How identify if polygons from layer A are in same polygon from layer B in Python

Question

I have two polygon layers, layer A and layer B. Layer B polygons are much bigger than layer A polygons. Now, some of the little polygons from layer A are located in the big polygons from layer B, and some are not. And so, for those little polygons from layer A that are located within the big polygons from layer B, some of those little layer A polygons will be located within different big polygons from layer B. That is the setting here.

Each layer A polygon has a unique Object ID. Same with the layer B polygons.

What I want to do is create a new column in the layer A attribute table called "B_ID".

In this column, and so for each layer A polygon/row, I want to enter the object ID of the layer B polygon it is located within. And so let's say Polygons 24 and 55 from layer A are both located within polygon 75 from layer B. Those rows in the layer A attribute table for polygons 24 and 55 would then show a "75" value under the "B_ID" column. And then, if a polygon from layer A happens to not be located within any layer B polygon, then just it would just show a "FALSE" or "N/A" or some rejection-value for that row under the "B_ID" column.

Here is a diagram example of what I am trying to accomplish:

From this I want to produce the following dataframe/table for layer A:

Object_ID     B_ID
------------------------------------
0                ?
1                ?
2                ?
3                ?
...
24              75
...
33             N/A
...
36             N/A
...
41             N/A
...
55              75
56              73  
57              73
58              73
...
61              76
62               ?
63               ?
64               ?
...

And so we see which polygons from layer B the polygons from layer A are located within.

How can this be approached in Python? I am thinking this will need to involve the .intersects() function, but I am confused how to reference location within polygons from a different layer and reference it back to the original layer. I am generally confused how to tell Python what exactly to intersect and how to pull the appropriate object ID from layer B.

Answer 1

It seems like you're trying to do a special type of spatial join.

To do that using geopandas, you'd have to do something like this:

import geopandas as gpd

# Suppose the unique ID column for layer_a is called "A_ID"
layer_a = gpd.read_file(...)

# Suppose the unique ID column for layer_b is called "B_ID"
layer_b = gpd.read_file(...)

# Finding the match between layer_a and layer_b
sjoin_results = layer_a.sjoin(layer_b[['B_ID','geometry']], how='left', predicate='within')

The sjoin_results should be a GeoDataFrame that has all of the columns of the original layer_a object with a new column: "B_ID".