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Transport is important as a means by which people engage in the economy and interact socially. It is important to understand the complex relationships between transport properties and other geographic measures in order to design integrated smart city solutions. In this paper, we apply complex network analysis to better understand the London underground and overground rail network and uncover its relation to how citizens make certain lifestyle choices and other urban geography features. We propose to apply Neighbourhood Centrality, which aggregates the network centrality values in a geographic area. We uncover that the neighbourhood eigenvector centrality can explain for up to 79% of the variations in: (1) the age demographics, (2) the choice in transportation mode, and (3) the price and choice of housing. This sheds light on some of the complexities that surround public transport networks and its relation to urban human geography, and can prove useful for the design of future smart city transport systems.
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Stored only when both papers are in the corpus — 8 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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