Abstract: The degree of spatial integration of metro station areas is evaluated within the context of urban renewal to identify driving mechanisms. A fusion-degree evaluation model is developed, consisting of 16 indicators within three dimensions (nodes, places, and spaces) based on 18 metro station areas in the Shinan district of Qingdao, China. Based on road network data, ArcGIS spatial network analysis is used to delineate the spatial scope of each station area with a 15-min walking distance threshold. The entropy method is used to objectively determine the weights of indicators, and the key factors influencing the degree of spatial integration of metro station areas are identified based on multiple linear stepwise regression analysis. The 18 station areas are classified into four categories—renovation and upgrading old residential areas (Category A), renovation and upgrading historical urban areas (Category B), renovation and upgrading central urban areas (Category C), and protection and utilization of old residential areas (Category D)—according to the Special Urban Renewal Plan of Qingdao City. The evaluation results indicate distinct differences in the average degrees of integration for the four station area types; Category A achieves the highest score (0.478), followed by Category B (0.421), Category D (0.380), and Category C (0.293). At the single-station level, Qingdao station has the highest integration degree (0.837), whereas the Ningxia Road station has the lowest (0.141). Weighting analysis reveals that the primary factors influencing the integration degree depend on the category type: node configuration and connection efficiency in Category A; transfer lines and functional density in Category B; transfer lines in Category C; and above-ground commercial density and functional density in Category D. Regression analysis results further identify functional density, node configuration, and intensity as the three primary factors influencing the degree of spatial integration of passenger flow in station areas.

Key words: urban renewal, metro station areas, spatial integration, entropy weight method, evaluation model