<｜begin▁of▁sentence｜>### Instruction:
As a GIS Specialist, you're likely familiar with structuring spatial data into layers that can be individually analyzed and manipulated. Imagine you have a network dataset representing connectivity between different points of interest within a city, with roads or paths as connections. 

The network consists of points labeled as 'A', 'B', 'C', 'D', and 'E', with paths connecting these points as follows: there's a path from 'A' to 'B', from 'B' to 'C', from 'A' to 'C', from 'C' to 'D', and from 'D' to 'E'. No path is duplicated, and no point has a path to itself. 

In the context of your GIS analysis, how could you utilize NetworkX to categorize each point into stratified layers based on their connectivity? This method, akin to peeling an onion, would allow you to determine the 'onion layers' where each subsequent layer is defined by those nodes that are not in a lower layer and that have a minimum degree, considering only the nodes not yet placed in a layer.

Think of this as organizing your spatial network into a series of concentric zones, each representing a different level of connectedness within the overall structure. How would you rephrase this task in your day-to-day work when dealing with spatial databases and geographic analysis using NetworkX as your tool of choice?
### Response:
import networkx as nx
from networkx.algorithms import onion_layers

# Create an undirected graph without self loops or parallel edges
G = nx.Graph()
G.add_edges_from([('A', 'B'), ('B', 'C'), ('A', 'C'), ('C', 'D'), ('D', 'E')])

# Compute the onion layers
od_layers = onion_layers(G)

print(od_layers)
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