Differences in the impact of leisure consumption spaces on urban residents' life satisfaction: An empirical analysis based on social media big data.

Abstract

As residents' demand for leisure consumption spaces continues to grow, the development of these spaces influences their perception of urban environments and life satisfaction. To examine how different urban leisure consumption spaces affect life satisfaction, we analyze service quality and life satisfaction using Dianping and Weibo Sign-in data through deep learning methods like Feature Tokenizer Transformer, then evaluate the relative importance of service quality's impact. Results show that high service quality significantly enhances life satisfaction, while the quantity of spaces has negligible effect. Among different space types, Catering exerts the strongest influence on life satisfaction, followed by Entertainment, Personal care, Retail, and Sports, with regional and functional variations in these effects. This systematic study using multi-source big data and deep learning enriches media geography and spatial behavior theories while providing references for optimizing urban functional layout and public service policies.