基于动态因子分析法的长江三角洲城市群城镇化质量测度研究

如何对多维数据进行降维处理在大数据时代显得愈加重要。传统的数据降维方法无法同时了解所有的动态变化趋势，因而具有一定的局限性。采取针对面板数据而设计的动态因子方法，以长江三角州城市群城镇化质量测度为例进行多维社会数据降维实验，结果发现该方法能够较为准确的识别多维数据中能够同时影响多个变量的“潜变量”，并可以刻画各个样本个体的“潜变量”随时间的变化趋势以及样本整体的变化趋势。结合案例研究结果可以发现，长江三角洲城市群同样存在人口城镇化和土地城镇化不协调的问题，需要加快新型城镇化建设，着重提升城镇化质量。

Study on the Measurement of Urbanization Quality of Yangtze River Delta Urban Agglomeration Based on Dynamic Factor Analysis

How to reduce the dimension of multi-dimensional data becomes more and more important in the era of big data. The traditional method of data dimensionality reduction can not understand all the dynamic trends at the same time, therefore has certain limitations. This paper adopts the dynamic factor method designed for panel data, and take the measurement of urbanization quality of Yangtze River Delta urban agglomeration as the case to carry out the dimensionality reduction experiment of multi-dimensional social data It is found that this method can more accurately identify the “latent variables” which can affect multiple variables at the same time in multi-dimensional data, and can describe the variation trend of “latent variable” of each individual sample along with time and that of the whole sample. Combined with the results of case study, this research found that the Yangtze River Delta urban agglomeration also exists the problem of incongruous between population urbanization and land urbanization, which needs to accelerate the construction of New-type urbanization and focus on improving the quality of urbanization.