<｜begin▁of▁sentence｜>### Instruction:
Imagine you are analyzing the movement patterns of a small community that is spread across four key locations: Area A, Area B, Area C, and Area D. These areas are connected by various pathways that people typically use to travel from one area to another. The pathways between Area A and B, A and C, B and C, and C and D have been assigned "congestion scores" based on previous surveys, reflecting how frequented they are. The scores are 2 for the path from Area A to B, 1 for the path from Area A to C, 1 for the path from B to C, and 1 for the path from C to D.

In order to strategize improvements for local infrastructure and to assist in future planning, you are tasked with evaluating which pathways are most critical when community members travel specifically from Area A to Area D. Understanding these critical pathways will help us appreciate the flow and distribution of population movement under this specific travel scenario. Could you please use your expertise to determine the relative importance of each pathway within this context, which in technical terms is known as the edge current flow betweenness centrality from Area A to Area D? This assessment will be invaluable for developing effective population density management strategies in our study areas.
### Response:
import networkx as nx

# Create a weighted graph
G = nx.Graph()
G.add_edge('A', 'B', weight=2)
G.add_edge('A', 'C', weight=1)
G.add_edge('C', 'B', weight=1)
G.add_edge('C', 'D', weight=1)

# Define sources and targets
sources = ['A']
targets = ['D']

# Compute edge current flow betweenness centrality
betweenness = nx.edge_current_flow_betweenness_centrality_subset(
    G, sources, targets, normalized=True, weight='weight', dtype=float, solver='lu'
)

# Print the edge betweenness centrality
for edge, centrality in betweenness.items():
    print(f'Edge {edge}: {centrality}')
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