Paper
14 June 2023 Identification and trend analysis of urban shrinkage in Liaoning province based on grey theory
Weihao Zhang, Tingting Yan, Zihao Tan, Ziyan Wang
Author Affiliations +
Proceedings Volume 12725, International Conference on Pure, Applied, and Computational Mathematics (PACM 2023); 1272505 (2023) https://doi.org/10.1117/12.2678961
Event: International Conference on Pure, Applied, and Computational Mathematics (PACM 2023), 2023, Suzhou, China
Abstract
Through the construction of urban shrinkage evaluation indicators, we analyze 30 cities in Liaoning Province. We divide the degree of urban shrinkage according to the evaluation index system. Through the construction of population economic elasticity model, quantitative analysis is made on the impact of various factors in the economic society on the population change of the shrinking city. The results show that there are 5 prefecture-level cities and 13 county-level cities with moderate shrinkage, and 6 prefecture-level cities with severe shrinkage. Liaoning's economic growth of 1%, the population size will be reduced by 2%, indicating that most cities in Liaoning Province are smart shrinking cities. In addition, 12 typical shrinking cities are selected as the main research objects. After 12 time series pass the rank comparison test, a mathematical model is established based on GM (1,1) grey prediction model. Then we predict the urban comprehensive index and urban shrinkage of 12 prefecture level cities.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Weihao Zhang, Tingting Yan, Zihao Tan, and Ziyan Wang "Identification and trend analysis of urban shrinkage in Liaoning province based on grey theory", Proc. SPIE 12725, International Conference on Pure, Applied, and Computational Mathematics (PACM 2023), 1272505 (14 June 2023); https://doi.org/10.1117/12.2678961
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KEYWORDS
Shrinkage

Mathematical modeling

Data modeling

Data conversion

Elasticity

Reflection

Error analysis

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