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In this post we are simply going to retrieve the restaurants from the city of Lyon-France from Open Street Map, and then plot them with Bokeh.

Downloading the restaurants name and coordinates is done using a fork of the great OSMnx library. The OSM-POI feature of this fork will probably soon be added to OSMnx from what I understand (issue).

First we create a fresh conda env, install jupyterlab, bokeh (the following lines show the Linux way to do it but a similar thing could be done with Windows):

$ conda create -n restaurants python=3.6
$ source activate restaurants
$ conda install jupyterlab
$ conda install -c bokeh bokeh
$ jupyter labextension install jupyterlab_bokeh
$ jupyter lab osm_restaurants.ipynb

The jupyterlab extension allows the rendering of JS Bokeh content.

Then we need to install the POI fork of OSMnx:

$ git clone Cette adresse e-mail est protégée contre les robots spammeurs. Vous devez activer le JavaScript pour la visualiser.:HTenkanen/osmnx.git
$ cd osmnx/
osmnx $  git checkout 1-osm-poi-dev
osmnx $  pip install .
osmnx $  cd ..

And we are ready to run the notebook:

jupyter lab osm_restaurants.ipynb

Download OSM restaurants

In [1]:
import osmnx as ox
place = "Lyon, France"
restaurant_amenities = ['restaurant', 'cafe', 'fast_food']
restaurants = ox.pois_from_place(place=place, 

We are looking for 3 kinds of amenity related to food: restaurants, cafés and fast-foods. The collected data is returned as a geodataframe, which is basically a Pandas dataframe associated with a geoserie of Shapely geometries. Along with the geometry, we are only keeping 4 columns:

  • restaurant name,
  • amenity type (restaurant, café or fast_food),
  • cuisine type and
  • element_type (OSM types: node, way relation).
In [2]:
25733699 POINT (4.8634608 45.7439964) Le Petit Comptoir restaurant international node
25733700 POINT (4.8689407 45.7410332) L'Esprit Bistro restaurant NaN node
26641424 POINT (4.8346121 45.7569848) Comptoir des Marronniers restaurant NaN node
33065934 POINT (4.7732746 45.7393443) Auberge de la Vallée restaurant NaN node
35694312 POINT (4.8342288 45.7581985) McDonald's fast_food burger node
In [3]:
ax = restaurants.plot()

Update POI's geometry type

A large majority of the restaurants corresponds to OSM nodes that are translated to shapely Points:

In [4]:
node        1609
way           43
relation       2
Name: element_type, dtype: int64
In [5]:
25733699    <class 'shapely.geometry.point.Point'>
25733700    <class 'shapely.geometry.point.Point'>
26641424    <class 'shapely.geometry.point.Point'>
33065934    <class 'shapely.geometry.point.Point'>
35694312    <class 'shapely.geometry.point.Point'>
Name: geometry, dtype: object

However, OSM ways appear as shapely polygons:

In [6]:
In [7]:
37801104    <class 'shapely.geometry.polygon.Polygon'>
40555792    <class 'shapely.geometry.polygon.Polygon'>
40555793    <class 'shapely.geometry.polygon.Polygon'>
43013914    <class 'shapely.geometry.polygon.Polygon'>
44505425    <class 'shapely.geometry.polygon.Polygon'>
Name: geometry, dtype: object

While OSM relation appears as shapely multipolygons:

In [8]:
In [9]:
1708967    <class 'shapely.geometry.multipolygon.MultiPol...
2655433    <class 'shapely.geometry.multipolygon.MultiPol...
Name: geometry, dtype: object

Using the shapely centroid method, we are going to get a point geometry for all polygon or multipolygon entries:

In [10]:
from shapely.geometry import Polygon, MultiPolygon
for item in ['way', 'relation']:
    restaurants.loc[restaurants.element_type==item, 'geometry'] = \
        restaurants[restaurants.element_type==item]['geometry'].map(lambda x: x.centroid)
In [11]:
restaurants['geometry_type'] =
<class 'shapely.geometry.point.Point'>    1654
Name: geometry_type, dtype: int64

 Plot the POIs with Bokeh

All restaurants may not have a name. Since we are going to plot all restaurants along with their name, we replace missing names by empty strings (we actually do the same for the cuisine type):

In [12]:'', inplace=True)
restaurants.cuisine.fillna('?', inplace=True)

Also, we reproject the geometric data to WGS 84 / Pseudo-Mercator in order to be compatible with tile providers:

In [13]:
restaurants = restaurants.to_crs(epsg=3857)

And we are now ready to plot the restaurants on a map.

In [14]:
from import output_notebook
from bokeh.plotting import figure, show 
from bokeh.models import (
from bokeh.tile_providers import CARTODBPOSITRON_RETINA 

source = ColumnDataSource(data=dict(

TOOLS = "pan,wheel_zoom,hover,reset"
p = figure(title="Lyon OSM restaurants", tools=TOOLS, 
           match_aspect=True, x_axis_location=None, y_axis_location=None, 
p.grid.grid_line_color = None
p.add_tile(CARTODBPOSITRON_RETINA)'x', y='y', source=source, fill_alpha=0.6, size=6)

hover = p.select_one(HoverTool)
hover.point_policy = "snap_to_data"
hover.tooltips = [
    ("Name", "@name"),
    ("Cuisine", "@cuisine"),

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