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ORIGINAL RESEARCH article

Front. Built Environ.

Sec. Urban Science

Volume 11 - 2025 | doi: 10.3389/fbuil.2025.1643104

From Data to Decision: Empirical Application of Machine Learning in Public Space Planning along the Grand Canal, Shandong Province, China

Provisionally accepted
Jing  ZhaoJing Zhao1Yuan  JiangYuan Jiang1Xiuhua  ZhangXiuhua Zhang2Qing  YeQing Ye3Qiang  ZhaoQiang Zhao4Xianhua  WuXianhua Wu1Linshen  WangLinshen Wang1*
  • 1University of Jinan, Jinan, China
  • 2Shandong Urban Construction Vocational College, Jinan, China
  • 3Hebei University of Technology, Tianjin, China
  • 4Tianjin Municipal Bureau of Planning and Natural Resources, Tianjin, China

The final, formatted version of the article will be published soon.

In the process of urbanization, public space plays an increasingly important role in improving the livability and sustainability of cities. However, effectively understanding the preferences of different groups for public space and conducting reasonable planning integrated with environmental and infrastructure elements remains a challenge in urban planning. Traditional planning methods often fail to fully capture the detailed behavior of residents. The purpose of this study was to explore the empirical application of machine learning technology to public space planning along the Grand Canal in Shandong Province (China), and to analyze the behavior patterns and preferences of residents regarding different public spaces, and thereby provide support for data-driven public space planning. Based on survey data from 1008 respondents across 4 cities, this study employed machine learning methods such as K-means clustering, association rule mining, and correlation analysis to investigate the relationships between visitor behavior and the environmental characteristics of public spaces. Cluster analysis identified three distinct groups: young and middle-aged local residents with a preference for accessibility, middle-aged and elderly groups enthusiastic about cultural engagement, and diverse transportation users with mixed spatial preferences. Association rule mining uncovered strong correlations between location types and perceived attributes such as cleanliness and aesthetics. Correlation analysis indicated statistically significant positive correlations between aesthetics and cleanliness, as well as between safety and cleanliness. This research offers data-driven insights for public space planning and management, and demonstrates that machine learning can effectively identify and quantify key factors influencing public space use. It thereby provides more accurate policy recommendations for urban planners and ensures public space planning better meets the needs of different groups. For urban planners, the findings can guide the optimization of facility layouts for specific groups, such as adding canal cultural display nodes for cultural engagement groups and improving barrier-free facilities for groups with high accessibility needs, thereby enhancing the inclusiveness and utilization efficiency of public spaces.

Keywords: machine learning, public space, Clustering analysis, association rule mining, Correlation analysis

Received: 09 Jun 2025; Accepted: 01 Oct 2025.

Copyright: © 2025 Zhao, Jiang, Zhang, Ye, Zhao, Wu and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Linshen Wang, cea_wangls@ujn.edu.cn

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