Abstract

High-speed railway (HSR) stations play an important role in shaping the development/redevelopment of the surrounding areas. While different studies have been conducted to explore station area development, they tend to simplify the conceptualisation of station area development and lack a comprehensive perspective that captures how different variables at multiple levels are interacted with station area development. This research proposes the ‘node-place-network-city’ framework, an innovative extension of the traditional ‘node-place’ model, to analyse station area development. Focusing on 123 HSR station areas in the Yangtze River Delta as of 2018, this research employs spatial big data to explore the intricate relationships between various city and network indicators and station area development. Unlike previous studies that have typically simplified station area development and treated the relationships as linear, this study adopts a decision tree model. This method effectively identifies diverse typologies of HSR stations and facilitates the formulation of tailored development strategies for each type. This research significantly contributes to the theoretical understanding of HSR station area development and provides valuable and actionable insights for urban planners and policymakers. It marks a step forward in comprehensively assessing and guiding the development of HSR station areas, thereby offering a robust framework for both academic research and practical application in spatial planning.

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