From text to map

Creating geometries from text

Creating new geometries from scratch

Since geopandas takes advantage of Shapely geometric objects, it is possible to create spatial data from scratch by passing Shapely’s geometric objects into the GeoDataFrame. This is useful as it makes it easy to convert e.g. a text file that contains coordinates into spatial data layers. Next we will see how to create a new GeoDataFrame from scratch and save it into a file. Our goal is to define a geometry that represents the outlines of the Senate square in Helsinki, Finland.

Let’s start by creating a new empty GeoDataFrame object.

import geopandas as gpd
newdata = gpd.GeoDataFrame()
type(newdata)
geopandas.geodataframe.GeoDataFrame

We have an empty GeoDataFrame! A geodataframe is basically a pandas DataFrame that should have one column dedicated for geometries. By default, the geometry-column should be named geometry (geopandas looks for geometries from this column).

Let’s create the geometry column:

# Create a new column called 'geometry' to the GeoDataFrame
newdata["geometry"] = None
print(newdata)
Empty GeoDataFrame
Columns: [geometry]
Index: []

Now we have a geometry column in our GeoDataFrame but we still don’t have any data.

Let’s create a Shapely Polygon repsenting the Helsinki Senate square that we can later insert to our GeoDataFrame:

from shapely.geometry import Polygon
# Coordinates of the Helsinki Senate square in decimal degrees
coordinates = [
    (24.950899, 60.169158),
    (24.953492, 60.169158),
    (24.953510, 60.170104),
    (24.950958, 60.169990),
]
# Create a Shapely polygon from the coordinate-tuple list
poly = Polygon(coordinates)
# Check the polyogon
print(poly)
POLYGON ((24.950899 60.169158, 24.953492 60.169158, 24.95351 60.170104, 24.950958 60.16999, 24.950899 60.169158))

Okay, now we have an appropriate Polygon -object.

Let’s insert the polygon into our ‘geometry’ column of our GeoDataFrame on the first row:

# Insert the polygon into 'geometry' -column at row 0
newdata.at[0, "geometry"] = poly
# Let's see what we have now
print(newdata)
                                            geometry
0  POLYGON ((24.95090 60.16916, 24.95349 60.16916...

Great, now we have a GeoDataFrame with a Polygon that we could already now export to a Shapefile. However, typically you might want to include some attribute information with the geometry.

Let’s add another column to our GeoDataFrame called location with text Senaatintori that describes the location of the feature.

# Add a new column and insert data
newdata.at[0, "location"] = "Senaatintori"

# Let's check the data
print(newdata)
                                            geometry      location
0  POLYGON ((24.95090 60.16916, 24.95349 60.16916...  Senaatintori

Okay, now we have additional information that is useful for recognicing what the feature represents.

The next step would be to determine the coordinate reference system (projection) for the GeoDataFrame. GeoDataFrame has an attribute called .crs that shows the coordinate system of the data. In our case, the layer doesn’t yet have any crs definition:

print(newdata.crs)
None

We passed the coordinates as latitude and longitude decimal degrees, so the correct CRS is WGS84 (epsg code: 4326). In this case, we can simply re-build the geodataframe and pass the correct crs information to the GeoDataFrame constructor. You will learn more about how to handle coordinate reference systems using pyproj CRS objects later in this chapter.

Re-create the GeoDataFrame with correct crs definition:

newdata = gpd.GeoDataFrame(newdata, crs=4326)
C:\Users\vuokkhei\AppData\Local\Temp/ipykernel_11788/2392668392.py:1: FutureWarning: CRS mismatch between CRS of the passed geometries and 'crs'. Use 'GeoDataFrame.set_crs(crs, allow_override=True)' to overwrite CRS or 'GeoDataFrame.to_crs(crs)' to reproject geometries. CRS mismatch will raise an error in the future versions of GeoPandas.
  newdata = gpd.GeoDataFrame(newdata, crs=4326)
newdata.crs.name
'WGS 84'

As we can see, now we have added coordinate reference system information into our GeoDataFrame. The CRS information is necessary for creating a valid projection information for the output file.

Finally, we can export the GeoDataFrame using .to_file() -function. The function works quite similarly as the export functions in pandas, but here we only need to provide the output path for the Shapefile. Easy isn’t it!:

# Determine the output path for the Shapefile
outfp = "L2_data/Senaatintori.shp"

# Write the data into that Shapefile
newdata.to_file(outfp)

Now we have successfully created a Shapefile from scratch using geopandas. Similar approach can be used to for example to read coordinates from a text file (e.g. points) and turn that information into a spatial layer.

Check your understanding

Check the output Shapefile by reading it with geopandas and make sure that the attribute table and geometry seems correct.

Re-project the data to ETRS-TM35FIN (EPSG:3067) and save into a new file!

Geocoding

Geocoding is the process of transforming place names or addresses into coordinates. In this lesson we will learn how to geocode addresses using Geopandas and geopy.

Geopy and other geocoding libaries (such as geocoder) make it easy to locate the coordinates of addresses, cities, countries, and landmarks across the globe using web services (“geocoders”). In practice, geocoders are often Application Programming Interfaces (APIs) where you can send requests, and receive responses in the form of place names, addresses and coordinates.

Geopy offers access to several geocoding services. Check the geopy documentation for more details about how to use each service via Python.

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.

Geocoding addresses

Let’s try this out.

We will geocode addresses stored in a text file called addresses.txt. These addresses are located in the Helsinki Region in Southern Finland.

The first rows of the data look like this:

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 = r"data/addresses.txt"

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

Let’s check that we imported the file correctly:

len(data)
data.head()

Now we have our data in a pandas DataFrame and we can geocode our addresses using the geopandas geocoding function that uses geopy package in the background.

  • Let’s import the geocoding function and geocode the addresses (column addr) using Nominatim.

  • Remember to provide a custom string (name of your application) in the user_agent parameter.

  • If needed, you can add the timeout-parameter which specifies how many seconds we will wait for a response from the service.

# Import the geocoding tool
from geopandas.tools import geocode

# Geocode addresses using Nominatim. Remember to provide a custom "application name" in the user_agent parameter!
geo = geocode(data["addr"], provider="nominatim", user_agent="autogis_xx", timeout=4)
geo.head()

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.

Rate-limiting

When geocoding a large dataframe, you might encounter an error when geocoding. In case you get a time out error, try first using the timeout parameter as we did above (allow the service a bit more time to respond). In case of Too Many Requests error, you have hit the rate-limit of the service, and you should slow down your requests. To our convenience, geopy provides additional tools for taking into account rate limits in geocoding services. This script adapts the usage of geopy RateLimiter to our input data:

from geopy.geocoders import Nominatim
from geopy.extra.rate_limiter import RateLimiter
from shapely.geometry import Point

# Initiate geocoder
geolocator = Nominatim(user_agent='autogis_xx')

# Create a geopy rate limiter:
geocode_with_delay = RateLimiter(geolocator.geocode, min_delay_seconds=1)

# Apply the geocoder with delay using the rate limiter:
data['temp'] = data['addr'].apply(geocode_with_delay)

# Get point coordinates from the GeoPy location object on each row:
data["coords"] = data['temp'].apply(lambda loc: tuple(loc.point) if loc else None)

# Create shapely point objects to geometry column:
data["geometry"] = data["coords"].apply(Point)

All in all, remember that Nominatim is not meant for super heavy use.

Table join (TODO: REMOVE TABLE JOIN FROM HERE!)

Table joins in pandas

For a comprehensive overview of different ways of combining DataFrames and Series based on set theory, have a look at pandas documentation about merge, join and concatenate.

Joining data between two or several tables is a common task in many (spatial) data analysis workflows. 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 the merge() -function. We used this approach in the geo-python course exercise 6.

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.

We can use this approach to join information from the original data to our geocoded addresses row-by-row join() -function which merges the two DataFrames together based on index by default. This approach works correctly because the order of the geocoded addresses in geo DataFrame is the same as in our original data DataFrame.

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

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

type(join)

As a result we have a new GeoDataFrame called join where we now have all original columns plus a new column for geometry. Note! If you would do the join the other way around, i.e. data.join(geo), the output would be a pandas DataFrame, not a GeoDataFrame!

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

# Output file path
outfp = r"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!

Nominatim works relatively nicely if you have well defined and well-known addresses such as the ones that we used in this tutorial. In practice, the address needs to exist in the OpenStreetMap database. Sometimes, however, you might want to geocode a “point-of-interest”, such as a museum, only based on it’s name. If the museum name is not on OpenStreetMap, Nominatim won’t provide any results for it, but you might be able to geocode the place using some other geocoder.