%0 Journal Article %A Lifan SHEN %A Chun ZHANG %A He LI %A Ye WANG %A Zijia WANG %T Relationship between urban rail transit commuting and jobs-housing balance: An empirical analysis from Beijing based on big data methods %D 2019 %R 10.18306/dlkxjz.2019.06.001 %J PROGRESS IN GEOGRAPHY %P 791-806 %V 38 %N 6 %X

The development of urban rail transit (URT) network improves the commuting efficiency of residents while it has a certain impact on their jobs-housing balance. This study took 206 URT stations in Beijing as an example and classified them according to their jobs-housing functions based on the Gaussian mixture model (GMM) and smart card data. The dynamic population distribution characteristics around URT station were explored and jobs-housing ratio was calculated by "Yichuxing" position data. The study found that: 1) The jobs-housing balance in the central city is obviously better than that outside of the central city. 2) At the ends of the URT network, the jobs-housing balance is worse while only a few stations with concentrated distribution of top service industries have formed regional employment centers. 3) There still exists a certain degree of jobs-housing mismatch in the areas around some suburban stations where employment and residential functions are relatively equal. Station outflow-inflow and jobs-housing balances were calculated by the station egrass-ingrass ratio and the jobs-housing ratio, and the correlation between URT commuting behavior and jobs-housing balance was analyzed by generalized autoregressive conditional heteroskedasticity (GARCH) model. The results of this study indicate that: 1) There is a very strong positive relationship between URT station egrass-ingrass balance and jobs-housing balance. The closer the numbers of URT station outflow and inflow population, the better the jobs-housing balance around the URT station is. 2) There is a strong positive relationship between employment opportunity and jobs-housing balance around a URT station; and there is a strong negative relationship between residential function and jobs-housing balance around a URT station. This suggests that dense settlement will not generate the same quantity of jobs while well-developed employment hubs can attract a certain number of residents to live nearby. 3) There is a positive correlation between locational conditions of URT stations and jobs-housing balance. 4) The GMM can effectively cluster URT stations with complex and unclear attributes. 5) With its advantages of real-time data capturing, high precision, wide coverage, and great accessibility, "Yichuxing" position data can effectively compensate for the limitations of other methods on collecting and analyzing spatial-temporal characteristics of real-time population distribution at the microscopic scale.

%U https://www.progressingeography.com/EN/10.18306/dlkxjz.2019.06.001