15 openstreetmap

Downloading OpenStreetMap data with a single line of code

Uncomment the following line to install leafmap if needed.

In [1]:

# !pip install geopandas osmnx leafmap
# !pip install geopandas osmnx leafmap

In [2]:

import leafmap
import leafmap

Add OSM data of place(s) by name or ID to the map. Note that the leafmap custom layer control does not support GeoJSON, we need to use the ipyleaflet built-in layer control.

In [3]:

m = leafmap.Map(toolbar_control=False, layers_control=True)
m.add_osm_from_geocode("New York City", layer_name='NYC')
m
m = leafmap.Map(toolbar_control=False, layers_control=True)
m.add_osm_from_geocode("New York City", layer_name='NYC')
m

/opt/hostedtoolcache/Python/3.9.10/x64/lib/python3.9/site-packages/osmnx/geocoder.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  gdf = gdf.append(_geocode_query_to_gdf(q, wr, by_osmid))

Out[3]:

Make this Notebook Trusted to load map: File -> Trust Notebook

In [4]:

m = leafmap.Map(toolbar_control=False, layers_control=True)
m.add_osm_from_geocode("Chicago, Illinois", layer_name='Chicago, IL')
m
m = leafmap.Map(toolbar_control=False, layers_control=True)
m.add_osm_from_geocode("Chicago, Illinois", layer_name='Chicago, IL')
m

/opt/hostedtoolcache/Python/3.9.10/x64/lib/python3.9/site-packages/osmnx/geocoder.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  gdf = gdf.append(_geocode_query_to_gdf(q, wr, by_osmid))

Out[4]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Add OSM entities within boundaries of geocodable place(s) to the map.

In [5]:

m = leafmap.Map(toolbar_control=False, layers_control=True)
place = "Bunker Hill, Los Angeles, California"
tags = {"building": True}
m.add_osm_from_place(place, tags, layer_name="Los Angeles, CA")
m
m = leafmap.Map(toolbar_control=False, layers_control=True)
place = "Bunker Hill, Los Angeles, California"
tags = {"building": True}
m.add_osm_from_place(place, tags, layer_name="Los Angeles, CA")
m

2022-01-27 22:04:48 Configured OSMnx 1.1.2
2022-01-27 22:04:48 HTTP response caching is on
2022-01-27 22:04:48 Retrieved response from cache file "cache/8d8366ad6b96fa854a9d6d038c022b8bdcdb6fe6.json"
2022-01-27 22:04:48 Created GeoDataFrame with 1 rows from 1 queries
2022-01-27 22:04:48 Constructed place geometry polygon(s) to query API
2022-01-27 22:04:48 Projected GeoDataFrame to +proj=utm +zone=11 +ellps=WGS84 +datum=WGS84 +units=m +no_defs +type=crs
2022-01-27 22:04:48 Projected GeoDataFrame to epsg:4326
2022-01-27 22:04:48 Requesting data within polygon from API in 1 request(s)
2022-01-27 22:04:48 Retrieved response from cache file "cache/3063abeb36c57d92b80fe38fd9a33fcf0e02c542.json"
2022-01-27 22:04:48 Got all geometries data within polygon from API in 1 request(s)
2022-01-27 22:04:48 Converting 1430 elements in JSON responses to geometries
2022-01-27 22:04:48 77 geometries created in the dict
2022-01-27 22:04:48 13 untagged geometries removed
2022-01-27 22:04:49 Created r-tree spatial index for 64 geometries
2022-01-27 22:04:49 Identified 64 geometries inside polygon
2022-01-27 22:04:49 0 geometries removed by the polygon filter
2022-01-27 22:04:49 8 geometries removed by the tag filter
2022-01-27 22:04:49 56 geometries in the final GeoDataFrame

/opt/hostedtoolcache/Python/3.9.10/x64/lib/python3.9/site-packages/osmnx/geocoder.py:110: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  gdf = gdf.append(_geocode_query_to_gdf(q, wr, by_osmid))

Out[5]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Show OSM feature tags.

https://wiki.openstreetmap.org/wiki/Map_features

In [6]:

# leafmap.osm_tags_list()
# leafmap.osm_tags_list()

Add OSM entities within some distance N, S, E, W of address to the map.

In [7]:

m = leafmap.Map(toolbar_control=False, layers_control=True)
m.add_osm_from_address(
    address="New York City", 
    tags={"amenity": "bar"}, 
    dist=1500,
    layer_name="NYC bars")
m
m = leafmap.Map(toolbar_control=False, layers_control=True)
m.add_osm_from_address(
    address="New York City", 
    tags={"amenity": "bar"}, 
    dist=1500,
    layer_name="NYC bars")
m

2022-01-27 22:04:49 Pausing 1 seconds before making HTTP GET request
2022-01-27 22:04:50 Get https://nominatim.openstreetmap.org/search?format=json&limit=1&dedupe=0&q=New+York+City with timeout=180
2022-01-27 22:04:50 Resolved nominatim.openstreetmap.org to 140.211.167.100
2022-01-27 22:04:50 Downloaded 0.5kB from nominatim.openstreetmap.org
2022-01-27 22:04:50 Saved response to cache file "cache/78258c318356c20c380e1fed844176f90c053cf7.json"
2022-01-27 22:04:50 Geocoded "New York City" to (40.7127281, -74.0060152)
2022-01-27 22:04:50 Created bbox 1500 m from (40.7127281, -74.0060152): 40.72621790503239,40.69923829496761,-73.98821837587228,-74.02381202412771
2022-01-27 22:04:50 Projected GeoDataFrame to +proj=utm +zone=18 +ellps=WGS84 +datum=WGS84 +units=m +no_defs +type=crs
2022-01-27 22:04:50 Projected GeoDataFrame to epsg:4326
2022-01-27 22:04:50 Requesting data within polygon from API in 1 request(s)
2022-01-27 22:04:50 Resolved overpass-api.de to 178.63.48.217
2022-01-27 22:04:50 Pausing 0 seconds before making HTTP POST request
2022-01-27 22:04:50 Post https://overpass-api.de/api/interpreter?data=%5Bout%3Ajson%5D%5Btimeout%3A180%5D%3B%28%28node%5B%27amenity%27%3D%27bar%27%5D%28poly%3A%2740.699238+-74.023812+40.699238+-73.988218+40.726218+-73.988218+40.726218+-74.023812+40.699238+-74.023812%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%28way%5B%27amenity%27%3D%27bar%27%5D%28poly%3A%2740.699238+-74.023812+40.699238+-73.988218+40.726218+-73.988218+40.726218+-74.023812+40.699238+-74.023812%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%28relation%5B%27amenity%27%3D%27bar%27%5D%28poly%3A%2740.699238+-74.023812+40.699238+-73.988218+40.726218+-73.988218+40.726218+-74.023812+40.699238+-74.023812%27%29%3B%28._%3B%3E%3B%29%3B%29%3B%29%3Bout%3B with timeout=180
2022-01-27 22:04:50 Resolved overpass-api.de to 178.63.48.217
2022-01-27 22:04:53 Downloaded 37.2kB from overpass-api.de
2022-01-27 22:04:53 Saved response to cache file "cache/7d10c2e8d50f39407dda671d56290ea2808b3d4a.json"
2022-01-27 22:04:53 Got all geometries data within polygon from API in 1 request(s)
2022-01-27 22:04:53 Converting 107 elements in JSON responses to geometries
2022-01-27 22:04:53 98 geometries created in the dict
2022-01-27 22:04:53 0 untagged geometries removed
2022-01-27 22:04:53 Created r-tree spatial index for 98 geometries
2022-01-27 22:04:53 Identified 98 geometries inside polygon
2022-01-27 22:04:53 0 geometries removed by the polygon filter
2022-01-27 22:04:53 0 geometries removed by the tag filter
2022-01-27 22:04:53 98 geometries in the final GeoDataFrame

Out[7]:

Make this Notebook Trusted to load map: File -> Trust Notebook