Study On The Selection Of Financial Centers Based On The Methods Of Spatial Statistical Analysis And Combined Evaluation

In this thesis, firstly, we apply spatial statistical analysis method to exam the phenomenon of regional financial agglomeration in China. Secondly, we establish an evaluation index system of urban financial competitiveness, and use several objective evaluation methods to evaluate the financial competitiveness of cities as financial centers in China, and then conduct a combined evaluation, giving the ultimate ranking. Finally, we divide these cities into groups at different levels by applying cluster analysis. The aim of this thesis is to provide reference values for the selection and layout of financial centers in China. The main works are as follows:1. Spatial statistical analysis method is successfully applied to the study on financial agglomeration in China. The research results show that:Although the spatial distribution of regional economic growth in China shows a statistically significant clustering, the phenomenon in financial development is not yet significant, and financial agglomeration is far behind the spatial agglomeration in economic growth; the level of spatial agglomeration in regional financial development in China is increasing, and provinces or cities with a high level of financial development can promote financial development of the provinces or cities around.2. A combined evaluation method is successfully applied to the study on the evaluation of urban financial competitiveness in China. As the construction of financial center is a multi-level, multi-objective, multi-factor and complex structural systems engineering, we establish an evaluation index system of urban financial competitiveness. Then, we use several objective evaluation methods separately, including entropy weight method, grey relational analysis and principal component analysis, to evaluate the financial competitiveness of cities as financial centers in China, and considering the different results produced by different methods, we conduct a combined evaluation, giving the ultimate ranking of the urban financial competitiveness.3. The K-means clustering analysis method is applied to the level classification of cities as financial centers. Based on the findings of the urban financial competitiveness and the current situation of regional economic growth and financial development in China, we put forward some policy proposals for the construction of multilayer financial center system in China.