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Spatial Joins

A spatial join uses binary predicates such as intersects and crosses to combine two GeoDataFrames based on the spatial relationship between their geometries.

A common use case might be a spatial join between a point layer and a polygon layer where you want to retain the point geometries and grab the attributes of the intersecting polygons.

In [1]:
from IPython.core.display import Image 
Image(url='https://web.natur.cuni.cz/~langhamr/lectures/vtfg1/mapinfo_1/about_gis/Image23.gif')
Out [1]:

Types of spatial joins

We currently support the following methods of spatial joins. We refer to the left_df and right_df which are the correspond to the two dataframes passed in as args.

Left outer join

In a LEFT OUTER JOIN (how='left'), we keep all rows from the left and duplicate them if necessary to represent multiple hits between the two dataframes. We retain attributes of the right if they intersect and lose right rows that don't intersect. A left outer join implies that we are interested in retaining the geometries of the left.

This is equivalent to the PostGIS query:

SELECT pts.geom, pts.id as ptid, polys.id as polyid  
FROM pts
LEFT OUTER JOIN polys
ON ST_Intersects(pts.geom, polys.geom);

                    geom                    | ptid | polyid 
--------------------------------------------+------+--------
 010100000040A9FBF2D88AD03F349CD47D796CE9BF |    4 |     10
 010100000048EABE3CB622D8BFA8FBF2D88AA0E9BF |    3 |     10
 010100000048EABE3CB622D8BFA8FBF2D88AA0E9BF |    3 |     20
 0101000000F0D88AA0E1A4EEBF7052F7E5B115E9BF |    2 |     20
 0101000000818693BA2F8FF7BF4ADD97C75604E9BF |    1 |       
(5 rows)

Right outer join

In a RIGHT OUTER JOIN (how='right'), we keep all rows from the right and duplicate them if necessary to represent multiple hits between the two dataframes. We retain attributes of the left if they intersect and lose left rows that don't intersect. A right outer join implies that we are interested in retaining the geometries of the right.

This is equivalent to the PostGIS query:

SELECT polys.geom, pts.id as ptid, polys.id as polyid  
FROM pts
RIGHT OUTER JOIN polys
ON ST_Intersects(pts.geom, polys.geom);

  geom    | ptid | polyid 
----------+------+--------
 01...9BF |    4 |     10
 01...9BF |    3 |     10
 02...7BF |    3 |     20
 02...7BF |    2 |     20
 00...5BF |      |     30
(5 rows)

Inner join

In an INNER JOIN (how='inner'), we keep rows from the right and left only where their binary predicate is True. We duplicate them if necessary to represent multiple hits between the two dataframes. We retain attributes of the right and left only if they intersect and lose all rows that do not. An inner join implies that we are interested in retaining the geometries of the left.

This is equivalent to the PostGIS query:

SELECT pts.geom, pts.id as ptid, polys.id as polyid  
FROM pts
INNER JOIN polys
ON ST_Intersects(pts.geom, polys.geom);

                    geom                    | ptid | polyid 
--------------------------------------------+------+--------
 010100000040A9FBF2D88AD03F349CD47D796CE9BF |    4 |     10
 010100000048EABE3CB622D8BFA8FBF2D88AA0E9BF |    3 |     10
 010100000048EABE3CB622D8BFA8FBF2D88AA0E9BF |    3 |     20
 0101000000F0D88AA0E1A4EEBF7052F7E5B115E9BF |    2 |     20
(4 rows)

Spatial Joins between two GeoDataFrames

Let's take a look at how we'd implement these using GeoPandas. First, load up the NYC test data into GeoDataFrames:

In [2]:
%matplotlib inline
from shapely.geometry import Point
from geopandas import datasets, GeoDataFrame, read_file
from geopandas.tools import overlay

# NYC Boros
zippath = datasets.get_path('nybb')
polydf = read_file(zippath)

# Generate some points
b = [int(x) for x in polydf.total_bounds]
N = 8
pointdf = GeoDataFrame([
    {'geometry': Point(x, y), 'value1': x + y, 'value2': x - y}
    for x, y in zip(range(b[0], b[2], int((b[2] - b[0]) / N)),
                    range(b[1], b[3], int((b[3] - b[1]) / N)))])

# Make sure they're using the same projection reference
pointdf.crs = polydf.crs
In [3]:
pointdf
Out [3]:
geometry value1 value2
0 POINT (913175 120121) 1033296 793054
1 POINT (932450 139211) 1071661 793239
2 POINT (951725 158301) 1110026 793424
3 POINT (971000 177391) 1148391 793609
4 POINT (990275 196481) 1186756 793794
5 POINT (1009550 215571) 1225121 793979
6 POINT (1028825 234661) 1263486 794164
7 POINT (1048100 253751) 1301851 794349
8 POINT (1067375 272841) 1340216 794534
In [4]:
polydf
Out [4]:
BoroCode BoroName Shape_Leng Shape_Area geometry
0 5 Staten Island 330470.010332 1.623820e+09 (POLYGON ((970217.0223999023 145643.3322143555...
1 4 Queens 896344.047763 3.045213e+09 (POLYGON ((1029606.076599121 156073.8142089844...
2 3 Brooklyn 741080.523166 1.937479e+09 (POLYGON ((1021176.479003906 151374.7969970703...
3 1 Manhattan 359299.096471 6.364715e+08 (POLYGON ((981219.0557861328 188655.3157958984...
4 2 Bronx 464392.991824 1.186925e+09 (POLYGON ((1012821.805786133 229228.2645874023...
In [5]:
pointdf.plot()
Out [5]:
<matplotlib.axes._subplots.AxesSubplot at 0x4f1b0b8>
In [6]:
polydf.plot()
Out [6]:
<matplotlib.axes._subplots.AxesSubplot at 0x12991208>

Joins

In [7]:
from geopandas.tools import sjoin
join_left_df = sjoin(pointdf, polydf, how="left")
join_left_df
# Note the NaNs where the point did not intersect a boro
Out [7]:
geometry value1 value2 index_right BoroCode BoroName Shape_Leng Shape_Area
0 POINT (913175 120121) 1033296 793054 NaN NaN NaN NaN NaN
1 POINT (932450 139211) 1071661 793239 0.0 5.0 Staten Island 330470.010332 1.623820e+09
2 POINT (951725 158301) 1110026 793424 0.0 5.0 Staten Island 330470.010332 1.623820e+09
3 POINT (971000 177391) 1148391 793609 NaN NaN NaN NaN NaN
4 POINT (990275 196481) 1186756 793794 NaN NaN NaN NaN NaN
5 POINT (1009550 215571) 1225121 793979 1.0 4.0 Queens 896344.047763 3.045213e+09
6 POINT (1028825 234661) 1263486 794164 4.0 2.0 Bronx 464392.991824 1.186925e+09
7 POINT (1048100 253751) 1301851 794349 NaN NaN NaN NaN NaN
8 POINT (1067375 272841) 1340216 794534 NaN NaN NaN NaN NaN
In [8]:
join_right_df = sjoin(pointdf, polydf, how="right")
join_right_df
# Note Staten Island is repeated
Out [8]:
index_left value1 value2 BoroCode BoroName Shape_Leng Shape_Area geometry
index_right
0 1.0 1071661.0 793239.0 5 Staten Island 330470.010332 1.623820e+09 (POLYGON ((970217.0223999023 145643.3322143555...
0 2.0 1110026.0 793424.0 5 Staten Island 330470.010332 1.623820e+09 (POLYGON ((970217.0223999023 145643.3322143555...
1 5.0 1225121.0 793979.0 4 Queens 896344.047763 3.045213e+09 (POLYGON ((1029606.076599121 156073.8142089844...
4 6.0 1263486.0 794164.0 2 Bronx 464392.991824 1.186925e+09 (POLYGON ((1012821.805786133 229228.2645874023...
2 NaN NaN NaN 3 Brooklyn 741080.523166 1.937479e+09 (POLYGON ((1021176.479003906 151374.7969970703...
3 NaN NaN NaN 1 Manhattan 359299.096471 6.364715e+08 (POLYGON ((981219.0557861328 188655.3157958984...
In [9]:
join_inner_df = sjoin(pointdf, polydf, how="inner")
join_inner_df
# Note the lack of NaNs; dropped anything that didn't intersect
Out [9]:
geometry value1 value2 index_right BoroCode BoroName Shape_Leng Shape_Area
1 POINT (932450 139211) 1071661 793239 0 5 Staten Island 330470.010332 1.623820e+09
2 POINT (951725 158301) 1110026 793424 0 5 Staten Island 330470.010332 1.623820e+09
5 POINT (1009550 215571) 1225121 793979 1 4 Queens 896344.047763 3.045213e+09
6 POINT (1028825 234661) 1263486 794164 4 2 Bronx 464392.991824 1.186925e+09

We're not limited to using the intersection binary predicate. Any of the Shapely geometry methods that return a Boolean can be used by specifying the op kwarg.

In [10]:
sjoin(pointdf, polydf, how="left", op="within")
Out [10]:
geometry value1 value2 index_right BoroCode BoroName Shape_Leng Shape_Area
0 POINT (913175 120121) 1033296 793054 NaN NaN NaN NaN NaN
1 POINT (932450 139211) 1071661 793239 0.0 5.0 Staten Island 330470.010332 1.623820e+09
2 POINT (951725 158301) 1110026 793424 0.0 5.0 Staten Island 330470.010332 1.623820e+09
3 POINT (971000 177391) 1148391 793609 NaN NaN NaN NaN NaN
4 POINT (990275 196481) 1186756 793794 NaN NaN NaN NaN NaN
5 POINT (1009550 215571) 1225121 793979 1.0 4.0 Queens 896344.047763 3.045213e+09
6 POINT (1028825 234661) 1263486 794164 4.0 2.0 Bronx 464392.991824 1.186925e+09
7 POINT (1048100 253751) 1301851 794349 NaN NaN NaN NaN NaN
8 POINT (1067375 272841) 1340216 794534 NaN NaN NaN NaN NaN
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