<｜begin▁of▁sentence｜>### Instruction:
As a civil engineer overseeing the construction of a new city district, you're tasked with ensuring efficient road networks. To optimize traffic flow, you need to understand how intersections connect and how many roads lead to each intersection.

Using advanced network analysis tools, like the "RoadNetX" API, you can generate a model representing the road network. For instance, let's consider a hypothetical scenario where you're designing a network of 100 intersections. Each intersection connects to other intersections randomly with a probability of 0.1, reflecting the likelihood of road connections between them.

Your task now involves calculating and presenting the distribution of the number of roads leading to each intersection. This distribution will help you identify critical intersections with high connectivity, which might require special attention in your road design to manage traffic effectively.
### Response:
import igraph as ig

# Create a graph
g = ig.Graph.Erdos_Renyi(n=100, p=0.1)

# Calculate the degree distribution
dd = g.degree_distribution()

# Print the degree distribution
data = [(left, right, count) for left, right, count in dd.bins()]
print(data)
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