Python osm python tools

Saved searches

Use saved searches to filter your results more quickly

You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session.

A library to access OpenStreetMap related services

License

eric-erki/osm-python-tools

This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?

Sign In Required

Please sign in to use Codespaces.

Launching GitHub Desktop

If nothing happens, download GitHub Desktop and try again.

Launching GitHub Desktop

If nothing happens, download GitHub Desktop and try again.

Launching Xcode

If nothing happens, download Xcode and try again.

Launching Visual Studio Code

Your codespace will open once ready.

There was a problem preparing your codespace, please try again.

Latest commit

Git stats

Files

Failed to load latest commit information.

README.md

The python package OSMPythonTools provides easy access to OpenStreetMap (OSM) related services, among them an Overpass endpoint, Nominatim, and the OSM API.

To install OSMPythonTools , you will need python3 and pip (How to install pip). Then execute:

pip install OSMPythonTools

On some operating systems, pip for python3 will be named pip3 :

pip3 install OSMPythonTools

Which object does the way with the id 5887599 represent?

We can use the OSM API to answer this question:

from OSMPythonTools.api import Api api = Api() way = api.query('way/5887599')

The resulting object contains information about the way, which can easily be accessed:

way.tag('building') # 'castle' way.tag('architect') # 'Johann Lucas von Hildebrandt' way.tag('website') # 'http://www.belvedere.at'

What is the English name of the church called ‘Stephansdom’, what address does it have, and which of which denomination is the church?

We use the Overpass API to query the corresponding data:

from OSMPythonTools.overpass import Overpass overpass = Overpass() result = overpass.query('way["name"="Stephansdom"]; out body;')

This time, the result is a number of objects, which can be accessed by result.elements() . We just pick the first one:

stephansdom = result.elements()[0]

Information about the church can now easily be accessed:

stephansdom.tag('name:en') # "Saint Stephen's Cathedral" '%s %s, %s %s' % (stephansdom.tag('addr:street'), stephansdom.tag('addr:housenumber'), stephansdom.tag('addr:postcode'), stephansdom.tag('addr:city')) # 'Stephansplatz 3, 1010 Wien' stephansdom.tag('building') # 'cathedral' stephansdom.tag('denomination') # 'catholic'

How many trees are in the OSM data of Vienna? And how many trees have there been in 2013?

This time, we have to first resolve the name ‘Vienna’ to an area id:

from OSMPythonTools.nominatim import Nominatim nominatim = Nominatim() areaId = nominatim.query('Vienna, Austria').areaId()

This area id can now be used to build the corresponding query:

from OSMPythonTools.overpass import overpassQueryBuilder, Overpass overpass = Overpass() query = overpassQueryBuilder(area=areaId, elementType='node', selector='"natural"="tree"', out='count') result = overpass.query(query) result.countElements() # 137830

There are 134520 trees in the current OSM data of Vienna. How many have there been in 2013?

result = overpass.query(query, date='2013-01-01T00:00:00Z', timeout=60) result.countElements() # 127689

Where are waterbodies located in Vienna?

Again, we have to resolve the name ‘Vienna’ before running the query:

from OSMPythonTools.nominatim import Nominatim nominatim = Nominatim() areaId = nominatim.query('Vienna, Austria').areaId()

The query can be built like in the examples before. This time, however, the argument includeGeometry=True is provided to the overpassQueryBuilder in order to let him generate a query that downloads the geometry data.

from OSMPythonTools.overpass import overpassQueryBuilder, Overpass overpass = Overpass() query = overpassQueryBuilder(area=areaId, elementType=['way', 'relation'], selector='"natural"="water"', includeGeometry=True) result = overpass.query(query)

Next, we can exemplarily choose one random waterbody (the first one of the download ones) and compute its geomtry like follows:

firstElement = result.elements()[0] firstElement.geometry() #

Observe that the resulting geometry is provided in the GeoJSON format.

How did the number of trees in Berlin, Paris, and Vienna change over time?

Before we can answer the question, we have to import some modules:

from collections import OrderedDict from OSMPythonTools.data import Data, dictRangeYears, ALL from OSMPythonTools.overpass import overpassQueryBuilder, Overpass

The question has two ‘dimensions’: the dimension of time, and the dimension of different cities:

dimensions = OrderedDict([ ('year', dictRangeYears(2013, 2017.5, 1)), ('city', OrderedDict(< 'berlin': 'Berlin, Germany', 'paris': 'Paris, France', 'vienna': 'Vienna, Austria', >)), ])

We have to define how we fetch the data. We again use Nominatim and the Overpass API to query the data (it can take some time to perform this query the first time!):

overpass = Overpass() def fetch(year, city): areaId = nominatim.query(city).areaId() query = overpassQueryBuilder(area=areaId, elementType='node', selector='"natural"="tree"', out='count') return overpass.query(query, date=year, timeout=60).countElements() data = Data(fetch, dimensions)

We can now easily generate a plot from the result:

data.plot(city=ALL, filename='example4.png')

Alternatively, we can generate a table from the result

data.select(city=ALL).getCSV() # year,berlin,paris,vienna # 2013.0,10180,1936,127689 # 2014.0,17971,26905,128905 # 2015.0,28277,90599,130278 # 2016.0,86769,103172,132293 # 2017.0,108432,103246,134616

More examples can be found inside the documentation of the modules.

The following modules are available (please click on their names to access further documentation):

• OSMPythonTools.Api — Access to the official OSM API
• OSMPythonTools.Data — Collecting, mining, and drawing data from OSM; to be used in combination with the other modules
• OSMPythonTools.Element — Elements are returned by other services, like the OSM API or the Overpass API
• OSMPythonTools.Nominatim — Access to Nominatim, a reverse geocoder
• OSMPythonTools.Overpass — Access to the Overpass API

This application is written and maintained by Franz-Benjamin Mocnik, mail@mocnik-science.net.

(c) by Franz-Benjamin Mocnik, 2017-2020.

The code is licensed under the GPL-3.

About

A library to access OpenStreetMap related services

Источник

OSMPythonTools

The python package OSMPythonTools provides easy access to OpenStreetMap related services, among them an Overpass endpoint, Nominatim, and the OSM API.

Installation

To install OSMPythonTools , you will need python3 and pip (How to install pip). Then execute:

pip install OSMPythonTools

On some operating systems, pip for python3 will be named pip3 :

pip3 install OSMPythonTools

Example 1

Which object does the way with the id 5887599 represent?

We can use the OSM API to answer this question:

from OSMPythonTools.api import Api api = Api() way = api.query('way/5887599') 

The resulting object contains information about the way, which can easily be accessed:

way.tag('building') # 'castle' way.tag('architect') # 'Johann Lucas von Hildebrandt' way.tag('website') # 'http://www.belvedere.at' 

Example 2

What is the English name of the church called «Stephansdom», what address does it have, and which of which denomination is the church?

We use the Overpass API to query the corresponding data:

from OSMPythonTools.overpass import Overpass overpass = Overpass() result = overpass.query('way["name"="Stephansdom"]; out body;') 

This time, the result is a number of objects, which can be accessed by result.elements() . We just pick the first one:

stephansdom = result.elements()[0] 

Information about the church can now easily be accessed:

stephansdom.tag('name:en') # "Saint Stephen's Cathedral" '%s %s, %s %s' % (stephansdom.tag('addr:street'), stephansdom.tag('addr:housenumber'), stephansdom.tag('addr:postcode'), stephansdom.tag('addr:city')) # 'Stephansplatz 3, 1010 Wien' stephansdom.tag('building') # 'cathedral' stephansdom.tag('denomination') # 'catholic' 

Example 3

How many trees are in the OSM data of Vienna? And how many trees have there been in 2013?

This time, we have to first resolve the name «Vienna» to an area id:

from OSMPythonTools.nominatim import Nominatim nominatim = Nominatim() areaId = nominatim.query('Vienna, Austria').areaId() 

This area id can now be used to build the corresponding query:

from OSMPythonTools.overpass import overpassQueryBuilder, Overpass overpass = Overpass() query = overpassQueryBuilder(area=areaId, elementType='node', selector='"natural"="tree"', out='count') result = overpass.query(query) result.countElements() # 137830 

There are 134520 trees in the current OSM data of Vienna. How many were there in 2013?

result = overpass.query(query, date='2013-01-01T00:00:00Z', timeout=60) result.countElements() # 127689 

Example 4

How did the number of trees in Berlin, Paris, and Vienna change over time?

Before we can answer the question, we have to import some modules:

from collections import OrderedDict from OSMPythonTools.data import Data, dictRangeYears, ALL from OSMPythonTools.overpass import overpassQueryBuilder, Overpass 

The question has two «dimensions»: the dimension of time, and the dimension of different cities:

dimensions = OrderedDict([ ('year', dictRangeYears(2013, 2017.5, 1)), ('city', OrderedDict( 'berlin': 'Berlin, Germany', 'paris': 'Paris, France', 'vienna': 'Vienna, Austria', >)), ]) 

We have to define how we fetch the data. We again use Nominatim and the Overpass API to query the data (it can take some time to perform this query the first time!):

overpass = Overpass() def fetch(year, city): areaId = nominatim.query(city).areaId() query = overpassQueryBuilder(area=areaId, elementType='node', selector='"natural"="tree"', out='count') return overpass.query(query, date=year, timeout=60).countElements() data = Data(fetch, dimensions) 

We can now easily generate a plot from the result:

data.plot(city=ALL, filename='example4.png') 

Alternatively, we can generate a table from the result

data.select(city=ALL).getCSV() # year,berlin,paris,vienna # 2013.0,10180,1936,127689 # 2014.0,17971,26905,128905 # 2015.0,28277,90599,130278 # 2016.0,86769,103172,132293 # 2017.0,108432,103246,134616 

More examples can be found inside the documentation of the modules.

Documentation

For further documentation, please refer to mocnik-science/osm-python-tools .

Источник