Speaker
Description
As urbanization in China transitions from quantitative expansion to qualitative improvement, understanding the dynamics of residents’ time use has become critical for addressing diverse and evolving needs. Time, as a finite resource, serves as a crucial indicator of quality of life and lifestyle patterns, offering insights into individual and household behaviors across different family life cycle stages. However, existing research often overlooks the dynamic interactions between time use patterns and the built environment, particularly within the lens of family life cycles.
This study employs a Markov chain approach to explore the temporal dynamics of time use among Shanghai residents across various family life cycle stages. Key family life cycle nodes—such as nuclear families, parenting households, and empty-nest households—are identified and used to construct time-use transition probability matrices, leveraging data from the 2018 Shanghai Time Use Survey. By integrating built environment characteristics (e.g., accessibility to community facilities and transportation networks) with virtual space behaviors (e.g., online entertainment, remote work), this research examines how spatial and virtual dimensions influence time allocation and perceived quality of life.
The findings reveal distinct time use patterns across family life cycles. Parenting households face significant time constraints due to commuting and caregiving responsibilities, leading to reduced leisure time and a heightened risk of decreased quality of life. In contrast, empty-nest households exhibit higher participation in leisure activities and report greater life satisfaction. Built environment factors, such as proximity to amenities and efficient transportation networks, play a pivotal role in alleviating time pressures, while virtual space behaviors expand activity options and contribute to the fusion of physical and digital spatial dimensions.
This research contributes to the discourse on inclusive urban planning by offering actionable insights and policy recommendations. Specifically, it advocates for the optimization of public service facility distribution to cater to the diverse needs of households at different life cycle stages. Additionally, it highlights the importance of integrating digital technologies with traditional urban planning practices to foster social equity and inclusive development. Future research will further investigate the evolutionary mechanisms of spatiotemporal behaviors using Markov chain models, providing new perspectives for dynamic urban planning and policy design.
| Keywords | time use; family life cycle; Markov chain; inclusive planning |
|---|---|
| Best Congress Paper Award | No |
