Spatial join


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 -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.
import geopandas as gpd

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

# Read the data
pop = gpd.read_file(fp)
# See the first rows
In [1]: pop.head()
   INDEX  ASUKKAITA  ASVALJYYS  IKA0_9  IKA10_19  IKA20_29  IKA30_39  \
0    688          8       31.0      99        99        99        99   
1    703          6       42.0      99        99        99        99   
2    710          8       44.0      99        99        99        99   
3    711          7       64.0      99        99        99        99   
4    715         19       23.0      99        99        99        99   

   IKA40_49  IKA50_59  IKA60_69  IKA70_79  IKA_YLI80  \
0        99        99        99        99         99   
1        99        99        99        99         99   
2        99        99        99        99         99   
3        99        99        99        99         99   
4        99        99        99        99         99   

0  POLYGON ((25472499.99532626 6689749.005069185,...  
1  POLYGON ((25472499.99532626 6685998.998064222,...  
2  POLYGON ((25472499.99532626 6684249.004130407,...  
3  POLYGON ((25472499.99532626 6683999.004997005,...  
4  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'}.
# Change the name of a column
In [2]: pop = pop.rename(columns={'ASUKKAITA': 'pop15'})

# See the column names and confirm that we now have a column called 'pop15'
In [3]: pop.columns
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
# Columns that will be sected
In [4]: selected_cols = ['pop15', 'geometry']

# Select those columns
In [5]: pop = pop[selected_cols]

# Let's see the last 2 rows
In [6]: pop.tail(2)
      pop15                                           geometry
5782      9  POLYGON ((25513499.99632164 6685498.999797418,...
5783  30244  POLYGON ((25513999.999929 6659998.998172711, 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
# Addresses filpath
In [7]: addr_fp = r"/home/geo/addresses_epsg3879.shp"

# Read data
In [8]: addresses = gpd.read_file(addr_fp)

# Check the head of the file
In [9]: addresses.head(2)
                                 address    id  \
0  Kampinkuja 1, 00100 Helsinki, Finland  1001   
1   Kaivokatu 8, 00101 Helsinki, Finland  1002   

0  POINT (25496123.30852197 6672833.941567578)  
1  POINT (25496774.28242895 6672999.698581985)  
  • Let’s make sure that the coordinate reference system of the layers are identical
# Check the crs of address points
In [10]:
{'ellps': 'GRS80',
 'k': 1,
 'lat_0': 0,
 'lon_0': 25,
 'no_defs': True,
 'proj': 'tmerc',
 'units': 'm',
 'x_0': 25500000,
 'y_0': 0}

# Check the crs of population layer
In [11]:
{'ellps': 'GRS80',
 'k': 1,
 'lat_0': 0,
 'lon_0': 25,
 'no_defs': True,
 'proj': 'tmerc',
 'units': 'm',
 'x_0': 25500000,
 'y_0': 0}

# Do they match? - We can test that
In [12]: ==
Out[12]: 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
# Make a spatial join
In [13]: join = gpd.sjoin(addresses, pop, how="inner", op="within")

# Let's check the result
In [14]: join.head()
                                          address    id  \
16  Malminkartanontie 17, 00410 Helsinki, Finland  1017   
19     Pitäjänmäentie 15, 00370 Helsinki, Finland  1020   
12       Trumstigen 8, 00420 Helsingfors, Finland  1013   
11           Kuparitie 8, 00440 Helsinki, Finland  1012   
15            Kylätie 23, 00320 Helsinki, Finland  1016   

                                       geometry  index_right  pop15  
16  POINT (25492349.68368251 6681772.551210108)         2684     74  
19  POINT (25492292.61413005 6679039.264838208)         2691    241  
12  POINT (25493207.62503373 6680836.727432437)         2852    577  
11  POINT (25493575.10327127 6679775.868274149)         2949    562  
15   POINT (25494077.1680778 6678341.639159317)         3036    414  

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

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 [15]: import matplotlib.pyplot as plt

# Plot the points with population info
In [16]: join.plot(column='pop15', cmap="Reds", markersize=7, scheme='natural_breaks', legend=True);

# Add title
In [17]: plt.title("Amount of inhabitants living close the the point");

# Remove white space around the figure
In [18]: 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.