Get OSM Features Within a Distance of Address Using OSMnx Feature Module

OpenStreetMap provides physical features on the ground (e.g ., roads, buildings, etc.) using tags attached to its basic data structure (nodes, ways, and relations). Each tag describes a geographic attribute of the feature for the specific node, way, or relation. In this article, we will see how we can get OSM features within a distance of address using the OSMnx feature module in Python.

Syntax of osmnx.features.features_from_address() Function

The function creates a GeoDataFrame of OSM features within some distance of the address N, S, E, W. Below is the syntax:

osmnx.features.features_from_address(address, tags, dist=1000)

Parameters:

• address (string): Address to geocode and use as the central point to get the features
• tags (dict): Dict of tags used for finding elements in the selected area. The results returned are the union of each tag. The dict keys should be OSM tags, (e.g., building, landuse, highway, etc) and the dict values should be either True to retrieve all items with the given tag, or a string to get a single tag-value combination, or a list of strings to get multiple values for the given tag.
• dist (numeric): distance in meters

Returns: gdf

Return Type: geopandas.GeoDataFrame

OSM Features Within a Distance of Address Using OSMnx Feature Module

Below are some approaches by which we can find OSM features within a distance of address using OSMnx feature module in Python:

Find the Building Details From the Open Street Map

In this step, we are finding the building details within 1000 m of a place. In the below code, we set the ‘building’ tag to true to list the available buildings within 1000m of SoHo, NewYork

Python3

import osmnx as ox

# get building details from address within 1000 m
place = "SoHo, New York, NY"
gdf = ox.features.features_from_address(
    place, {'building': True}, dist=1000)

# print first 5 building details
gdf.head(5)

Output

building details

We can list the available columns in our geodataframe using info() method.

Python3

gdf.info()

Output

<class 'geopandas.geodataframe.GeoDataFrame'>
MultiIndex: 7268 entries, ('node', 4710026885) to ('relation', 1651490)
Data columns (total 99 columns):
 #   Column                   Non-Null Count  Dtype   
---  ------                   --------------  -----   
 0   alt_name:en              1 non-null      object  
 1   alt_name:ml              2 non-null      object  
 2   name                     308 non-null    object  
 3   name:ml                  19 non-null     object  
 4   geometry                 7268 non-null   geometry
 5   building                 7268 non-null   object  
 6   addr:district            2 non-null      object  
 7   addr:place               1 non-null      object  
 8   addr:state               1 non-null      object  
 9   amenity                  101 non-null    object  
 10  denomination             2 non-null      object  
 11  female                   1 non-null      object  
 12  opening_hours            12 non-null     object  
 13  religion                 31 non-null     object  
 14  service_times            2 non-null      object  
 15  wheelchair               5 non-null      object  
 16  name:en                  6 non-null      object  
 17  survey:date              1 non-null      object  
 18  tourism                  27 non-null     object  
 19  addr:city                148 non-null    object  
 20  nodes                    7258 non-null   object  
 21  covered                  1 non-null      object  
 22  shelter_type             1 non-null      object  
 23  wikidata                 3 non-null      object  
 24  building:levels          81 non-null     object  
 25  Administration           1 non-null      object  
 26  layer                    20 non-null     object  
 27  level                    1 non-null      object  
 28  operator                 8 non-null      object  
 29  building:part            2 non-null      object  
 30  name:mr                  1 non-null      object  
 31  castle_type              6 non-null      object  
 32  historic                 6 non-null      object  
 33  leisure                  1 non-null      object  
 34  shop                     83 non-null     object  
 35  source                   6035 non-null   object  
 36  subdenomination          1 non-null      object  
 37  payment:cash             6 non-null      object  
 38  payment:credit_cards     3 non-null      object  
 39  addr:postcode            51 non-null     object  
 40  addr:street              145 non-null    object  
 41  architect                1 non-null      object  
 42  architect:wikidata       1 non-null      object  
 43  architect:wikipedia      1 non-null      object  
 44  brand                    26 non-null     object  
 45  brand:wikidata           26 non-null     object  
 46  brand:wikipedia          22 non-null     object  
 47  cuisine                  7 non-null      object  
 48  outdoor_seating          1 non-null      object  
 49  smoking                  2 non-null      object  
 50  addr:housenumber         5 non-null      object  
 51  office                   35 non-null     object  
 52  int_name                 1 non-null      object  
 53  railway                  1 non-null      object  
 54  height                   64 non-null     object  
 55  internet_access          8 non-null      object  
 56  internet_access:fee      3 non-null      object  
 57  phone                    8 non-null      object  
 58  stars                    2 non-null      object  
 59  website                  6 non-null      object  
 60  area                     2 non-null      object  
 61  landuse                  1 non-null      object  
 62  operator:wikidata        1 non-null      object  
 63  atm                      4 non-null      object  
 64  drive_through            2 non-null      object  
 65  short_name               6 non-null      object  
 66  screen                   3 non-null      object  
 67  air_conditioning         2 non-null      object  
 68  payment:debit_cards      1 non-null      object  
 69  branch                   3 non-null      object  
 70  healthcare               2 non-null      object  
 71  man_made                 1 non-null      object  
 72  fee                      2 non-null      object  
 73  museum                   2 non-null      object  
 74  name:etymology:wikidata  2 non-null      object  
 75  check_date               1 non-null      object  
 76  building:material        4 non-null      object  
 77  description              2 non-null      object  
 78  opening_hours:covid19    1 non-null      object  
 79  rooms                    2 non-null      object  
 80  brand:ks                 1 non-null      object  
 81  brand:pa                 1 non-null      object  
 82  brand:ur                 1 non-null      object  
 83  name:ks                  1 non-null      object  
 84  name:pa                  1 non-null      object  
 85  name:ur                  1 non-null      object  
 86  takeaway                 1 non-null      object  
 87  alt_name                 1 non-null      object  
 88  wikipedia                1 non-null      object  
 89  government               2 non-null      object  
 90  guest_house              1 non-null      object  
 91  craft                    2 non-null      object  
 92  clothes                  1 non-null      object  
 93  healthcare:speciality    1 non-null      object  
 94  operator:type            1 non-null      object  
 95  ref                      1 non-null      object  
 96  repair                   1 non-null      object  
 97  ways                     1 non-null      object  
 98  type                     1 non-null      object  
dtypes: geometry(1), object(98)
memory usage: 5.8+ MB

Plotting the OSM Features Using plot_footprints() Method

In this step, we will plot the OSM features using plot_footprints() method by passing the geodataframe inside it.

Python3

fig, ax = ox.plot_footprints(gdf, figsize=(3, 3))

Output

building plot

We can plot it on a map by using the explore() method from geodataframe.

Python3

gdf.explore()

Output

building feature on map

Find the Emergency Support System Details from the Open Street Map

We can find the emergency support system details like hospitals, ambulances, etc. by setting the ’emergency’ tag as true and OSMnx will lists all the available emergency support system.

Python3

import osmnx as ox

place = "Thiruvananthapuram, Kerala"
gdf = ox.features.features_from_address(
    place, {'emergency': True}, dist=3000)
print(gdf)

Output

emergency as tag

Get Multiple Map Features from Open Street Map

Let’s try multiple tags. We can try the below tag

tags = {‘amenity’:True,

‘landuse’:[‘retail’,’commercial’],

‘highway’:’bus_stop’}

In the below code, it lists the entire amenity available since we set ‘amenity’ tag as true; In case of ‘landuse’ tag, OSMnx fetches details based on ‘retail’ and ‘commercial’ subtags. Similary the ‘bus_stop’ subtag details from ‘highway’ tag.

Python3

import osmnx as ox

tags = {'amenity': True,
        'landuse': ['retail', 'commercial'],
        'highway': 'bus_stop'}
# fetch multiple features
gdf = ox.features.features_from_address(
    "Thiruvananthapuram, Kerala", tags, dist=500)
# display it on map
gdf.explore()

Output

Multiple Features