Research on Spatiotemporal Measurement and Influencing Factors of Vitality around Metro Stations in the Main Urban Area of Guangzhou,China
WANG Chengfang
GAO Fuli
LU Peijun
WU Zichao
YANG Ruomei
Abstract：In the era of stock-based planning,the construction of high-capacity rail transit terminals represented by metro systems brings both opportunities and challenges for urban spatial redevelopment.Enhancing the overall spatial vitality of station areas and promoting efficient construction in these zones are of great significance when it comes to improving urban spatial quality.This study engages in systematic spatiotemporal measurement of the vitality of metro stations and spheres of influence,using Baidu heatmap data to analyze the complex mechanisms linking built environment factors,weekly average vitality,vitality dispersion,and scenario-specific vitality in the spheres of influence of 164 metro stations in the main urban area of Guangzhou,China.An index system covering locations,accessibility,land use,functions,and development intensity was established.Sensitivity factors which influence the vitality of metro stations were explored and analyzed thoroughly according to field surveys at 18 sampling stations.Methodologically,the study innovatively introduces the Bayesian Structural Equation Model(BSEM)to address issues related to small sample sizes and non-normal data.Three structural equation models,including the"Weekly Average Vitality Model",the"Vitality Dispersion Model",and the"Weekday Average Vitality Model",were constructed to identify underlying mechanisms.Additionally,functional density was disaggregated into six categories of point of interest(POI)density for supplementary validation.
The main findings are as follows:1)All three models consistently reveal a hierarchical relationship:"Location/Accessibility/Land Use → Functional Characteristics/Development Intensity → Vitality".Among these relationships,the direct effect of functional density and degree of mix on vitality intensity is the most prominent.2)Key driving factors can be identified:accessibility plays a significant upstream driving role in shaping functional characteristics and development intensity.Development intensity exhibits an amplifying effect on vitality in weekday scenarios,but contributes little to vitality stability.3)The mechanism that stabilizes vitality can be characterized:the spatial stability of vitality shows significant dependence on location.It is important to note that density of commercial-service POIs has significantly positive effects on vitality,but there's no statistically significant correlation between the macro-level proportion of commercial land use and vitality level.This indicates that micro-level functional configuration has a more direct impact on vitality than macro-level land use zoning.4)The validity of the analytic methods used in the study can be established:BSEM provides stable estimations based on prior distributions and posterior sampling of Markov Chain Monte Carlo(MCMC),which allows BSEM to avoid the reliance on large sample size and normality that is characteristic of traditional structural equation models.BSEM is therefore applicable for mechanism identification of complex urban systems.
Based on these conclusions,the study argues that it is necessary to formulate robust strategies to improve the vitality of metro station areas in the most complex and mixed-use urban spaces by taking into account strongly correlated influencing factors.It is recommended that planners prioritize the configuration of functional density and mixed uses,formulating differentiated development intensity and functional strategies according to location conditions and the relative accessibility of station areas.In this connection,Brisbane's land planning and control policies,which consider different zones and hierarchical characteristics of influencing factors,provide a useful example.Future studies should expand the model by undertaking multi-city horizontal comparison and longitudinal tracking via long time series,while integrating machine-learning methods like gradient boosting decision trees(GBDTs)to further explore nonlinear relationships among influencing mechanisms.Overall,this study aims to deepen localized transit-oriented development(TOD)theories and improve planning practices for high-density urban context in China.
Keywords：metro station areavitality measurementheatmapBayesian structural equation modelinginfluencing factors
Publication Date：2025-12-30
Online Publishing Date：2026-01-08(First online date of this platform, not the publication date of the document)
Pages：10( 10-19 )