<｜begin▁of▁sentence｜>### Instruction:
As a Conservation Scientist, you're tasked with managing natural resources and ecosystems to uphold biodiversity and ecological equilibrium. Imagine you're studying a population of interconnected habitats forming a ring-like structure. Each habitat represents a vertex, and the connections between them symbolize ecological corridors. To assess the importance of each habitat in maintaining connectivity, you need to calculate its "closeness centrality." This metric gauges how efficiently a habitat can access others within the network. Utilizing the NetworkX library, you can compute the closeness centrality of each habitat and visualize these values to prioritize conservation efforts effectively.
### Response:
from igraph import Graph

# Create a graph
vertex_count = 5
g = Graph.Ring(vertex_count)

# Calculate closeness for each vertex
closeness_values = [vertex.closeness() for vertex in g.vs]

# Print closeness values
for i, value in enumerate(closeness_values):
    print(f'Vertex {i}: Closeness = {value}')
<|EOT|>
