Enhancing economic competitiveness analysis through machine learning: Exploring complex urban features.

ABSTRACT: Urban economic competitiveness is a fundamental indicator for assessing the level of urban development and serves as an effective approach for understanding regional disparities. Traditional economic competitiveness research that relies solely on traditional regression models and assumes feature relationship theory tends to fall short in fully exploring the intricate interrelationships and nonlinear associations among features. As a result, the study of urban economic disparities remains limited to a narrow range of urban features, which is insufficient for comprehending cities as complex systems. The ability of deep learning neural networks to automatically construct models of nonlinear relationships among complex features provides a new approach to research in this issue. In this study,

SUBMITTER: Xu X 

PROVIDER: S-EPMC10629647 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature