pyrosm 0.4.0

A Python tool to parse OSM data from Protobuf format into GeoDataFrame.

Pyrosm

Pyrosm is a Python library for reading OpenStreetMap from protobuf 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).

Current features

• 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 any other data from PBF by using a custom user-defined filter
• filter data based on bounding box

Roadmap

• improve docs and make simple website
• run benchmarks against other tools
• add possibility to crop PBF and save a subset into new PBF.
• automate PBF downloading from Geofabrik (?)

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. To read drivable street networks from OpenStreetMap protobuf file (package includes a small test protobuf file), simply:

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 all residential and retail buildings
# =========================================
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 POIs such as shops and amenities 
# =====================================
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]

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'.

Examples

For further usage examples (for now), take a look at the tests, such as:

• test_network_parsing.py
• test_building_parsing.py

Performance

Proper benchmarking results are on their way, but to give some idea, 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:

1. 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

2. 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.