From data to decision: empirical application of machine learning in public space planning along the Grand Canal, Shandong Province, China

Introduction 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. This is because traditional planning methods often fail to fully capture the detailed behavior of residents. Therefore, 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), analyze the behavior patterns and preferences of residents regarding different public spaces, and thereby provide support for data - driven public space planning. Methods 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. Results The application of these methods yielded several important results. 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. Additionally, association rule mining uncovered strong correlations between location types and perceived attributes such as cleanliness and aesthetics. Moreover, correlation analysis indicated statistically significant positive correlations between aesthetics and cleanliness, as well as between safety and cleanliness. Discussion This research offers valuable data - driven insights for public space planning and management. It demonstrates that machine learning can effectively identify and quantify key factors influencing public space use. As a result, it provides more accurate policy recommendations for urban planners and ensures that 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. For instance, 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.