Quickstart

Quickstart#

Pyrosm reads OpenStreetMap data from .osm.pbf files into GeoPandas GeoDataFrames. Because it reads from local PBF dumps (rather than an online API), it can be used to parse data for whole cities or even countries very efficiently (assuming sufficient RAM is available).

This page is a five-minute tour; the other guides go deeper into each topic.

How to?

Download OSM data#

pyrosm ships a small test dataset and can download data for hundreds of regions with get_data(). Here we use the bundled test file:

from pyrosm import OSM, get_data

# A small bundled extract (or e.g. get_data("Helsinki") to download a city)
fp = get_data("malta")
fp

Downloaded Protobuf data 'malta-latest.osm.pbf' (8.35 MB) to:
'/var/folders/f2/pgp09jl542zffhtrt2hx8zhh0000gp/T/pyrosm/malta-latest.osm.pbf'

'/var/folders/f2/pgp09jl542zffhtrt2hx8zhh0000gp/T/pyrosm/malta-latest.osm.pbf'

You can also fetch data straight from a place name — this geocodes the place, downloads the covering Geofabrik extract, and crops it to the place which is stored to disk:

from pyrosm import get_data_by_geocoding

fp = get_data_by_geocoding("Tallinn, Estonia")

Geocoded 'Tallinn, Estonia' to: Tallinn, Harju maakond, Eesti
Geofabrik extract covering the area: 'Estonia' (id: estonia)

size_mb = os.path.getsize(fp) / (1024 * 1024)
print(fp)
print(f"{size_mb:.2f} MB")

/var/folders/f2/pgp09jl542zffhtrt2hx8zhh0000gp/T/pyrosm/tallinn-estonia.osm.pbf
25.15 MB

See further info from downloading OSM data for downloading by name, bounding box or place name.

Read a street network#

Initialise the OSM reader with a file path, then read a network for a given travel mode:

%matplotlib inline
osm = OSM(fp)
drive = osm.get_network("driving")
drive.head(3)
access bicycle bridge busway cycleway foot highway int_ref junction lanes ... tunnel turn width id timestamp version tags osm_type geometry length
0 NaN NaN NaN NaN NaN NaN living_street NaN NaN NaN ... NaN NaN NaN 4016128 1697738840 27 {"visible":false,"noname":"yes"} way MULTILINESTRING ((24.66446 59.39728, 24.66459 ... 16.0
1 NaN NaN NaN NaN NaN no secondary NaN NaN 2 ... NaN NaN NaN 4016133 1749933197 23 {"visible":false,"lanes:backward":"1","lanes:f... way MULTILINESTRING ((24.63663 59.3793, 24.63653 5... 1680.0
2 NaN NaN NaN NaN NaN no secondary NaN NaN 3 ... NaN NaN NaN 4016145 1776945157 40 {"visible":false,"trolley_wire":"yes"} way MULTILINESTRING ((24.70499 59.42929, 24.70475 ... 17.0

3 rows × 35 columns

ax = drive.plot(figsize=(6, 6), color="gray", linewidth=0.7)
_images/4057492eb85db57723a226be3bf8ff5579ad0efb7d7cbd0d9a04928affd4abf7.png

Read buildings and POIs#

The same reader extracts other datasets — buildings, Points of Interest, landuse, natural elements and boundaries:

buildings = osm.get_buildings()
pois = osm.get_pois()
print("buildings:", len(buildings), "| POIs:", len(pois))
ax = buildings.plot(figsize=(6, 6), color="lightblue", edgecolor="gray", linewidth=0.3)

buildings: 77736 | POIs: 65435
_images/ffe96e8665e50f036939eb0129072a0dac2fed1e3b846958b764154b382cd1b5.png

Export a network to a graph#

Street networks can be exported as a directed graph for networkx, igraph or pandarm:

nodes, edges = osm.get_network(nodes=True)
G = OSM.to_graph(nodes, edges, graph_type="networkx")
type(G)

networkx.classes.multidigraph.MultiDiGraph

Crop a PBF to a smaller file#

You can write a smaller .osm.pbf that covers only a bounding box — handy before sharing a file or re-reading a sub-area:

import os
from pyrosm import get_data

# Extent of the current data
minx, miny, maxx, maxy = buildings.total_bounds

# Keep roughly the south-west quarter and write it to a new PBF
bbox = [minx, miny, (minx + maxx) / 2, (miny + maxy) / 2]

# Write to PBF based on bounding box
cropped = OSM(fp, bounding_box=bbox).to_pbf()

size_mb = os.path.getsize(cropped) / (1024 * 1024)
print(f"wrote {os.path.basename(cropped)} ({size_mb:.2f} MB)")

wrote pyrosm_crop_3w8xgmfw.osm.pbf (17.72 MB)

Where to next#