Python Spatial Join with GeoPandas (and GEOS)

Updated Sep 13, 2021


The purpose of this post is to perform an "efficient" spatial join in Python. What is a spatial join? Here is the definition from

A Spatial join is a GIS operation that affixes data from one feature layer’s attribute table to another from a spatial perspective.

For example, in the following, we are going to perform a spatial join between a point layer and a polygon layer. One of the attributes of the polygon layer is a string code that we want to attach to the points located within each polygon.

Warning: the time measurements given in the following correspond to different Jupyter sessions with various other jobs running alongside, so they would vary if run again. They are only presented to provide order of magnitude.

The Datasets

Point layer

The point dataset deals with real estate price. Its called DVF and is provided by the french open data. Here is a description from

The “Requests for real estate values” database, or DVF, lists all the sales of real estate made over the last five years, in mainland France and in the overseas departments and territories - except in Mayotte and Alsace-Moselle. The goods concerned can be built (apartment and house) or not built (land lot, fields).

The locations of most of these real estate transfers have been geocoded, so they exhibit some longitude and latitude attributes. The data can be downloaded from here as yearly CSVs. For a given year, different files correspond to different levels of administrative divisions (communes, departements):

Index of /geo-dvf/latest/csv/2020/
communes/                                          01-Jun-2021 15:30                   -
departements/                                      01-Jun-2021 15:30                   -
full.csv.gz                                        01-Jun-2021 12:48            71045047

However, here we are going to use the "full" data files corresponding to the whole territory: full2016.csv.gz, full2017.csv.gz, ..., full2020.csv.gz. Although data is only available for the last five years, we also included 2 previously collected files: full2014.csv.gz and full2015.csv.gz, for a total 7 years time span. This results in a dataframe with 19 895 888 rows (note that a property transfer can hold several distinct rows).

The csv files have been previously loaded with Pandas, concatenated and saved as a Parquet file. Here is the snippet used (dfv_fps is a list with the gzipped CSV file paths):

df = pd.DataFrame()
for fp in dfv_fps:
    df_tmp = pd.read_csv(fp, low_memory=False)
    df = pd.concat([df, df_tmp], axis=0)
df.date_mutation = pd.to_datetime(df.date_mutation)
df.sort_values(by="date_mutation", ascending=True, inplace=True)
df.reset_index(drop=True, inplace=True)
for col in ["lot1_numero", "lot2_numero", "lot3_numero", "lot4_numero", "lot5_numero"]:
    df[col] = df[col].astype(str)

Polygon layer

For the polygon layer, we are going to use the IRIS zones. Here is a definition of these zones from INSEE (National Institute of Statistics and Economic Studies) website:

In order to prepare for the dissemination of the 1999 population census, INSEE developed a system for dividing the country into units of equal size, known as IRIS2000. In French, IRIS is an acronym of ‘aggregated units for statistical information’, and the 2000 refers to the target size of 2000 residents per basic unit. Since that time IRIS (the term which has replaced IRIS2000) has represented the fundamental unit for dissemination of infra-municipal data. These units must respect geographic and demographic criteria and have borders which are clearly identifiable and stable in the long term. Towns with more than 10,000 inhabitants, and a large proportion of towns with between 5,000 and 10,000 inhabitants, are divided into several IRIS units. This separation represents a division of the territory.

Data can be downloaded from here. We used the 2021 version from this ftp link:

There are 48589 zones that form a partition without overlap of mainland France and Corsica.

GeoPandas and PyGeos

Most of the treatments in the following are made with the great GeoPandas library, installed with conda:

conda install -c conda-forge geopandas

GeoPandas may use GEOS as a "booster", using the PyGeos interface. Here is a presentation of PyGeos from its documentation:

PyGEOS is a C/Python library with vectorized geometry functions. The geometry operations are done in the open-source geometry library GEOS. PyGEOS wraps these operations in NumPy ufuncs providing a performance improvement when operating on arrays of geometries.

GEOS (Geometry Engine - Open Source) is a C++ port of the ​Java Topology Suite (JTS).

As explained in the GeoPandas documentation:

whether the speedups are used or not is determined by:

If PyGEOS >= 0.8 is installed, it will be used by default (but installing GeoPandas will not yet automatically install PyGEOS as dependency, you need to do this manually).

You can still toggle the use of PyGEOS when it is available, by [...] setting an option: geopandas.options.use_pygeos = True/False. Note, although this variable can be set during an interactive session, it will only work if the GeoDataFrames you use are created (e.g. reading a file with read_file) after changing this value.

Here we installed pygeos version 0.10.2, which uses libgeos 3.9.1:

conda install pygeos --channel conda-forge


import contextily as ctx
from colorcet import palette
import datashader as ds
from datashader.mpl_ext import dsshow
import geopandas as gpd
from import sjoin
import matplotlib.pyplot as plt
import pandas as pd
from pygeos import Geometry
import numpy as np
from shapely.geometry import Polygon, MultiPolygon
gpd.options.use_pygeos = True

Load/prepare the point data

In order to perform the spatial join, we only load 3 columns:

  • id_parcelle is a string identification for plots of land, that we are going to use later to join with the full dataset (attribute join).
  • longitude and latitude are the geographical coordinates (WGS 84 - EPSG:4326)
dvf_df = pd.read_parquet('dvf.parquet', columns=['id_parcelle', 'longitude', 'latitude'])
CPU times: user 6.03 s, sys: 1.43 s, total: 7.46 s
Wall time: 6.38 s
id_parcelle     object
longitude      float64
latitude       float64
dtype: object

Drop rows with missing coordinates:

dvf_df.dropna(subset=["latitude", "longitude"], how="any", inplace=True)

Only keep mainland France and Corsica:

bbox = (-5.06, 41.04, 9.91, 51.27)
dvf_df = dvf_df[
    (dvf_df.longitude > bbox[0])
    & (dvf_df.latitude > bbox[1])
    & (dvf_df.longitude < bbox[2])
    & (dvf_df.latitude < bbox[3])

Plot the points

Let's plot thes points with datashader using its native support for matplotlib. We start by loading the french border geometry as a geodataframe, after downloading the shapefile from the naturalearth website:

world = gpd.read_file("./admin_countries/ne_10m_admin_0_countries.shp")
france = world[(world.SOVEREIGNT == "France") & (world.TYPE == "Country")]["geometry"]
france.geometry.values[0] = MultiPolygon(
    [france.geometry.values[0][1], france.geometry.values[0][11]]  # extract mainland France and Corsica

We also make sure to use the same CRS (Coordinate Reference Systems) as for the points from the DVF dataset (EPSG:4326):
<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich
cmap = palette["dimgray"][::-1]
fig, ax = plt.subplots(figsize=(15,15))
_ = dsshow(
    ds.Point("longitude", "latitude"),
_ = ax.set(title="DVF points")
ax = france.plot(facecolor="none", edgecolor="black", ax=ax)
CPU times: user 1.61 s, sys: 447 ms, total: 2.05 s
Wall time: 1.4 s


We can see that data for Alsace and Moselle are missing (top right) because they have a specific legal status.

We want to assign a single zone code (from a polygon) to each id_parcelle. So let's keep a single geographical point per id_parcelle:

dvf_df.drop_duplicates(subset="id_parcelle", keep="first", inplace=True)
CPU times: user 8.85 s, sys: 557 ms, total: 9.41 s
Wall time: 9.44 s

This reduces the size of the point dataset significantly! Let's transform the dataframe into a geodataframe:

dvf_gdf = gpd.GeoDataFrame(
    dvf_df[['id_parcelle']], geometry=gpd.points_from_xy(dvf_df.longitude, dvf_df.latitude), crs="EPSG:4326")
del dvf_df
CPU times: user 1.86 s, sys: 649 ms, total: 2.51 s
Wall time: 1.98 s

This is where the GEOS library starts to make a significant difference regarding the elapsed time:

With GEOSWithout GEOS
1.98s4m 14s
016002000ZM0106POINT (0.20973 46.06524)
1830930000A2119POINT (5.71065 43.32764)
2830930000A2120POINT (5.71082 43.32771)
id_parcelle    0
geometry       0
dtype: int64

Load/prepare the polygon data

iris_gdf = gpd.read_file(
)[['CODE_IRIS', 'geometry']]
CPU times: user 4.5 s, sys: 220 ms, total: 4.72 s
Wall time: 3.19 s
0721910000POLYGON ((498083.500 6747517.400, 498128.000 6...
1772480000POLYGON ((685753.100 6868612.900, 685757.700 6...
2514260000POLYGON ((759067.200 6849592.700, 758778.600 6...

There is no significant difference regarding the elapsed time with or without GEOS for this loading operation:

With GEOSWithout GEOS
48589 g: g.geom_type).value_counts()
Polygon         48333
MultiPolygon      256
Name: geometry, dtype: int64
<Projected CRS: EPSG:2154>
Name: RGF93 / Lambert-93
Axis Info [cartesian]:
- X[east]: Easting (metre)
- Y[north]: Northing (metre)
Area of Use:
- name: France - onshore and offshore, mainland and Corsica.
- bounds: (-9.86, 41.15, 10.38, 51.56)
Coordinate Operation:
- name: Lambert-93
- method: Lambert Conic Conformal (2SP)
Datum: Reseau Geodesique Francais 1993
- Ellipsoid: GRS 1980
- Prime Meridian: Greenwich

We need to use the same CRS between the point and polygon layers. So let's convert the the polygon layer to EPSG4326:

iris_gdf = iris_gdf.to_crs("EPSG:4326")
CPU times: user 4.68 s, sys: 68.3 ms, total: 4.75 s
Wall time: 4.48 s

Again we observe an improvement with GEOS:

With GEOSWithout GEOS

Plot the polygons

ax = iris_gdf.plot(facecolor="white", edgecolor="black", alpha=0.1, figsize=(15, 15))
CPU times: user 17.8 s, sys: 228 ms, total: 18.1 s
Wall time: 18 s


Spatial indexing

In order to perform the spatial join between 10256266 points and 48589 Poygons/MultiPolygons, we need some spatial indexing. What is it? Here is a definition from

A spatial index is a data structure that allows for accessing a spatial object efficiently. It is a common technique used by spatial databases. Without indexing, any search for a feature would require a "sequential scan" of every record in the database, resulting in much longer processing time. In a spatial index construction process, the minimum bounding rectangle serves as an object approximation. Various types of spatial indices across commercial and open-source databases yield measurable performance differences. Spatial indexing techniques are playing a central role in time-critical applications and the manipulation of spatial big data.

From GeoPandas documentation:

GeoPandas offers built-in support for spatial indexing using an R-Tree algorithm. Depending on the ability to import pygeos, GeoPandas will either use pygeos.STRtree or rtree.index.Index.

So we won't use the same piece of code with or without GEOS, but it will use some kind of R-trees in both cases. Here is the definition of R-trees from wikipedia:

R-trees are tree data structures used for spatial access methods, i.e., for indexing multi-dimensional information such as geographical coordinates, rectangles or polygons. The R-tree was proposed by Antonin Guttman in 1984 and has found significant use in both theoretical and applied contexts. A common real-world usage for an R-tree might be to store spatial objects such as restaurant locations or the polygons that typical maps are made of: streets, buildings, outlines of lakes, coastlines, etc. and then find answers quickly to queries such as "Find all museums within 2 km of my current location", "retrieve all road segments within 2 km of my location" (to display them in a navigation system) or "find the nearest gas station" (although not taking roads into account).

Simple example of a R-tree for 2D rectangles from wikipedia:


The implementation in GEOS is a Sort-Tile-Recursive (STR) algorithm.

Let's build the index.

CPU times: user 7.38 s, sys: 855 ms, total: 8.23 s
Wall time: 8.24 s
<geopandas.sindex.PyGEOSSTRTreeIndex at 0x7fa8b3e2c0a0>
With GEOSWithout GEOS
8.24s6min 15s

With PyGeos we get a geopandas.sindex.PyGEOSSTRTreeIndex index object, otherwise we get a rtree.index.Index(bounds=[-5.059852, 41.363063, 9.557005, 51.082069], size=10256266).

Spatial join

Now we are going to perform a left join with a within operator (point-in-polygon). It seems that the intersects operator takes really more time in the current case.

dvf_gdf_sjoin = sjoin(
        iris_gdf[['CODE_IRIS', 'geometry']],
dvf_gdf_sjoin.drop("index_right", axis=1, inplace=True)
CPU times: user 19.8 s, sys: 2.69 s, total: 22.5 s
Wall time: 17.7 s
With GEOSWithout GEOS
17.7s3min 1s
016002000ZM0106POINT (0.20973 46.06524)160020000
1830930000A2119POINT (5.71065 43.32764)830930000
2830930000A2120POINT (5.71082 43.32771)830930000
id_parcelle       0
geometry          0
CODE_IRIS      2559
dtype: int64

So unfortunately 2559 points (over 10256266) did not match any zone.

Orphan points

Let's isolate them:

orphans_gdf = dvf_gdf_sjoin[dvf_gdf_sjoin.CODE_IRIS.isna()].drop("CODE_IRIS", axis=1)
CPU times: user 833 ms, sys: 3.6 ms, total: 836 ms
Wall time: 842 ms
231583019000BZ0030POINT (6.36461 43.12122)
755911202000DX0019POINT (3.03670 42.86225)
1084334003000OK0003POINT (3.50888 43.27685)

Plot the orphan points

ax = orphans_gdf.plot(figsize=(15, 15))
ax = france.plot(facecolor="none", edgecolor="black", ax=ax)

Orphan points

We can see that the orphans are all located at the outside border of the IRIS polygons. Let's zoom in to see an area where a lot of points are orphans:

# Plouguerneau
bb = (-4.618113, 48.555804, -4.397013, 48.658866)
bb_polygon = Polygon([(bb[0], bb[1]), (bb[0], bb[3]), (bb[2], bb[3]), (bb[2], bb[1])])
bbox = gpd.GeoDataFrame(geometry=[bb_polygon], crs="EPSG:4326")
iris_zoom = gpd.overlay(iris_gdf, bbox, how="intersection")
orphans_zoom = gpd.overlay(orphans_gdf, bbox, how="intersection")
# Convert the data to Web Mercator to display some tiles
ax = orphans_zoom.to_crs("EPSG:3857").plot(color="r", markersize=25, figsize=(15, 15))
ax = iris_zoom.to_crs("EPSG:3857").plot(
    facecolor="none", edgecolor="k", linewidth=2, alpha=0.6, ax=ax
ctx.add_basemap(ax, source=ctx.providers.Stamen.Terrain)


The poins may be orphans because they are located on a small island or because the IRIS contour is simplified and do not match exactly the actual land contour. So let's perform a nearest query using the spatial index, in order to match each orphan point with the nearest IRIS polygon.

Nearest query

Although it is not mandatory here, we switch to a projected CRS before looking for the nearest zones. As explained here, this allows to minimize visual distortion in a particular region. Basically distances can be accurately measured in a projected CRS.

orphans_gpd = orphans_gdf.to_crs("EPSG:2154")  # RGF93 / Lambert-93 -- France
iris_gdf = iris_gdf.to_crs("EPSG:2154")
CPU times: user 2.79 s, sys: 103 ms, total: 2.89 s
Wall time: 2.9 s
With GEOSWithout GEOS
2.9s9.29 s

We see that the iris_gdf does not have an index, let's do that with (very quick process):


Note that the required indices are built automatically if needed when performing the spatial join. It is not required to call the index contructor by hand.

Now we create an array of pygeos.Geometry objects:

points_str = list(
points = np.vectorize(Geometry)(points_str)
CPU times: user 186 ms, sys: 560 µs, total: 186 ms
Wall time: 177 ms

array([<pygeos.Geometry POINT (6.36 43.1)>,
       <pygeos.Geometry POINT (3.04 42.9)>,
       <pygeos.Geometry POINT (3.51 43.3)>, ...,
       <pygeos.Geometry POINT (-3.15 47.3)>,
       <pygeos.Geometry POINT (-2.79 47.5)>,
       <pygeos.Geometry POINT (3.78 50.4)>], dtype=object)

Also, we need a function returning the nearest Polygon:

def find_nearest_idx(point):
    return iris_gdf.sindex.nearest(point)[1][0]

The following vectorization actually only works with GEOS. Otherwise we get the following error message:

TypeError: Bounds must be a sequence
indices = np.vectorize(find_nearest_idx)(points)
CPU times: user 459 ms, sys: 3.47 ms, total: 463 ms
Wall time: 459 ms

Here is for example the index of the nearest zone for the first orphan point:


We can actually check is the distance method of GeoPandas returns the same index:

CPU times: user 252 ms, sys: 0 ns, total: 252 ms
Wall time: 250 ms

Attribute join

Now let's put everything together:

orphans_gdf["CODE_IRIS"] = iris_gdf.iloc[indices].CODE_IRIS.values
orphans_gpd = orphans_gpd.to_crs("EPSG:4326")
dvf_gdf = pd.concat([dvf_gdf_sjoin.dropna(), orphans_gdf], axis=0)
id_parcelle    0
geometry       0
CODE_IRIS      0
dtype: int64
dvf_df = pd.read_parquet("dvf.parquet")
CPU times: user 57.8 s, sys: 9.53 s, total: 1min 7s
Wall time: 29.6 s
dvf_df = pd.merge(dvf_df, dvf_gdf[["id_parcelle", "CODE_IRIS"]], on="id_parcelle", how="left")
CPU times: user 1min 44s, sys: 28.3 s, total: 2min 12s
Wall time: 2min 12s
id_mutation                            0
date_mutation                          0
numero_disposition                     0
nature_mutation                        0
valeur_fonciere                   262966
adresse_numero                   8401364
adresse_suffixe                 19058836
adresse_nom_voie                  200632
adresse_code_voie                 185574
code_postal                       197452
code_commune                           0
nom_commune                            0
code_departement                       0
ancien_code_commune             19435502
ancien_nom_commune              19435502
id_parcelle                            0
ancien_id_parcelle              19781277
numero_volume                   19839247
lot1_numero                            0
lot1_surface_carrez             18251916
lot2_numero                            0
lot2_surface_carrez             19488477
lot3_numero                            0
lot3_surface_carrez             19855820
lot4_numero                            0
lot4_surface_carrez             19885521
lot5_numero                            0
lot5_surface_carrez             19891330
nombre_lots                            0
code_type_local                  9199398
type_local                       9199398
surface_reelle_bati             11885358
nombre_pieces_principales        9215887
code_nature_culture              6133795
nature_culture                   6133795
code_nature_culture_speciale    18972456
nature_culture_speciale         18972456
surface_terrain                  6134186
longitude                         610533
latitude                          610533
CODE_IRIS                         752239
dtype: int64

Remaining missing values for the CODE_IRIS column corresponds to rows with missing coordinates or locations outside mainland France or Corsica.