<｜begin▁of▁sentence｜>### Instruction:
Imagine you're tasked with analyzing satellite imagery to monitor the interconnectivity of different land areas, represented by various vegetation patterns and their relationships to nearby regions, each exhibiting unique water retention capacities. Envision these relationships as a conceptual bipartite network where sets A and B correspond to two distinct types of terrains or land uses you're currently studying; set A includes regions with the degree sequence [3, 2, 1, 1], indicating varying levels of connectivity to water resources, and set B includes those with a degree sequence of [1, 2, 2, 2], suggesting different intensities of vegetation.

To simulate the interconnectivity between these regions in a more structured manner, could you employ the Havel-Hakimi algorithm available in NetworkX to design a hypothetical bipartite graph? Furthermore, proceed to render a graphical visualization of this network analogy, which could potentially assist in anticipating environmental patterns or predicting the flow of resources among these areas. Remember to integrate the provided degree sequences into this practical scenario without deviating from the original semantic context of the task.
### Response:
import networkx as nx

# Degree sequences for both sets
aseq = [3, 2, 1, 1]
bseq = [1, 2, 2, 2]

# Use Havel-Hakimi algorithm to create the bipartite graph
B = nx.bipartite.havel_hakimi_graph(aseq, bseq)

# Draw the graph
import matplotlib.pyplot as plt
pos = nx.bipartite_layout(B, nodes=[0, 1, 2, 3])
nx.draw(B, pos, with_labels=True)
plt.show()
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