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Create networkx graphs from OpenStreetMap data
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Paul Rosenzweig
Latest commit 4f8e646 Mar 3, 2016

README.md

osmgraph

Create networkx graphs from OpenStreetMap (OSM) data. osmgraph uses imposm-parser for parsing OpenStreetMap XML (including bz2) and PBF files and osmqa-parser for parsing OSM QA tiles.

Usage

>>> import osmgraph
>>> g = osmgraph.parse_file(filename)

Install osmqa-parser to import a graph from a vector tile. Read the documentation to understand the caveats associated with forming a network from OSM QA tiles.

### Load data from mbtiles or other source
>>> g = osmgraph.parse_qa_tile(x, y, zoom, data)

Graph Structure

osmgraph parses OSM data to create a networkx directed graph. OSM nodes correspond directly to the nodes in the directed graph. The OSM tags become attributes of the node. Additionally osmgraph adds a coordinate attribute containing the (lon, lat) tuple of the node's coordinates.

For example:

g = osmgraph.parse_file('boston_massachusetts.osm.bz2')

Given the following XML node:

  <node id="665539692" lat="42.3971185" lon="-71.0207486" version="2" timestamp="2014-06-25T04:45:25Z" changeset="23135192" uid="422979" user="Parcanman">
    <tag k="railway" v="level_crossing"/>
  </node>
>>> g.node[665539692]
{'coordinate': (-71.0207486, 42.3971185), 'railway': 'level_crossing'}

Similarly, the nodes comprising an OSM way form the graph's edges. The way's attributes are duplicated across the edges. For example, given the following XML way:

  <way id="8636532" version="13" timestamp="2011-01-14T00:47:46Z" changeset="6963395" uid="381909" user="JessAk71">
    <nd ref="61448456"/>
    <nd ref="1102764005"/>
    <nd ref="1099120555"/>
    <nd ref="1099120556"/>
    <nd ref="61420229"/>
    <nd ref="61420222"/>
    <nd ref="61420249"/>
    <nd ref="61420207"/>
    <nd ref="61420214"/>
    <nd ref="643774918"/>
    <tag k="name" v="North Washington Street"/>
    <tag k="width" v="30.2"/>
    <tag k="oneway" v="yes"/>
    <tag k="source" v="massgis_import_v0.1_20071008193615"/>
    <tag k="highway" v="primary"/>
    <tag k="condition" v="fair"/>
    <tag k="attribution" v="Office of Geographic and Environmental Information (MassGIS)"/>
    <tag k="massgis:way_id" v="134349"/>
  </way>
>>> g[61448456][1102764005]
{'attribution': 'Office of Geographic and Environmental Information (MassGIS)',
 'condition': 'fair',
 'highway': 'primary',
 'massgis:way_id': '134349',
 'name': 'North Washington Street',
 'oneway': 'yes',
 'source': 'massgis_import_v0.1_20071008193615',
 'width': '30.2'}

Ways that are not oneway roads will have edges in both directions.

Notes

osmgraph loads the entire graph in memory. You should be careful how much data is being loaded. All parsing functions accept a ways_tag_filter and nodes_tag_filter arguments. These are functions that accept a dictionary of node or way tags. They should manipulate the dictionary in place to drop unused tags.

For example, if we only care about nodes containing a traffic light.

def traffic_lights_filter(tags):
    if tags.get('highway') != 'traffic_signals':
        tags.clear()

g = osmgraph.parse_file(filename, nodes_tag_filter=traffic_lights_filter)

Example: Build a Cheapo Router

Parse some OSM data, add a length property to each edge using geog, use networkx's builtin shortest path algorithm to find the shortest path between two nodes, use geojsonio.py to show the line on geojson.io

import geog
import networkx as nx
import osmgraph

# By default any way with a highway tag will be loaded
g = osmgraph.parse_file('boston_massachusetts.osm.bz2')  # or .osm or .pbf
for n1, n2 in g.edges_iter():
    c1, c2 = osmgraph.tools.coordinates(g, (n1, n2))   
    g[n1][n2]['length'] = geog.distance(c1, c2)


import random
start = random.choice(g.nodes())
end = random.choice(g.nodes())
path = nx.shortest_path(g, start, end, 'length')
coords = osmgraph.tools.coordinates(g, path)

# Find the sequence of roads to get from start to end
edge_names = [g[n1][n2].get('name') for n1, n2 in osmgraph.tools.pairwise(path)]
import itertools
names = [k for k, v in itertools.groupby(edge_names)]
print(names)
     ['North Harvard Street',
      'Franklin Street',
      'Lincoln Street',
      None,
      'Cambridge Street',
      'Gordon Street',
      'Warren Street',
      'Commonwealth Avenue']

# Visualize the path using geojsonio.py
import geojsonio
import json
geojsonio.display(json.dumps({'type': 'LineString', 'coordinates': coords}))

Route Line

See Also

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