Scholarlink

<p>Family has been playing a critical role in SDG 11 implementation, serving as a key intersection point between urban social governance and spatial governance. However, supposed family-friendly cities remains predominantly framed as a social policy concern rather than a core urban planning paradigm, simultaneously focusing on the micro scale rather than the urban macro scale, with limited translation into substantive urban planning transformation for family-friendly city. Accordingly, we have developed a comprehensive applicable family big data-driven theoretical-methodological system for decoding family-environment interaction dynamics and family-friendly cities. Through an empirical case study of family-friendly urban park planning in Chinese megacity Wuhan, we established the following key insights: (1) A multi-source methodological framework for capturing family daily big data and converting into family-level behavioral signatures; (2) Quantitative characterization approach of family-environment interaction dynamics based on family big data; (3) A multidimensional Family-Friendly Index (FFI) to evaluate the family-friendliness level of city; (4) Identification system for critical environmental determinants constraining family-friendly city governance; (5) Big data-driven optimization protocols model for family-friendly urban planning. Our research serves as a foundation platform to attract more global scholarly understanding and practical applications, contributing to advance continuous evolution of family-friendly urban planning paradigms for SDG 11.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Insights into family-friendly cities for SDG11: a theoretical and methodological system based on family-environment interaction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月30日

查看全文
  • Hao Zhang,
  • Qiang Niu,
  • Dongming Zhou,
  • Wenqi Fu,
  • Lei Wu

摘要

Family has been playing a critical role in SDG 11 implementation, serving as a key intersection point between urban social governance and spatial governance. However, supposed family-friendly cities remains predominantly framed as a social policy concern rather than a core urban planning paradigm, simultaneously focusing on the micro scale rather than the urban macro scale, with limited translation into substantive urban planning transformation for family-friendly city. Accordingly, we have developed a comprehensive applicable family big data-driven theoretical-methodological system for decoding family-environment interaction dynamics and family-friendly cities. Through an empirical case study of family-friendly urban park planning in Chinese megacity Wuhan, we established the following key insights: (1) A multi-source methodological framework for capturing family daily big data and converting into family-level behavioral signatures; (2) Quantitative characterization approach of family-environment interaction dynamics based on family big data; (3) A multidimensional Family-Friendly Index (FFI) to evaluate the family-friendliness level of city; (4) Identification system for critical environmental determinants constraining family-friendly city governance; (5) Big data-driven optimization protocols model for family-friendly urban planning. Our research serves as a foundation platform to attract more global scholarly understanding and practical applications, contributing to advance continuous evolution of family-friendly urban planning paradigms for SDG 11.