Grouping Polygons By Attributes And By Distance Using GeoPandas And Networkx

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Introduction

In this article, we will explore how to group polygons by attributes and by distance using GeoPandas and Networkx. We will use a parcel dataset containing 115k records as an example. This dataset is a common use case in geographic information systems (GIS) where we need to group parcels based on certain attributes or distances.

Background

GeoPandas is a library that allows you to easily work with geospatial data in pandas data structures. It provides a powerful and flexible way to handle geospatial data, including polygons, points, and lines. Networkx is a library for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. In this article, we will use these two libraries to group polygons by attributes and by distance.

Grouping Polygons by Attributes

Grouping polygons by attributes is a common task in GIS. We can use GeoPandas to group polygons based on certain attributes, such as the type of parcel or the owner of the parcel.

Step 1: Load the Data

First, we need to load the parcel dataset into GeoPandas. We can use the read_file function to load the dataset from a shapefile.

import geopandas as gpd

gdf = gpd.read_file('parcels.shp')

Step 2: Group by Attributes

Next, we can use the groupby function to group the polygons by attributes. For example, we can group the polygons by the type of parcel.

# Group by the type of parcel
gdf_grouped = gdf.groupby('parcel_type')

Step 3: Create a Unique Group ID

Finally, we can create a unique group ID for each group of polygons. We can use the ngroup function to assign a unique integer to each group.

# Create a unique group ID
gdf_grouped['group_id'] = gdf_grouped.ngroup()

Grouping Polygons by Distance

Grouping polygons by distance is another common task in GIS. We can use Networkx to create a graph of the polygons and then use the shortest_path function to find the shortest path between each pair of polygons.

Step 1: Create a Graph

First, we need to create a graph of the polygons. We can use the Graph class from Networkx to create a graph.

import networkx as nx


G = nx.Graph()

Step 2: Add Edges to the Graph

Next, we need to add edges to the graph. We can use the add_edge function to add an edge between each pair of polygons.

# Add edges to the graph
for i in range(len(gdf)):
    for j in range(i+1, len(gdf)):
        G.add_edge(i, j)

Step 3: Find the Shortest Path

Finally, we can use the shortest_path function to find the shortest path between each pair of polygons.

# Find the shortest path
shortest_paths = nx.shortest_path(G)

Step 4: Group by Distance

We can then use the groupby function to group the polygons by distance. We can use the shortest_paths dictionary to get the shortest path between each pair of polygons.

# Group by distance
gdf_grouped_distance = gdf.groupby(shortest_paths)

Step 5: Create a Unique Group ID

Finally, we can create a unique group ID for each group of polygons. We can use the ngroup function to assign a unique integer to each group.

# Create a unique group ID
gdf_grouped_distance['group_id'] = gdf_grouped_distance.ngroup()

Conclusion

In this article, we have shown how to group polygons by attributes and by distance using GeoPandas and Networkx. We have used a parcel dataset containing 115k records as an example. We have demonstrated how to group polygons by attributes using the groupby function and how to group polygons by distance using Networkx. We have also shown how to create a unique group ID for each group of polygons using the ngroup function.

Example Use Cases

This article has several example use cases. For example, we can use this technique to group parcels by type and then create a unique group ID for each group of parcels. We can also use this technique to group parcels by distance and then create a unique group ID for each group of parcels.

Code

The code for this article is available on GitHub. You can download the code and run it on your own machine to see the results.

References

Future Work

Q: What is GeoPandas and how does it relate to grouping polygons?

A: GeoPandas is a library that allows you to easily work with geospatial data in pandas data structures. It provides a powerful and flexible way to handle geospatial data, including polygons, points, and lines. GeoPandas is particularly useful for grouping polygons by attributes, such as the type of parcel or the owner of the parcel.

Q: What is Networkx and how does it relate to grouping polygons by distance?

A: Networkx is a library for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. In the context of grouping polygons by distance, Networkx is used to create a graph of the polygons and then find the shortest path between each pair of polygons.

Q: How do I load a parcel dataset into GeoPandas?

A: You can use the read_file function to load a parcel dataset into GeoPandas. For example:

import geopandas as gpd


gdf = gpd.read_file('parcels.shp')

Q: How do I group polygons by attributes using GeoPandas?

A: You can use the groupby function to group polygons by attributes. For example:

# Group by the type of parcel
gdf_grouped = gdf.groupby('parcel_type')

Q: How do I create a unique group ID for each group of polygons?

A: You can use the ngroup function to assign a unique integer to each group. For example:

# Create a unique group ID
gdf_grouped['group_id'] = gdf_grouped.ngroup()

Q: How do I group polygons by distance using Networkx?

A: You can use Networkx to create a graph of the polygons and then find the shortest path between each pair of polygons. For example:

import networkx as nx


G = nx.Graph()



for i in range(len(gdf)):
for j in range(i+1, len(gdf)):
G.add_edge(i, j)



shortest_paths = nx.shortest_path(G)



gdf_grouped_distance = gdf.groupby(shortest_paths)

Q: What are some common use cases for grouping polygons by attributes and by distance?

A: Some common use cases for grouping polygons by attributes include:

  • Grouping parcels by type and then creating a unique group ID for each group of parcels
  • Grouping parcels by owner and then creating a unique group ID for each group of parcels

Some common use cases for grouping polygons by distance include:

  • Grouping parcels by distance from a central location and then creating a unique group ID for each group of parcels
  • Grouping parcels by distance from a road or other feature and then creating a unique group ID for each group of parcels

Q: What are some tips for working withPandas and Networkx?

A: Some tips for working with GeoPandas and Networkx include:

  • Make sure to load the necessary libraries and import the necessary functions before starting your analysis
  • Use the groupby function to group polygons by attributes and the shortest_path function to find the shortest path between each pair of polygons
  • Use the ngroup function to assign a unique integer to each group
  • Use the read_file function to load a parcel dataset into GeoPandas
  • Use the add_edge function to add edges to the graph in Networkx

Q: Where can I find more information about GeoPandas and Networkx?

A: You can find more information about GeoPandas and Networkx on the following websites: