Study on Urban Shrinkage Based on Entropy Weight-TOPSIS and GCA Method

This paper aims to explore the problem of urban shrinkage by establishing a multi-index evaluation model. Taking 30 cities in Liaoning Province as an example, six indicators are established which can effectively reflect the shrinking city from the dimensions of population, economy, society and industry. The six indicators are: population growth rate, GDP growth rate, per capita GDP, employment growth rate of secondary and tertiary industries, per capita total social consumption and per capita public budget expenditure. Firstly, the weights of 6 indexes are determined by entropy weight method. Then the shrinkage degree of 30 cities is obtained by TOPSIS method. Based on the evaluation results of the model, five shrinkage intervals were established to classify the cities, namely, severe shrinkage, moderate shrinkage, mild shrinkage, slight shrinkage, and critical or growing cities. Secondly, a multiple regression prediction model is established for the relationship between the six indexes and the urban shrinkage degree. And the shrinkage degree is predicted and analyzed. Then, the grey prediction model is used to test the results, and the evolution trend of shrinking cities is obtained. The results show that the prediction accuracy of grey prediction and multiple regression prediction is up to 92.3%, which greatly improves the accuracy and reliability of the evolution trend prediction results.