Greg Cocks Modeling And Analyzing Urban Networks And Amenities With OSMnx
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https://doi.org/10.1111/gean.70009 <-- shared technical article / 'how to'
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#GIS #spatial #mapping #OSMnx #OpenStreetMap #model #modeling #python #code #spatialanalysis #urban #networks #roads #transportation #infrastructure #geography #urbanplanning #planning #usage #design #software #development #openscience #opensource #analysis #tutorial #howto
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#osmnx
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«The official OSMnx reference paper has just been published open-access by Geographical Analysis: [link below] Years in the making, this article describes what OSMnx does and why it does it that way. But wait, there's more! I also discuss many lessons learned over the past decade in geospatial software development [...]» — @gboeing
Modeling and Analyzing Urban Networks and Amenities With OSMnx
I had missed the great episode with Dr. Geoff Boeing at #TalkPythonToMe: #495: #OSMnx: #Python and #OpenStreetMap
I really love OSMnx and use it all the time, often with #py5 (which has a cool #shapely integration that helps).
Episode webpage: https://talkpython.fm/episodes/show/495/osmnx-python-and-openstreetmap
Media file: https://talkpython.fm/episodes/download/495/osmnx-python-and-openstreetmap.mp3
talkpython.fmOSMnx: Python and OpenStreetMapOn this episode, I'm joined by Dr. Geoff Boeing, an assistant professor at the University of Southern California whose research spans urban planning, spatial analysis, and data science. We explore why OpenStreetMap is such a powerful source of global map data—and how Geoff's Python library, OSMnx, makes that data easier to download, model, and visualize. Along the way, we talk about what shapes city streets around the world, how urban design influences everything from daily commutes to disaster resilience, and why turning open data into accessible tools can open up completely new ways of understanding our cities. If you've ever wondered how to build or analyze your own digital maps in Python, or what it takes to manage a project that transforms raw geographic data into meaningful research, you won't want to miss this conversation.
#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.
GitHubRelease OSMNX 2.0 update · nathanrooy/taxicabAddresses the following:
OSMNX 2.0 updates (#15) thanks @rubchacon and @villares
Chronological point ordering (#10) thanks @YannickAaron
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
Code at: https://github.com/villares/sketch-a-day/tree/main/2025/sketch_2025_02_26
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
#OSMnx #OpenStreetMap #shapely #Processing #Python #py5 #CreativeCoding
#SãoPaulo
Code at: https://github.com/villares/sketch-a-day/tree/main/2025/sketch_2025_02_24
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
#shapely #osmnx #OpenStreetMap
Poly data (c) OpenStreetMap contributors https://www.openstreetmap.org/copyright
#Processing #Python #py5 #CreativeCoding
Latest episode: #OSMnx - #Python and #OpenStreetMap with
@gboeing and @mkennedy
https://talkpython.fm/episodes/show/495/osmnx-python-and-openstreetmap
talkpython.fmOSMnx: Python and OpenStreetMapOn this episode, I'm joined by Dr. Geoff Boeing, an assistant professor at the University of Southern California whose research spans urban planning, spatial analysis, and data science. We explore why OpenStreetMap is such a powerful source of global map data—and how Geoff's Python library, OSMnx, makes that data easier to download, model, and visualize. Along the way, we talk about what shapes city streets around the world, how urban design influences everything from daily commutes to disaster resilience, and why turning open data into accessible tools can open up completely new ways of understanding our cities. If you've ever wondered how to build or analyze your own digital maps in Python, or what it takes to manage a project that transforms raw geographic data into meaningful research, you won't want to miss this conversation.
#OSMnx Principales caracteristicas del paquete de #Python para descargar, modelar, analizar y visualizar fácilmente características geoespaciales de #OpenStreetMap https://medium.com/@kaxtillo/osmnx-caracteristicas-3b8e61d9f256
Medium · OSMnx principales caracteristicas - Carlos Felipe Castillo - Medium
Antwortete im Thread
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
day 22: 2 colours.
is #halftone cheating?
the Lake District with halftone topography taken from Copernicus Global DEM and lakes and rivers from #OpenStreetMap
#30DayMapChallenge Day 19: #typography
Bit fiddly this one. But reasonably happy with it.
Data from #OpenStreetMap via #osmnx
fonts used: water splash (https://www.fontspace.com/water-splash-script-font-f89646); Engravers MT; DejaVu Sans Mono; Bauhaus 93.
all done in #python with #matplotlib #geopandas and #shapely
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!
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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
FediScience.orgtlohde (@tlohde@fediscience.org)Angehängt: 1 Bild
Croissant Distribution🥐 📊
or, where am I most likely to have my next croissant, assuming (something something) independence from my previous croissant. 🥐^k * e^(-🥐) / k!
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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
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
