1/ I've posted this kind of thing before: a flight of steps that is not tagged as steps in OSM.

But this time is different. This time, I didn't find the steps by randomly walking around Malaga. This time, I had A System.

With the help of the OSMnx package and IGN elevation data, I was able to estimate gradients of street segments in OSM data. The example shows one barrio (Maqueda) that has a flat eastern part, and a western part built on both sides of a ridge.

#OpenStreetMap #osmnx #SãoPaulo
Code at: https://github.com/villares/sketch-a-day/tree/main/2025/sketch_2025_03_13
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

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

It is super cool to be listed as a "contributor" for having chimed in (constructively) on a project's issue :D

https://github.com/nathanrooy/taxicab/releases/tag/v0.1.1

#taxicab is a library that improves on #OSMnx route-finding function, giving more useful results, specially on short routes.

Code at: https://github.com/villares/sketch-a-day/tree/main/2025/sketch_2025_02_27
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

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/

Regional Capitals of Italy.

Polygons of Parks and gardens (green), buildings (white) and piazzas/squares (red). Data from #OpenStreetMap via #osmnx and plotted with #geopandas & #matplotlib. font: Scoglietto by Petra Heidorn

took me far too long to work out the required OpenStreetMap tags to get the nodes for the regional capitals.

#30DayMapChallenge Day 3: #Polygons

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

Fidddling with #py5 #geospacial #CreativeCoding #prettymaps #shapely #OSM #osmnx #GeoDataFrames
https://github.com/py5coding/py5generator/discussions/484

#osmnx sur #rouen, en rose les voies probablement à 30km/h, en vert celles qui ont été renseignées à 30km/h.

petit essai avec #osmnx pour tenter d'inférer les maxspeed des routes, ici en rose dans la ville de dieppe on suppose que la vitesse max est de 30km/h.

Despite my recent absolute failure with #LazyMaston, here we go again:

Dear toot-pals, where can I find wholesome learning materials on #matplotlib and/or #maps /#cartography / #geodataviz in #Python?

I've been playing with #OpenStreetMaps via #osmnx, which is awesome , but I struggle with simple stuff like adding a bunch of places as markers. Everything looks a bit like the owl drawing meme, either showing something too easy and useless, or something too advanced and also useless or beyond my comprehension. Maybe some other Python tools?
(I know about Marcelo's fabulous PrettyMaps but it is not exactly a viz tool)

A geodataframe "hello world" ?

day 15 (*only* half way): OpenStreetMap

benches. that's it. all 111,694 of 'em.

Data from #OpenStreetMap via #osmnx

code here: https://github.com/tlohde/30DMC/tree/main/2023

#30DayMapChallenge

might go have a sit down.

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

Update this is an OLE map

For true connoisseurs of the art please note, this makes a distinction between voltage, frequency, and type (OLE/third-rail/fourth-rail)