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Download, read/parse and import/export OpenStreetMap data extracts

Project description


Author: Qian Fu Twitter URL

PyPI PyPI - Python Version PyPI - License GitHub code size in bytes PyPI - Downloads

This package provides helpful utilities for researchers to easily download and read/parse the OpenStreetMap data extracts (in .pbf and which are available at the free download servers: Geofabrik and BBBike. In addition, it also provides a convenient way to import/dump the parsed data to, and retrieve it from, a PostgreSQL sever.


Windows OS users may the "pip install" command in Command Prompt:

pip install --upgrade pydriosm

If you are using some IDE, such as PyCharm, you should be able to find pydriosm in the PyPI repository. (In PyCharm, go to "Settings" and find pydriosm in "Project Interpreter"; to install it, select pydriosm and then click "Install Package".)

  • Successful installation of pydriosm (and ensuring its full functionality) requires a few dependencies. On Windows OS, however, pip install may fail to go through the installation of some supporting packages, such as python-Levenshtein, Fiona, GDAL and Shapely. In that case, you might have to resort to installing their .whl files, which can be downloaded from the Unofficial Windows Binaries for Python Extension Packages. Once those packages are all ready, we could go ahead with the pip command.

Quick start

Firstly, we import the package:

import pydriosm as dri

The current version of the package deals only with subregion data files provided on the free server. To get a full list of subregion names that are available, you can use

subregion_list = dri.fetch_subregion_info_catalogue("GeoFabrik-subregion-name-list")

Below is an example of using .pbf data of the "Greater London" area to demonstrate briefly some main functions this package can do.

Download data

To download the OSM data for a region (or rather, a subregion) of which the data extract is available, you need to specify the name of the region (e.g. "Greater London"):

subregion_name = 'Greater London'
# or, subregion_name = 'london'; case-insensitive and fuzzy (but not toooo... fuzzy)

Download .pbf data of "Greater London":

dri.download_subregion_osm_file(subregion_name, osm_file_format=".osm.pbf", 
                                download_dir=None, update=False,

Note that download_dir is None by default. In that case, a default file path will be created and the downloaded file will be saved there.

Check the default file path and name:

default_fn, default_fp = dri.get_default_path_to_osm_file(subregion_name, 
                                                          mkdir=False, update=False)
print("Default filename: {}".format(default_fn))
print("Default file path: {}".format(default_fp))

However, you may also set download_dir to be any other valid directory, especially when downloading data of multiple subregions. For example,

# Specify the our own data directory
customised_data_dir = "test_data"
# So "test_data" folder will be created in our current working directory

# Alternatively, we could specify a full path 
# import os
# customised_data_dir = os.path.join(os.getcwd(), "test_data")

# Download .pbf data of both 'London' and 'Kent' to the `customised_data_dir`
dri.download_subregion_osm_file('London', 'Kent', 
                                osm_file_format=".osm.pbf", update=False,

The .pbf data file will then be saved to the download_dir as specified.

Read/parse data

Parsing the .pbf data relies mainly on GDAL/OGR, using read_osm_pbf() function.

greater_london = dri.read_osm_pbf(subregion_name, data_dir=None, parsed=True, 
                                  file_size_limit=50, fmt_other_tags=True, 
                                  fmt_single_geom=True, fmt_multi_geom=True, 
                                  update=False, download_confirmation_required=True, 
                                  pickle_it=True, rm_raw_file=False)

The parsing process may take a few minutes or even longer if the data file is too large. If the file size is greater than the given file_size_limit (default: 50 MB), the data will be parsed in a chunk-wise manner.

Note that greater_london is a dict with its keys being the name of five different layers: "points", "lines", "multilinestrings", "multipolygons" and "other_relations".

If only the name of a subregion is given, i.e. read_osm_pbf(subregion_name, ...), the function will go to look for the data file from the default file path. Otherwise, the function requires a specific data directory. For example, to read/parse the data in customised_data_dir, i.e. "test_data" folder, you need to set data_dir=customised_data_dir as follows:

greater_london_test = dri.read_osm_pbf(subregion_name, data_dir=customised_data_dir)

greater_london and greater_london_test should be the same.

To make life easier, you can simply skip the download step and use read_osm_pbf() directly. That is, if the targeted data is not available, read_osm_pbf() will download the data file first. By default, a confirmation of downloading the data will be prompted, given that download_confirmation_required=True.

Setting pickle_it=True is to save a local copy of the parsed data as a pickle file. If update=False, when you run read_osm_pbf(subregion_name) again, the function will load the pickle file directly. If update=True, the function will try to download the latest version of the data file and parse it again.

In comparison, you can use read_shp_zip(), which relies mainly on GeoPandas, to read data files:

# We need to specify a layer, e.g. 'railways'
layer_name = 'railways'

greater_london_shp = dri.read_shp_zip(subregion_name, layer=layer_name, 
                                      feature=None, data_dir=None, update=False,
                                      pickle_it=True, rm_extracts=False)

Note that greater_london_shp and greater_london are different.

To get information about more than one subregion, you can also merge .shp files of specific layers from those subregions. For example, to merge the "railways" layer of two subregions: "Greater London" and "Essex", we could do as follows.

subregion_names=['Greater London', 'Kent']
# layer_name = 'railways'
dri.merge_multi_shp(subregion_names, layer=layer_name, update_shp_zip=False, 
                   download_confirmation_required=True, output_dir=None)

You could also set data_dir=customised_data_dir to save the downloaded files; or output_dir=customised_data_dir to make the merged .shp file available into customised_data_dir.

Import and retrieve data with a PostgreSQL server

Pydriosm also provides a class, named "OSM", which communicates with PostgreSQL server.

osmdb = dri.OSM()

To establish a connection with the server, you will be asked to type in your username, password, host name/address and name of the database you intend to connect.

For example, you may type in "postgres" to connect the default database. (Note that the quotation marks should be removed when typing in the name.)

If you would like to connect to another database (instead of the default "postgres"), run


Then, a database named "osm_pbf_data_extracts" will be created automatically if it does not exist before the connection is established.

(1) Import the data to the database

To import greater_london (i.e. the parsed .pbf data of "Greater London") to the database, "osm_pbf_data_extracts":

osmdb.dump_osm_pbf_data(greater_london, table_name=subregion_name, parsed=True, 
                        if_exists='replace', chunk_size=None,

Each element (i.e. layer) of greater_london data will be stored in a different schema. The schema is named as the name of each layer.

(2) Retrieve data from the database

To retrieve the dumped data:

greater_london_retrieval = osmdb.read_osm_pbf_data(table_name=subregion_name, 
                                                   chunk_size=None, id_sorted=True)

Note that greater_london_retrieval may not be exactly the same as greater_london. This is because the keys of the elements in greater_london are in the following order: 'points', 'lines', 'multilinestrings', 'multipolygons' and 'other_relations'; whereas when dumping greater_london to the database, the five different schemas are sorted alphabetically as follows: 'lines', 'multilinestrings', 'multipolygons', 'other_relations', and 'points', and so retrieving data from the server will be in the latter order. Despite that, the data contained in both greater_london and greater_london_retrieval is consistent.

If you need to query data of a specific layer (or several layers), or in a specific order of layers (schemas):

london_points_lines = osmdb.read_osm_pbf_data(subregion_name, 'points', 'lines')
# Another example:
# london_lines_mul = osmdb.read_osm_pbf_data('london', 'lines', 'multilinestrings')

(3) Import data of all subregions of a given (sub)region to the database

# Find all subregions (without smaller subregions) of a subregion.
# Take for example, to find all subregions of 'England':
subregions = dri.retrieve_subregion_names_from('England')

# Import data of all contained in `subregions`
dri.psql_osm_pbf_data_extracts(subregions, database_name='osm_pbf_data_extracts', 
                               data_dir=None, update_osm_pbf=False, 
                               if_table_exists='replace', file_size_limit=50,
                               parsed=True, fmt_other_tags=True, 
                               fmt_single_geom=True, fmt_multi_geom=True, 

Setting rm_raw_file=False and data_dir=None will keep all raw .pbf data files in the default data folder.

If you would like to import all subregion data of "Great Britain":

gb_subregions = dri.retrieve_subregion_names_from('Great Britain')

Instead of returning ['England', 'Scotland', 'Wales'], the list gb_subregions will include all subregions of "England" (rather than "England" as a single element), "Scotland" and "Wales".

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Data/Map data © Geofabrik GmbH and OpenStreetMap Contributors

All data from the OpenStreetMap is licensed under the OpenStreetMap License.

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