[口头报告]Investigating the effects of urban morphology on vitality of community life circles using machine learning and geospatial approaches

Investigating the effects of urban morphology on vitality of community life circles using machine learning and geospatial approaches
编号：77 稿件编号：2519 访问权限：仅限参会人 更新：2024-04-09 11:01:04 浏览：499次 口头报告

报告开始：2024年05月18日 15:08 （Asia/Shanghai）

报告时间：10min

所在会议：[S8] 主题8、人文地理与区域高质量发展 » [S8-1] 主题8、人文地理与区域高质量发展 专题8.2、专题8.6、专题8.11（18日下午，4F观海厅2）

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摘要
In examining the associations between urban morphology and urban vitality, it is essential to focus on a scale that is directly observable, tangible, and appreciable. Past studies, however, have primarily concentrated on regional and urban scales, and the complexities of human-environment interactions necessitate a more fine-grained measure of urban morphology. This study captures the human-scale landscapes by employing a bottom-up metric system, encompassing the eye-level quality, road network design, buildings and functions, and neighborhood location. The study then examines the heterogeneous impact and interaction effect of morphological variables on urban vitality at community life circle level. The findings reveal a core-periphery structure and strong spatial autocorrelations between morphological variables and urban vitality. Furthermore, the determinants of urban vitality display a blend of global, regional, and local spatial contexts. Notably, human-observed greenery, sky openness, and public bus accessibility demonstrate significant patterns of spatial heterogeneity, whereas street betweenness and commercial facilities exhibit limited effects. The interaction effects between morphological variables can either enhance or weaken the individual impacts, highlighting the need for community life circle planning considering the combined effect of morphological variables. This study offers new insights for promoting urban design centered on revitalization and micro-renewal at community life circle level.

 
关键字
urban morphology; urban vitality; machine learning; geospatial approaches
报告人
贺三维
教授 中南财经政法大学

稿件作者
贺三维 中南财经政法大学
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