Accessibility Cluster Analysis of Subway Station Based on Spatial Big Data——A Case Study of Dongcheng and Xicheng Districts in Beijing City

Abstract

Subway is an important means of daily commuting in city life due to its punctuality and speed. Residential accessibility around subway station reflects the transportation convenience and connectivity between the necessary facilities which affecting residents’ daily lives. Therefore, this study research on station accessibility factors by improving the walk-score model and establishing a multi-feature integrated transportation model that comprehensively considers the age difference based on spatial big data. Quantitative analysis was conducted on facility and station accessibility. Based on clustering algorithm considering three age groups, subway stations were classified into four types: mature, well-equipped, nurturing, and deficient. Using friendly characteristics, subway stations were categorized into three dominant age types. By integrating the analysis of accessibility, spatial layout, clustering differences and age-friendly characteristics, suggestions were proposed to improve station connectivity and supporting facility development.