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<p>This study employs a two-staged structural equation modeling-artificial neural network (SEM-ANN) approach to examine the necessity of urban spatial restructuring (USR) for implementing the 15-Min City (15MC) framework and its impact on quality of life (QoL) in Gwalior, a Tier Two Indian city. Data collected from 457 respondents reveals that USR significantly enhances the implementation of the 15MC framework and QoL. The results highlight that spatial accessibility to amenities (SAA) mediates these relationships, underscoring the importance of proximity to essential services in improving urban living standards. Socioeconomic status (SS) moderates these relationships, with higher SS amplifying the positive impacts of USR and 15MC on QoL. The study demonstrates the utility of combining linear and non-linear modeling techniques to provide a comprehensive understanding of factors influencing QoL in urban areas. By identifying key drivers of livable urban environments, this research contributes to evidence-based decision-making for sustainable urban development.</p>

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Transforming urban spaces for 15-min city implementation: a two-stage SEM-ANN approach to enhancing quality of life

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月3日

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  • Swati Agrawal,
  • Sanjay Singh Jadon

摘要

This study employs a two-staged structural equation modeling-artificial neural network (SEM-ANN) approach to examine the necessity of urban spatial restructuring (USR) for implementing the 15-Min City (15MC) framework and its impact on quality of life (QoL) in Gwalior, a Tier Two Indian city. Data collected from 457 respondents reveals that USR significantly enhances the implementation of the 15MC framework and QoL. The results highlight that spatial accessibility to amenities (SAA) mediates these relationships, underscoring the importance of proximity to essential services in improving urban living standards. Socioeconomic status (SS) moderates these relationships, with higher SS amplifying the positive impacts of USR and 15MC on QoL. The study demonstrates the utility of combining linear and non-linear modeling techniques to provide a comprehensive understanding of factors influencing QoL in urban areas. By identifying key drivers of livable urban environments, this research contributes to evidence-based decision-making for sustainable urban development.