Optimization Study of Greening and Ecological Effect in Public Space Based on Big Data Analysis

Abstract

People are paying increasing attention to urban environmental quality issues. Future urban competition will not be about speed, height, or scale; instead, concepts such as green, ecological, low-carbon, and sustainable development will be the themes of future urban development. This article analyzes the ecological effects of public green spaces, examines the mechanisms of green space elements, and proposes strategies for creating green public spaces. Taking the public space greening environment of the Pearl River Delta as the research object, this study constructs a public space greening information extraction model based on an improved SRResNet network and a global-local cross-attention mechanism. The model's effectiveness is validated through a dataset, and the optimization effects, ecological effects, and green view rate satisfaction of public space green spaces are analyzed using the SD method and correlation analysis. The results show that the overall accuracy and Kappa coefficient of this model are 93.75% and 0.92, respectively, which are superior to those of the comparison algorithms. The average SD score for public space greening in the Pearl River Delta is 2.47, indicating that the overall ecological effects of public space greening are good, and a green visibility rate of over 29% can significantly improve residents' satisfaction.