“Snapping” Polygons Together in Python (Without ArcGIS)

If you’re working with geospatial data in Python, you’ve probably encountered the need to “snap” polygons together. This process involves matching and merging overlapping or adjacent polygons to create a seamless and cohesive dataset. But what if you don’t have access to ArcGIS or prefer not to use it? Fear not, dear reader! In this article, we’ll explore how to “snap” polygons together in Python without using ArcGIS.

The Problem: Why Do We Need to Snap Polygons?

When working with geospatial data, it’s not uncommon to encounter overlapping or adjacent polygons. These can arise from various sources, such as:

• Boundary discrepancies between datasets
• Polygon creation errors
• Data integration from different sources

These overlaps can lead to issues like:

• Data redundancy and duplication
• Inconsistent boundaries and shapes
• Inaccurate calculations and analysis

By snapping polygons together, we can eliminate these issues and ensure a clean, consistent, and accurate dataset.

Prerequisites and Libraries

Before we dive into the process, make sure you have the following libraries installed:

• GeoPandas (pip install geopandas)
• Shapely (pip install shapely)
• Fiona (pip install fiona)

We’ll also assume you have a basic understanding of Python and geospatial data handling.

Step 1: Prepare Your Data

Load your polygon data into a GeoDataFrame using GeoPandas:

import geopandas as gpd

# Load your polygon data
gdf = gpd.read_file('path/to/your/data.shp')

Ensure your data is in a suitable projection (e.g., WGS84) and has a valid geometry column.

Step 2: Identify Overlapping Polygons

Use the buffer method to create a buffer around each polygon, and then identify overlapping polygons using the intersects method:

from shapely.geometry import Polygon

# Create a buffer around each polygon (adjust buffer distance as needed)
buff_dist = 0.001
gdf['buffer'] = gdf.geometry.apply(lambda x: x.buffer(buff_dist))

# Identify overlapping polygons
overlaps = gdf[gdf.intersects(gdf.unary_union)]

This code creates a buffer around each polygon and then finds overlapping polygons by checking if each polygon intersects with the union of all other polygons.

Step 3: Snap Polygons Together

Now it’s time to “snap” the overlapping polygons together. We’ll use the union method to merge the overlapping polygons:

# Merge overlapping polygons
gdf_snapped = gdf.copy()
for idx, row in overlaps.iterrows():
    overlap_geom = row['geometry']
    for other_idx, other_row in overlaps.iterrows():
        if other_idx != idx:
            other_geom = other_row['geometry']
            if overlap_geom.intersects(other_geom):
                new_geom = overlap_geom.union(other_geom)
                gdf_snapped.at[idx, 'geometry'] = new_geom
                gdf_snapped.drop(other_idx, inplace=True)
                break

This code iterates through the overlapping polygons, merging them using the union method. The resulting snapped polygons are stored in a new GeoDataFrame (gdf_snapped).

Step 4: Clean Up and Visualize

Remove any duplicate or redundant polygons from the snapped GeoDataFrame:

gdf_snapped.drop_duplicates(subset='geometry', inplace=True)

Visualize the snapped polygons using a library like Matplotlib or Folium:

import matplotlib.pyplot as plt

# Plot the snapped polygons
gdf_snapped.plot(color='blue', edgecolor='black')
plt.show()

Voilà! You’ve successfully “snapped” polygons together in Python without ArcGIS.

Conclusion and Next Steps

In this article, we’ve demonstrated how to “snap” polygons together in Python using GeoPandas, Shapely, and Fiona. This process is essential for ensuring accurate and consistent geospatial data analysis.

Some potential next steps include:

• Integrating this process into a larger geospatial data workflow
• Refining the snapping process using more advanced techniques (e.g., using a tolerance value)
• Exploring other libraries and tools for snapping polygons (e.g., PySAL, GEOS)

Remember, clean and consistent data is crucial for accurate analysis and decision-making. By snapping polygons together, you’ll be well on your way to working with reliable and effective geospatial data.

Library	Description
GeoPandas	A library for working with geospatial data in Python, providing a GeoDataFrame data structure and various geospatial operations.
Shapely	A library for manipulation and analysis of planar geometric objects, providing an efficient and flexible way to work with geometric data.
Fiona	A library for reading and writing geospatial data in various formats, providing a convenient way to work with geospatial data in Python.

References:

• GeoPandas documentation: https://geopandas.org/en/stable/docs/reference.html
• Shapely documentation: https://shapely.readthedocs.io/en/stable/manual.html
• Fiona documentation: https://fiona.readthedocs.io/en/1.9.0/index.html

Frequently Asked Question

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