree data for London is available in the London Datastore.

Code: https://github.com/Lisa-Ho/30-day-map-challenge/tree/main/2024

#OSMnx 2.0.0 has just been released! https://osmnx.readthedocs.io/en/stable/

Nom.

https://fediscience.org/@tlohde/113412191058419280
@tlohde - Croissant Distribution

or, where am I most likely to have my next croissant, assuming (something something) independence from my previous croissant. ^k * e^(-) / k!
--
2d kernel density estimation of the location of bakeries (blue) and farms (red) across France.

Locations taken from OpenStreetMap, with #osmnx & plotted with #matplotlib & #seaborn. font: Borel, by Rosalie Wagner.

#30DayMapChallenge Day 2: Lines

Croissant Distribution

or, where am I most likely to have my next croissant, assuming (something something) independence from my previous croissant. ^k * e^(-) / k!
--
2d kernel density estimation of the location of bakeries (blue) and farms (red) across France.

Locations taken from OpenStreetMap (considered paying attention to the size of the farm, but didn't), with #osmnx & plotted with #matplotlib & #seaborn. font: Borel, by Rosalie Wagner.

#30DayMapChallenge Day 2: Lines

Calculating different #centrality measures for a street #network takes longer than expected.

#DegreeCentrality is calculated in a few milliseconds. But oh boy. #ClosenessCentrality and #BetweennessCentrality are proper whoppers. For a network of 65 000 nodes, we're talking about 2+ hour calculation times for the closeness centrality, not to mention the betweenness.

Apparently switching to #igraph would provide a speed boost over #networkx but the convenience of #osmnx has won me over.

For those interested in such things, here is a connoisseurs map of "European" current rail-electrification based on @openstreetmap tagged data

Electrified track OLE is red, third- and fourth-rail DC in green and brown, unelectrified in black

Colours in the spirit of the FizzyKnitting @25kV

Developed in @python using #OSMnx to wrangle the data and visualised in @qgis

For those interested in such things, here is a map of "European" current rail-electrification based on @openstreetmap tagged data

Electrified track is red, all other track in black

Developed in @python using #OSMnx to wrangle the data and visualised in @qgis

True connoisseurs of the art please note, this does not make a distinction between voltage, frequency, and type (OLE/third-rail/fourth-rail)

#30DayMapChallenge Day 6 - Network

Initially I wanted to map the amazing tram network in Amsterdam, but #osmnx doesn't provide data for that easily and I wanted this to be a relatively fast map after yesterday. Sticking with the #Amsterdam concept I decided to map their famous #CycleNetwork and to throw in a little twist, I was inspired by a tutorial I found to colour code the map by the length of the cycle lane.

Tutorial inspo:
https://towardsdatascience.com/making-artistic-maps-with-python-9d37f5ea8af0