<｜begin▁of▁sentence｜>### Instruction:
Imagine you're mediating a complex logistical challenge for a transportation network consisting of various routes with assigned weights based on travel time or cost. The network in question involves a set of routes, specifically [(Route A-B, 10 units), (Route B-C, 5 units), (Route C-D, 10 units)], where each route connects two locations and the units signify the weight of the route, perhaps in minutes or fuel cost.

Your task is to optimize the flow of traffic through this network by identifying an arrangement of these locations. In this process, you aim to sequence locations that are interconnected without any barriers (or weakly linked areas) in such a way that those within the same sector are grouped together and further arranged internally based on a specialized metric known as the Fiedler value—a measure arguably related to traffic fluidity within each sector.

Could you counsel on how this specialized sequencing can be achieved using a tool like NetworkX, which specializes in the analysis of complex networks? The goal is to maintain the practical framework you're accustomed to while utilizing this computational approach to enhance the efficiency and harmony of the network's operation.
### Response:
import networkx as nx
import numpy as np

# Create a graph
G = nx.Graph()
G.add_weighted_edges_from([(1, 2, 10), (2, 3, 5), (3, 4, 10)])

# Compute the spectral ordering
ordering = nx.spectral_ordering(G, weight='weight', normalized=True, tol=1e-8, method='tracemin_pcg', seed=None)

print('The spectral ordering of the nodes is:', ordering)
<|EOT|>
