地理科学进展 ›› 2023, Vol. 42 ›› Issue (1): 79-88.doi: 10.18306/dlkxjz.2023.01.007

• 研究论文 • 上一篇    下一篇

城市活力与建成环境的非线性关系和阈值效应研究——以广州市中心城区为例

汪成刚1(), 王波2,3,*(), 王琪智2, 雷雅钦2   

  1. 1.广州市城市规划设计有限公司,广州 510030
    2.中山大学地理科学与规划学院,广州 510275
    3.南方海洋科学与工程广东省实验室(珠海),广东 珠海 519000
  • 收稿日期:2022-06-03 修回日期:2022-08-20 出版日期:2023-01-28 发布日期:2023-03-28
  • 通讯作者: *王波(1987— ),男,湖南衡阳人,博士,副教授,硕士生导师,研究方向为城市地理与区域规划、智慧城市。E-mail: wangbo68@mail.sysu.edu.cn
  • 作者简介:汪成刚(1980— ),男,湖北京山人,硕士,高级工程师,研究方向为城市与区域规划。E-mail: vitovito@qq.com
  • 基金资助:
    国家自然科学基金项目(42271210);国家自然科学基金项目(41901191);广东省自然科学基金项目(2022A1515011572);广州市基础与应用基础研究项目(202102020795)

Nonlinear associations between urban vitality and built environment factors and threshold effects: A case study of central Guangzhou City

WANG Chenggang1(), WANG Bo2,3,*(), WANG Qizhi2, LEI Yaqin2   

  1. 1. Guangzhou Urban Planning & Design Studio, Guangzhou 510030, China
    2. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
    3. Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, Guangdong, China
  • Received:2022-06-03 Revised:2022-08-20 Online:2023-01-28 Published:2023-03-28
  • Supported by:
    National Natural Science Foundation of China(42271210);National Natural Science Foundation of China(41901191);Natural Science Foundation of Guangdong Province(2022A1515011572);Guangzhou Basic and Applied Basic Research Foundation(202102020795)

摘要:

城市地理与城乡规划一直关注城市活力与建成环境的关系。论文以广州中心城区为例,通过采集百度热力图、建筑矢量数据、路网数据、兴趣点数据等多源空间大数据,应用梯度提升决策树模型,探究建设强度、功能性质和交通可达性3个维度的建成环境要素对城市活力影响的非线性关系和阈值效应,并对比工作日白天与夜间的影响差异。研究发现:① 容积率对城市活力塑造的相对重要性最高,其次是休闲设施密度与公交密度,且白天与夜间的差异不显著。合理的开发建设强度、集聚的休闲与办公设施、公交导向交通发展,更有助于塑造充满活力的城市。② 各建成环境要素与城市活力之间均存在非线性关系和阈值效应,且部分建成环境要素白天与夜间的差异较明显。研究结论可为精细化的建成环境规划与治理以促进城市活力提供一定的政策启示。

关键词: 城市活力, 建成环境, 非线性, 阈值效应, 机器学习, 百度热力图

Abstract:

The relationship between urban vitality and the built environment has always been a subject of intense research in urban geography and planning. Traditionally, global regression techniques have been mostly developed to analyze their quantitative relationship based on a linear consumption. However, the results from existing studies are rather mixed, indicating that their relationship may not be global. Therefore, it is necessary to explore the local characteristics of the associations between urban vitality and the built environment. Based on a collection of multi-source datasets including Baidu Heat Map data, building data, road network data, and point of interest data in central Guangzhou City—including Liwan, Yuexiu, Tianhe and Haizhu districts, this study applied the gradient boosting decision tree model to unveil the nonlinear associations of built environment characteristics (including intensity, function, and accessibility) with urban vitality and threshold effects. The differences in the impacts of the built environment between daytime and nighttime on weekdays have also been examined. The results show that: 1) During both the daytime and the nighttime, floor area ratio contributes the greatest to urban vitality, followed by the intensity of recreation and public transport facilities. Compared to diversity, reasonable development intensity, concentration of leisure and office facilities, and public transport oriented development have larger collective contributions to urban vitality. 2) Although differences exist between daytime and nighttime, all built environment variables have nonlinear associations with urban vitality. The threshold value and gradient of key built environment variables are recognized in the nonlinear shapes of associations. Urban planners and local governments are recommended to meticulously disentangle the complicated built environment associations to make informed and targeted interventions for fostering and maintaining urban vitality.

Key words: urban vitality, built environment, nonlinear associations, threshold effect, machine learning, Baidu Heat Map