This page was generated from source/notebooks/L4/spatial-join.ipynb.
Binder badge
Binder badge

Spatial join

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 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 in the gpd.sjoin() -function:

  • "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 these two layers:

  • Addresses: the address-point Shapefile that we created and reprojected previously
  • Population grid: a Polygon layer that is a 250m x 250m grid showing the amount of people living in the Helsinki Region.
    • The population grid a dataset is produced by the Helsinki Region Environmental Services Authority (HSY) (see this page to access data from different years).
    • For this lesson we will use the population grid for year 2015, which can be dowloaded from Helsinki Region Infroshare (HRI) open data portal

Clean the data

  • Let’s read the data into memory and see what we have.
In [2]:
import geopandas as gpd

# Filepath
fp = "L4_data/Vaestotietoruudukko_2015.shp"

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

# See the first rows

INDEX ASUKKAITA ASVALJYYS IKA0_9 IKA10_19 IKA20_29 IKA30_39 IKA40_49 IKA50_59 IKA60_69 IKA70_79 IKA_YLI80 geometry
0 688 8 31.0 99 99 99 99 99 99 99 99 99 POLYGON ((25472499.99532626 6689749.005069185,...
1 703 6 42.0 99 99 99 99 99 99 99 99 99 POLYGON ((25472499.99532626 6685998.998064222,...
2 710 8 44.0 99 99 99 99 99 99 99 99 99 POLYGON ((25472499.99532626 6684249.004130407,...
3 711 7 64.0 99 99 99 99 99 99 99 99 99 POLYGON ((25472499.99532626 6683999.004997005,...
4 715 19 23.0 99 99 99 99 99 99 99 99 99 POLYGON ((25472499.99532626 6682998.998461431,...

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'}.
In [3]:
# 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'
Index(['INDEX', 'pop15', 'ASVALJYYS', 'IKA0_9', 'IKA10_19', 'IKA20_29',
       'IKA30_39', 'IKA40_49', 'IKA50_59', 'IKA60_69', 'IKA70_79', 'IKA_YLI80',
  • Let’s also get rid of all unnecessary columns by selecting only columns that we need i.e. pop15 and geometry
In [4]:
# Columns that will be sected
selected_cols = ['pop15', 'geometry']

# Select those columns
pop = pop[selected_cols]

# Let's see the last 2 rows
pop15 geometry
0 8 POLYGON ((25472499.99532626 6689749.005069185,...
1 6 POLYGON ((25472499.99532626 6685998.998064222,...
2 8 POLYGON ((25472499.99532626 6684249.004130407,...
3 7 POLYGON ((25472499.99532626 6683999.004997005,...
4 19 POLYGON ((25472499.99532626 6682998.998461431,...

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
In [5]:
# Addresses filpath
addr_fp = "L4_data/addresses.shp"

# Read data
addresses = gpd.read_file(addr_fp)

# Check the head of the file
address id geometry
0 Kampinkuja 1, 00100 Helsinki, Finland 1001 POINT (24.9301701 60.1683731)
1 Kaivokatu 8, 00101 Helsinki, Finland 1002 POINT (24.9418933 60.1698665)
2 Hermanstads strandsväg 1, 00580 Helsingfors, F... 1003 POINT (24.9774004 60.18735880000001)
3 Itäväylä, 00900 Helsinki, Finland 1004 POINT (25.0919641 60.21448089999999)
4 Tyynenmerenkatu 9, 00220 Helsinki, Finland 1005 POINT (24.9214846 60.1565781)

In order to do a spatial join, the layers need to be in the same projection

In [6]:
# Do they match? - We can test that ==

Re-project addresses to the projection of the population layer:

In [7]:
addresses = addresses.to_crs(
  • Let’s make sure that the coordinate reference system of the layers are identical
In [8]:
# Check the crs of address points

# Check the crs of population layer

# Do they match now? ==
{'proj': 'tmerc', 'lat_0': 0, 'lon_0': 25, 'k': 1, 'x_0': 25500000, 'y_0': 0, 'ellps': 'GRS80', 'units': 'm', 'no_defs': True}
{'proj': 'tmerc', 'lat_0': 0, 'lon_0': 25, 'k': 1, 'x_0': 25500000, 'y_0': 0, 'ellps': 'GRS80', 'units': 'm', 'no_defs': True}

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
In [9]:
# Make a spatial join
join = gpd.sjoin(addresses, pop, how="inner", op="within")

# Let's check the result
address id geometry index_right pop15
0 Kampinkuja 1, 00100 Helsinki, Finland 1001 POINT (25496123.30852197 6672833.941567578) 3326 173
1 Kaivokatu 8, 00101 Helsinki, Finland 1002 POINT (25496774.28242895 6672999.698581985) 3449 31
10 Rautatientori 1, 00100 Helsinki, Finland 1011 POINT (25496808.64582102 6673146.836896984) 3449 31
3 Itäväylä, 00900 Helsinki, Finland 1004 POINT (25505098.34340289 6677972.568484426) 5112 353
4 Tyynenmerenkatu 9, 00220 Helsinki, Finland 1005 POINT (25495639.56049686 6671520.343245601) 3259 1397

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
In [10]:
# Output path
outfp = "L4_data/addresses_pop15_epsg3979.shp"

# Save to disk

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.
In [12]:
%matplotlib inline
import matplotlib.pyplot as plt

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

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

# Remove white space around the figure

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.