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Spatial join

Sources

Following materials are partly based on documentation of Geopandas.

Spatial join is yet another classic GIS problem. Getting attributes from one layer and transferring them into another layer based on their spatial relationship is something you most likely need to do on a regular basis.

The previous materials focused on learning how to perform a Point in Polygon query. We could now apply those techniques and create our own function to perform a spatial join between two layers based on their spatial relationship. We could for example join the attributes of a polygon layer into a point layer where each point would get the attributes of a polygon that contains the point.

Luckily, spatial join (gpd.sjoin() -function) is already implemented in Geopandas, thus we do not need to create it ourselves. There are three possible types of join that can be applied in spatial join that are determined with op -parameter:

  • "intersects"
  • "within"
  • "contains"

Sounds familiar? Yep, all of those spatial relationships were discussed in the previous materials, thus you should know how they work.

Let's perform a spatial join between the address-point Shapefile that we created and then reprojected and a Polygon layer that is a 250m x 250m grid showing the amount of people living in Helsinki Region.

Download and clean the data

For this lesson we will be using publicly available population data from Helsinki that can be downloaded from Helsinki Region Infroshare (HRI) which is an excellent source that provides all sorts of open data from Helsinki, Finland.

From HRI download a Population grid for year 2015 that is a dataset (.shp) produced by Helsinki Region Environmental Services Authority (HSY) (see this page to access data from different years).

  • Unzip the file in Terminal into a folder called Pop15 (using -d flag)
$ cd
$ unzip Vaestotietoruudukko_2015.zip -d Pop15
$ ls Pop15
Vaestotietoruudukko_2015.dbf  Vaestotietoruudukko_2015.shp
Vaestotietoruudukko_2015.prj  Vaestotietoruudukko_2015.shx

You should now have a folder /home/geo/Pop15 with files listed above.

  • Let's read the data into memory and see what we have.
.. ipython:: python
  :suppress:

    import os
    import gdal
    fp = os.path.join(os.path.abspath('data'), "Vaestotietoruudukko_2015.shp")
    pop = gpd.read_file(fp)


import geopandas as gpd

# Filepath
fp = "/home/geo/Pop15/Vaestotietoruudukko_2015.shp"

# Read the data
pop = gpd.read_file(fp)
.. ipython:: python

    # See the first rows
    pop.head()

Okey so we have multiple columns in the dataset but the most important one here is the column ASUKKAITA (population in Finnish) that tells the amount of inhabitants living under that polygon.

  • Let's change the name of that columns into pop15 so that it is more intuitive. Changing column names is easy in Pandas / Geopandas using a function called rename() where we pass a dictionary to a parameter columns={'oldname': 'newname'}.
.. ipython:: python

    # Change the name of a column
    pop = pop.rename(columns={'ASUKKAITA': 'pop15'})

    # See the column names and confirm that we now have a column called 'pop15'
    pop.columns
  • Let's also get rid of all unnecessary columns by selecting only columns that we need i.e. pop15 and geometry
.. ipython:: python

    # Columns that will be sected
    selected_cols = ['pop15', 'geometry']

    # Select those columns
    pop = pop[selected_cols]

    # Let's see the last 2 rows
    pop.tail(2)

Now we have cleaned the data and have only those columns that we need for our analysis.

Join the layers

Now we are ready to perform the spatial join between the two layers that we have. The aim here is to get information about how many people live in a polygon that contains an individual address-point . Thus, we want to join attributes from the population layer we just modified into the addresses point layer addresses_epsg3879.shp.

  • Read the addresses layer into memory
.. ipython:: python

    # Addresses filpath
    addr_fp = r"/home/geo/addresses_epsg3879.shp"

    @suppress
    import os

    @suppress
    "NOTICE: Following is the real path to the data, the one above is for online documentation to reflect the situation at computing instance"

    @suppress
    addr_fp = os.path.join(os.path.abspath('data'), "addresses_epsg3879.shp")

    # Read data
    addresses = gpd.read_file(addr_fp)

    # Check the head of the file
    addresses.head(2)
  • Let's make sure that the coordinate reference system of the layers are identical
.. ipython:: python

    # Check the crs of address points
    addresses.crs

    # Check the crs of population layer
    pop.crs

    # Do they match? - We can test that
    addresses.crs == pop.crs

Indeed they are identical. Thus, we can be sure that when doing spatial queries between layers the locations match and we get the right results e.g. from the spatial join that we are conducting here.

  • Let's now join the attributes from pop GeoDataFrame into addresses GeoDataFrame by using gpd.sjoin() -function
.. ipython:: python

    # Make a spatial join
    join = gpd.sjoin(addresses, pop, how="inner", op="within")

    # Let's check the result
    join.head()

Awesome! Now we have performed a successful spatial join where we got two new columns into our join GeoDataFrame, i.e. index_right that tells the index of the matching polygon in the pop layer and pop15 which is the population in the cell where the address-point is located.

  • Let's save this layer into a new Shapefile
# Output path
outfp = r"/home/geo/addresses_pop15_epsg3979.shp"

# Save to disk
join.to_file(outfp)

Do the results make sense? Let's evaluate this a bit by plotting the points where color intensity indicates the population numbers.

  • Plot the points and use the pop15 column to indicate the color. cmap -parameter tells to use a sequential colormap for the values, markersize adjusts the size of a point, scheme parameter can be used to adjust the classification method based on pysal, and legend tells that we want to have a legend.
.. ipython:: python

    import matplotlib.pyplot as plt

    # Plot the points with population info
    join.plot(column='pop15', cmap="Reds", markersize=7, scheme='natural_breaks', legend=True);

    # Add title
    plt.title("Amount of inhabitants living close the the point");

    # Remove white space around the figure
    @savefig population_points.png width=7in
    plt.tight_layout()

By knowing approximately how population is distributed in Helsinki, it seems that the results do make sense as the points with highest population are located in the south where the city center of Helsinki is.