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Geocoding in Geopandas

It is possible to do geocoding in Geopandas using its integrated functionalities of geopy. Geopandas has a function called geocode() that can geocode a list of addresses (strings) and return a GeoDataFrame containing the resulting point objects in geometry column. Nice, isn’t it! Let’s try this out.

Download data

For the lesson three download data package from here.

The package contains a text file called addresses.txt which has a few addresses around Helsinki Region. The first rows of the data looks like following:

id;addr
1000;Itämerenkatu 14, 00101 Helsinki, Finland
1001;Kampinkuja 1, 00100 Helsinki, Finland
1002;Kaivokatu 8, 00101 Helsinki, Finland
1003;Hermannin rantatie 1, 00580 Helsinki, Finland

We have an id for each row and an address on column addr.

• Let’s first read the data into a Pandas DataFrame using the read_csv() -function:

# Import necessary modules
import pandas as pd
import geopandas as gpd
from shapely.geometry import Point

# Filepath
fp = "L3_data/addresses.txt"

# Read the data
data = pd.read_csv(fp, sep=';')

# Let's take a look of the data
data.head()

	id	addr
0	1000	Itämerenkatu 14, 00101 Helsinki, Finland
1	1001	Kampinkuja 1, 00100 Helsinki, Finland
2	1002	Kaivokatu 8, 00101 Helsinki, Finland
3	1003	Hermannin rantatie 1, 00580 Helsinki, Finland
4	1005	Tyynenmerenkatu 9, 00220 Helsinki, Finland

Now we have our data in a Pandas DataFrame and we can geocode our addresses.

# Import the geocoding tool and geopy
from geopandas.tools import geocode

# Geocode addresses with Nominatim backend
geo = geocode(data['addr'], provider='nominatim', user_agent='csc_user_ht')
geo.head(2)

	geometry	address
0	POINT (24.9155624 60.1632015)	Ruoholahti, 14, Itämerenkatu, Ruoholahti, Läns...
1	POINT (24.9316914 60.1690222)	Kamppi, 1, Kampinkuja, Kamppi, Eteläinen suurp...

And Voilà! As a result we have a GeoDataFrame that contains our original address and a ‘geometry’ column containing Shapely Point -objects that we can use for exporting the addresses to a Shapefile for example. However, the id column is not there. Thus, we need to join the information from data into our new GeoDataFrame geo, thus making a Table Join.

Table join

Table joins are really common procedures when doing GIS analyses. As you might remember from our earlier lessons, combining data from different tables based on common key attribute can be done easily in Pandas/Geopandas using .merge() -function.

However, sometimes it is useful to join two tables together based on the index of those DataFrames. In such case, we assume that there is same number of records in our DataFrames and that the order of the records should be the same in both DataFrames. In fact, now we have such a situation as we are geocoding our addresses where the order of the geocoded addresses in geo DataFrame is the same as in our original data DataFrame.

Hence, we can join those tables together with join() -function which merges the two DataFrames together based on index by default.

join = geo.join(data)
join.head()

	geometry	address	id	addr
0	POINT (24.9155624 60.1632015)	Ruoholahti, 14, Itämerenkatu, Ruoholahti, Läns...	1000	Itämerenkatu 14, 00101 Helsinki, Finland
1	POINT (24.9316914 60.1690222)	Kamppi, 1, Kampinkuja, Kamppi, Eteläinen suurp...	1001	Kampinkuja 1, 00100 Helsinki, Finland
2	POINT (24.9416849 60.1699637)	Bangkok9, 8, Kaivokatu, Keskusta, Kluuvi, Etel...	1002	Kaivokatu 8, 00101 Helsinki, Finland
3	POINT (24.9655355 60.2008878)	1, Hermannin rantatie, Hermanninmäki, Hermanni...	1003	Hermannin rantatie 1, 00580 Helsinki, Finland
4	POINT (24.9216003 60.1566475)	Hesburger, 9, Tyynenmerenkatu, Jätkäsaari, Län...	1005	Tyynenmerenkatu 9, 00220 Helsinki, Finland

• Let’s also check the data type of our new join table.

type(join)

geopandas.geodataframe.GeoDataFrame

As a result we have a new GeoDataFrame called join where we now have all original columns plus a new column for geometry.

• Now it is easy to save our address points into a Shapefile

# Output file path
outfp = "L3_data/addresses.shp"

# Save to Shapefile
join.to_file(outfp)

That’s it. Now we have successfully geocoded those addresses into Points and made a Shapefile out of them. Easy isn’t it!

Notes about Nominatim

Nominatim works relatively nicely if you have well defined and well-known addresses such as the ones that we used in this tutorial. However, in some cases, you might not have such well-defined addresses, and you might have e.g. only the name of a museum available. In such cases, Nominatim might not provide such good results, and in such cases you might want to use e.g. Google Geocoding API (V3). Take a look from past year’s materials where we show how to use Google Geocoding API in a similar manner as we used Nominatim here.