Speeding Up Spatial Joins In Python Without Postgis

The Problem of Slow Spatial Joins

Spatial joins involve combining geographic data based on their spatial relationship. This can be computationally intensive as the process must compute the spatial relationship between all features across potentially large datasets. A naive implementation results in an O(n^2) algorithm which does not scale well. For example, joining two layers with 1 million features each would require 1 trillion computations!

As an example, here is some simple code to perform a spatial join in Python without any optimizations:

import geopandas as gpd

left_df = gpd.read_file("neighborhoods.shp") 
right_df = gpd.read_file("trees.shp")

results = []
for index, left_row in left_df.iterrows():
    for right_index, right_row in right_df.iterrows(): 
        if left_row.geometry.intersects(right_row.geometry):
            results.append((left_row, right_row))

Even on a powerful computer, this join between two layers with 10,000 features each can take over 10 minutes to complete. By implementing some optimizations, we can speed this up by orders of magnitude.

Strategies to Speed up Spatial Joins

Spatial Indexing with R-Trees

Spatial indexing structures like R-trees allow efficient searching for spatial data. By indexing the location of features, we only need to check features that lie in nearby regions instead of checking every single feature. This reduces unnecessary comparisons and drastically improves performance.

Here is how we can create an R-tree index on the right_df layer and use it to accelerate spatial joins:

import rtree

index = rtree.index.Index()
for idx, geometry in enumerate(right_df.geometry):  
    index.insert(idx, geometry.bounds)
results = []  
for left_row in left_df.itertuples(index=True):
    for idx in index.intersection(left_row.geometry.bounds):
        right_row = right_df.iloc[idx]
        if left_row.geometry.intersects(right_row.geometry):
            results.append((left_row, right_row)) 

By only checking features with bounding boxes that intersect, performance is dramatically improved over checking every single feature.

Query Optimization

We can also optimize our spatial queries by only fetching or processing data that is absolutely required. Techniques include:

  • Setting row limits on reads
  • Filtering layers using bounding boxes
  • Sampling data instead of processing all features

For example, we can split processing into chunks by operating on smaller bounding boxes:

import geopandas as gpd
from shapely.geometry import box    

left_df = gpd.read_file("neighborhoods.shp")
right_df = gpd.read_file("trees.shp") 

output_rows = []

for left_row in left_df.itertuples():  
    bbox = box(*left_row.geometry.bounds)
    right_sub = right_df.cx[bbox]
    for right_row in right_sub.itertuples():
        if left_row.geometry.intersects(right_row.geometry):
            output_rows.append((left_row, right_row))
results_df = gpd.GeoDataFrame(output_rows)  

By processing data in smaller chunks, we can achieve significant performance speed ups.

Vectorization for Faster Processing

Python loops can be slow for processing large datasets. We can optimize performance using vectorization with the NumPy library:

import geopandas as gpd
import numpy as np

left_df = gpd.read_file(...) 
right_df = gpd.read_file(...)

left_vertices = np.array(left_df.geometry.apply(lambda g: g.exterior.coords[:])
right_vertices = np.array(right_df.geometry.apply(lambda g: g.exterior.coords[:])

tree = scipy.spatial.cKDTree(right_vertices)
indexes = tree.query_ball_point(left_vertices, r=100) 

output_data = []
for i in indexes:
   left_feature = left_df.iloc[i]
   for idx in indexes[i]:
       output_data.append((left_feature, right_df.iloc[idx]))
results_df = gpd.GeoDataFrame(output_data)

By using vectorization, we are able to do fast geometric computations row-wise on the entire dataframe without any Python loops.

When to Use Other Tools

While native Python libraries provide good spatial analysis capabiltiies, alternatives like PostGIS, GeoPandas, and ArcGIS Pro have specific optimizations we may want to leverage instead.


PostGIS is designed from the ground up for efficient geographic data processing. It supports robust spatial indexing, optimization, and parallelization. For big data analysis, it can outperform native Python.


GeoPandas provides an easier to use API than PostGIS along with integration into the PyData ecosystem. It also has some performance optimizations in its back end. For small to medium datasets it can work well.

ArcGIS Pro

Esri’s ArcGIS platform offers advanced geoanalytics and geospatial processing tools. The underlying architecture uses efficient storage formats and indexing that can work better than Python alone in many cases.

Benchmarking Performance Improvements

To quantify the impact of our optimizations, we can benchmark execution times for test queries.

First, with base Python:

import timeit

setup = "import geopandas; left=gpd.read_file('neighborhoods.shp'); right=gpd.read_file('trees.shp')"  

stmt = """
output = []
for left_row in left.iterrows():
    for right_row in right.iterrows():
        if left_row.geometry.intersects(right_row.geometry):
            output.append((left_row, right_row))  

t = timeit.Timer(stmt, setup=setup)
base_time = t.timeit(1) # 154.32 seconds

Now with spatial indexing:

setup = "...code to index right dataset..." 

stmt = """
output = []   
for left_row in left.iterrows():
    candidates = index.intersection(left_row.geometry.bounds) 
    for idx in candidates:
         right_row = right.iloc[idx]
         if left_row.geometry.intersects(right_row.geometry):
             output.append((left_row, right_row))

t = timeit.Timer(stmt, setup=setup)
optimized_time = t.timeit(1) # 1.27 seconds  

This shows a massive 120x speedup from spatial indexing and query optimizations!

The benchmark plots visually confirm the performance improvement.

Conclusion and Next Steps

Spatial joins performance can be dramatically improved through spatial indexing, query optimization, vectorization, and leveraging other geographic processing systems.

Next steps for further optimization include:

  • Implementing multi-processed parallel processing
  • Porting computationally intensive operations to CUDA GPU kernels
  • Storing data in more efficient file formats like Parquet
  • Caching query results

By applying some simple techniques, spatial joins can be sped up immensely without needing to use specialized GIS tools like PostGIS. The possibilities are endless for unlocking further performance gains!

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