A Python tool to parse OSM data from Protobuf format into GeoDataFrame.
Project description
Pyrosm
Pyrosm is a Python library for reading OpenStreetMap data from Protocolbuffer Binary Format -files (*.osm.pbf
) into Geopandas GeoDataFrames.
Pyrosm makes it easy to extract various datasets from OpenStreetMap pbf-dumps including e.g. road networks, buildings,
Points of Interest (POI), landuse and natural elements. Also fully customized queries are supported which makes it possible
to parse the data from OSM with more specific filters.
Pyrosm is easy to use and it provides a somewhat similar user interface as OSMnx. The main difference between pyrosm and OSMnx is that OSMnx reads the data over internet using OverPass API, whereas pyrosm reads the data from local OSM data dumps that can be downloaded e.g. from GeoFabrik's website. This makes it possible to read data much faster thus allowing e.g. parsing street networks for whole country in a matter of minutes instead of hours (however, see caveats).
The library has been developed by keeping performance in mind, hence, it is mainly written in Cython (Python with C-like performance) which makes it probably faster than any other Python alternatives for parsing OpenStreetMap data. Pyrosm is built on top of another Cython library called Pyrobuf which is a faster Cython alternative to Google's Protobuf library: It provides 2-4x boost in performance for deserializing the protocol buffer messages compared to Google's version with C++ backend. Google's Protocol Buffers is a commonly used and efficient method to serialize and compress structured data which is also used by OpenStreetMap contributors to distribute the OSM data in PBF format (Protocolbuffer Binary Format).
Documentation is available at https://pyrosm.readthedocs.io.
Current features
- download PBF data easily from hundreds of locations across the world
- read street networks (separately for driving, cycling, walking and all-combined)
- read buildings from PBF
- read Points of Interest (POI) from PBF
- read landuse from PBF
- read "natural" from PBF
- read boundaries from PBF (+ allow searching by name)
- read any other data from PBF by using a custom user-defined filter
- filter data based on bounding box
Roadmap
- add possibility to crop PBF and save a subset into new PBF.
- add Cython specific tests
Install
Pyrosm is distributed via PyPi and it can be installed with pip:
$ pip install pyrosm
Troubleshooting
Notice that pyrosm
requires geopandas to work.
On Linux and Mac installing geopandas with pip
should work without a problem, which is handled automatically when installing pyrosm.
However, on Windows installing geopandas with pip is likely to cause issues, hence, it is recommended to install Geopandas before installing
pyrosm
. See instructions from Geopandas website.
How to use?
Using pyrosm
is straightforward. See docs
for instructions how to use the library.
To read drivable street networks from OpenStreetMap protobuf file (package includes a small test protobuf file), simply:
Read street networks
from pyrosm import OSM
from pyrosm import get_path
fp = get_path("test_pbf")
# Initialize the OSM parser object
osm = OSM(fp)
# Read all drivable roads
# =======================
drive_net = osm.get_network(network_type="driving")
>>> drive_net.head()
...
access bridge ... id geometry
0 None None ... 4732994 LINESTRING (26.94310 60.52580, 26.94295 60.525...
1 None None ... 5184588 LINESTRING (26.94778 60.52231, 26.94717 60.522...
2 None yes ... 5184589 LINESTRING (26.94891 60.52181, 26.94778 60.52231)
3 None None ... 5184590 LINESTRING (26.94310 60.52580, 26.94452 60.525...
4 None None ... 22731285 LINESTRING (26.93072 60.52252, 26.93094 60.522...
[5 rows x 14 columns]
Read buildings
# Read all residential and retail buildings
# =========================================
from pyrosm import OSM
from pyrosm import get_path
fp = get_path("test_pbf")
# Initialize the OSM parser object
osm = OSM(fp)
custom_filter = {'building': ['residential', 'retail']}
buildings = osm.get_buildings(custom_filter=custom_filter)
>>> buildings.head()
...
building ... geometry
0 retail ... POLYGON ((26.94511 60.52322, 26.94487 60.52314...
1 retail ... POLYGON ((26.95093 60.53644, 26.95083 60.53642...
2 residential ... POLYGON ((26.96536 60.52540, 26.96528 60.52539...
3 residential ... POLYGON ((26.93920 60.53257, 26.93940 60.53254...
4 residential ... POLYGON ((26.96578 60.52129, 26.96569 60.52137...
Read Points of Interest
# Read POIs such as shops and amenities
# =====================================
from pyrosm import OSM
from pyrosm import get_path
fp = get_path("test_pbf")
# Initialize the OSM parser object
osm = OSM(fp)
custom_filter = {'amenity': True, 'shop': True }
pois = osm.get_pois(custom_filter=custom_filter)
>>> pois.head()
...
changeset timestamp lon version ... phone building landuse parking
0 0.0 1461601534 26.951475 2 ... NaN NaN NaN NaN
1 0.0 1310921959 26.945166 3 ... NaN NaN NaN NaN
2 0.0 1347308819 26.932177 2 ... NaN NaN NaN NaN
3 0.0 1310921960 26.949650 2 ... NaN NaN NaN NaN
4 0.0 1307995246 26.959021 1 ... NaN NaN NaN NaN
[5 rows x 22 columns]
Read landuse/natural
# Read landuse and natural
# =====================================
from pyrosm import OSM
from pyrosm import get_path
fp = get_path("test_pbf")
# Initialize the OSM parser object
osm = OSM(fp)
landuse = osm.get_landuse()
natural = osm.get_natural()
>>> natural.head()
...
id timestamp changeset ... geometry osm_type water
0 29985880 1496174642 0.0 ... POINT (24.95299 60.17726) node NaN
1 379182204 1511211673 0.0 ... POINT (24.95300 60.16710) node NaN
2 946524698 1286962007 0.0 ... POINT (24.94548 60.17759) node NaN
3 1533462976 1408442828 0.0 ... POINT (24.95214 60.17820) node NaN
4 1533462983 1408442828 0.0 ... POINT (24.95223 60.17820) node NaN
[5 rows x 12 columns]
Read OSM data with custom filter
Pyrosm also allows making custom queries. For example, to parse all transit related OSM elements you can use following approach and create a custom filter combining multiple criteria:
from pyrosm import OSM
from pyrosm import get_path
fp = get_path("helsinki_pbf")
# Initialize the OSM parser object with test data from Helsinki
osm = OSM(fp)
# Test reading all transit related data (bus, trains, trams, metro etc.)
# Exclude nodes (not keeping stops, etc.)
routes = ["bus", "ferry", "railway", "subway", "train", "tram", "trolleybus"]
rails = ["tramway", "light_rail", "rail", "subway", "tram"]
bus = ['yes']
transit = osm.get_data_by_custom_criteria(custom_filter={
'route': routes,
'railway': rails,
'bus': bus,
'public_transport': True},
# Keep data matching the criteria above
filter_type="keep",
# Do not keep nodes (point data)
keep_nodes=False,
keep_ways=True,
keep_relations=True)
>>> transit.head()
bicycle bus ... geometry osm_type
0 None None ... LINESTRING (24.94133 60.17141, 24.94114 60.173... way
1 None None ... LINESTRING (24.94024 60.17530, 24.94020 60.175... way
2 None None ... LINESTRING (24.94115 60.17597, 24.94092 60.176... way
3 no yes ... LINESTRING (24.94271 60.17099, 24.94282 60.17093) way
4 None None ... LINESTRING (24.93872 60.16970, 24.93893 60.169... way
[5 rows x 17 columns]
Help
To get further information how to use the tool, you can use good old help
:
help(osm.get_network)
...
Help on method get_network in module pyrosm.pyrosm:
get_network(network_type='walking') method of pyrosm.pyrosm.OSM instance
Reads data from OSM file and parses street networks
for walking, driving, and cycling.
Parameters
----------
network_type : str
What kind of network to parse. Possible values are: 'walking' | 'cycling' | 'driving' | 'all'.
Performance
See docs for more comprehensive benchmarking tests. Reading all drivable roads in Helsinki Region (approx. 85,000 roads) takes approximately 12 seconds (laptop with 16GB memory, SSD drive, and Intel Core i5-8250U CPU 1.6 GHZ). And the result looks something like:
Parsing all buildings from the same area (approx. 180,000) takes approximately 17 seconds. And the result looks something like:
Parsing all Points of Interest (POIs) with defaults elements (amenities, shops and tourism) takes approximately 14 seconds (approx. 32,000 features). And the result looks something like:
Get in touch
If you find a bug from the tool, have question, or would like to suggest a new feature to it, you can make a new issue here.
Development
You can install a local development version of the tool by 1) installing necessary packages with conda and 2) building pyrosm from source:
-
install conda-environment for Python 3.7 or 3.8 by:
- Python 3.7 (you might want to modify the env-name which is
test
by default):$ conda env create -f ci/37-conda.yaml
- Python 3.8:
$ conda env create -f ci/38-conda.yaml
- Python 3.7 (you might want to modify the env-name which is
-
build pyrosm development version from master (activate the environment first):
pip install -e .
You can run tests with pytest
by executing:
$ pytest -v
Caveats
Filtering large files by bounding box
Although pyrosm
provides possibility to filter even larger data files based on bounding box,
this process can slow down the reading process significantly (1.5-3x longer) due to necessary lookups when parsing the data.
This might not be an issue with smaller files (up to ~100MB) but with larger data dumps this can take longer than necessary.
Hence, a recommended approach with large data files is to first filter the protobuf file based on bounding box into a smaller subset by using a dedicated open source Java tool called Osmosis which is available for all operating systems. Detailed installation instructions are here, and instructions how to filter data based on bounding box are here.
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