Geocoding & Table Join


*Following materials are partly based on documentation of Geopandas, geopy and Pandas

Overview of Geocoders

Geocoding, i.e. converting addresses into coordinates or vice versa, is a really common GIS task. Luckily, in Python there are nice libraries that makes the geocoding really easy. One of the libraries that can do the geocoding for us is geopy that makes it easy to locate the coordinates of addresses, cities, countries, and landmarks across the globe using third-party geocoders and other data sources.

As said, Geopy uses third-party geocoders - i.e. services that does the geocoding - to locate the addresses and it works with multiple different service providers such as:

Thus, there is plenty of geocoders where to choose from! However, to be able to use these services you might need to request so called API access-keys from the service provider to be able to use the service. You can get your access keys to e.g. Google Geocoding API from Google APIs console by creating a Project and enabling a that API from Library. Read a short introduction about using Google API Console from here.

There are also other Python modules in addition to geopy that can do geocoding such as Geocoder.

Geocoding in Geopandas

It is also 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 a text file called addresses.txt that contains few addresses around Helsinki Region. The first rows of the data looks like following:

1000;Itämerenkatu 14, 00101 Helsinki, Finland
1001;Kampinkuja 1, 00100 Helsinki, Finland
1002;Kaivokatu 8, 00101 Helsinki, Finland
1003;Hermanstads strandsväg 1, 00580 Helsingfors, Finland

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

In [24]:
# Import necessary modules
import pandas as pd
import geopandas as gpd
from shapely.geometry import Point

# Import the geocoding function
from import geocode

# Filepath
fp = r"/home/geo/addresses.txt"
fp = r"C:\HY-Data\HENTENKA\KOODIT\Opetus\Automating-GIS-processes\AutoGIS-Sphinx\source\data\addresses.txt"

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

# Let's take a look of the data

     id                                            address
0  1000           Itämerenkatu 14, 00101 Helsinki, Finland
1  1001              Kampinkuja 1, 00100 Helsinki, Finland
2  1002               Kaivokatu 8, 00101 Helsinki, Finland
3  1003  Hermanstads strandsväg 1, 00580 Helsingfors, F...
4  1004                  Itäväylä, 00900 Helsinki, Finland
  • Now we have our data in a Pandas DataFrame and we can geocode our addresses

    • Notice: here we will be using my API key that has a limitation of 2500 requests / hour. Because of this, only the computer instances of our course environment have access to Google Geocoding API for a short period of time. Thus, the following key will NOT work from your own computer, only from our cloud computers. If you wish, you can create your own API key to Google Geocoding API V3 from Google APIs console. See the notes from above

In [25]:
from import geocode

# Key for our Google Geocoding API 
# Notice: only the cloud computers of our course can access and successfully execute the following
key = 'AIzaSyAwNVHAtkbKlPs-EEs3OYqbnxzaYfDF2_8'

# Geocode addresses
geo = geocode(data['address'], api_key=key)


                                    address                       geometry
0  Itämerenkatu 14, 00180 Helsinki, Finland  POINT (24.9146767 60.1628658)
1     Kampinkuja 1, 00100 Helsinki, Finland  POINT (24.9301701 60.1683731)

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 again something that you need to really frequently when doing GIS analyses. Combining data from different tables based on common key attribute can be done easily in Pandas/Geopandas using .merge() -function.

  • Let's join the data and geo DataFrames together based on common column address. Parameter on is used to determine the common key in the tables. If your key in the first table would be named differently than in the other one, you can also specify them separately for each table by using left_on and right_on -parameters.

In [22]:
# Join tables by using a key column 'address'
join = geo.merge(data, on='address')

# Let's see what we have

# Let's also check the data type

                                             address  \
0              Kampinkuja 1, 00100 Helsinki, Finland   
1               Kaivokatu 8, 00101 Helsinki, Finland   
2  Hermanstads strandsväg 1, 00580 Helsingfors, F...   
3                  Itäväylä, 00900 Helsinki, Finland   
4         Tyynenmerenkatu 9, 00220 Helsinki, Finland   

                               geometry    id  
0         POINT (24.9301701 60.1683731)  1001  
1         POINT (24.9418933 60.1698665)  1002  
2  POINT (24.9774004 60.18735880000001)  1003  
3  POINT (25.0919641 60.21448089999999)  1004  
4         POINT (24.9214846 60.1565781)  1005  

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

In [26]:
# Output file path
outfp = r"/home/geo/addresses.shp"
outfp = r"C:\HY-Data\HENTENKA\KOODIT\Opetus\Automating-GIS-processes\AutoGIS-Sphinx\source\data\addresses.shp"

# Save to Shapefile

That's it. Now we have successfully geocoded those addresses into Points and made a Shapefile out of them.

Task: Make a map out of the points. What do you think that the addresses are representing?