CN 11-5366/S     ISSN 1673-1530
“风景园林,不只是一本期刊。”

儿童友好城市街道空间感知与居民情绪影响作用研究——基于福州实证分析

The Research on the Impact of Street Space Perception on Children's Friendly Cities and Residents' Emotions: An Empirical Analysis Based on Fuzhou.

  • 摘要: 【目的】本研究旨在探讨儿童友好城市建设中街道空间感知对居民情绪的影响机制。通过对福州市主城区的实证分析,揭示不同街景要素对居民,特别是儿童及其监护人情绪的潜在影响,为儿童友好型城市的规划设计提供参考和理论依据。【方法】研究基于多源数据,结合百度街景图像、社交媒体用户生成内容(UGC)以及政府投诉数据,运用先进的机器学习技术和地理分析手段进行综合分析。【结果】研究结果表明,交通流量、视觉复杂度、安全感知以及栅栏占比是影响居民情绪的四个主要因素。其中,交通流量对居民情绪的负面影响最为显著,过大的交通流量会显著降低居民的情绪感知水平。视觉复杂度和安全感知度也对情绪有重要影响,适中的视觉复杂度和高安全感知度能够显著提升居民的情绪体验。此外,栅栏占比对情绪的正面作用也较为突出,表明合理的街道安全设施设计有助于提升儿童友好性。然而,某些街道尽管经过适儿化改造,但其情绪反馈仍低迷,因此优化设计应更关注实际使用感受和情绪反馈。【结论】本研究发现街道空间感知对情绪的影响具有复杂性和多样性。基于研究结果,提出了优化儿童友好城市设计的建议。有助于儿童及其家庭创造更为安全、健康、舒适的生活环境,同时为政策制定者和城市规划者提供了理论支持和实证参考。

     

    Abstract: ObjectiveThis study investigates the complex relationship between street space perception in child-friendly cities and residents' emotions, focusing on Fuzhou’s urban area. The aim is to explore how specific street design elements impact residents' emotional responses, with an emphasis on creating a more child-friendly urban environment. Given the growing attention to child-friendly cities, understanding how these urban spaces influence not only children but also the emotions of accompanying residents (especially parents) is essential for better urban planning. Methods The study employs a mixed-methods approach using multi-source data, including Baidu Street View images, user-generated content (UGC) from social media platforms, and official complaint data from government services. Geographic analysis technology and machine learning methods, such as convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and XGBoost with SHAP (Shapley Additive Explanations), are utilized to analyze the relationship between urban street space perception and emotional responses. Key environmental indicators like traffic flow, visual complexity, safety perception, sidewalk quality, and fence ratio are extracted from street view images using semantic segmentation and human-machine adversarial scoring. Emotional analysis of the collected textual data from social media and complaint platforms is performed through sentiment analysis models to quantify the residents' emotional responses (positive, neutral, negative) toward the urban environment. Results The results indicate that several key urban design factors significantly impact residents' emotions. Traffic flow (0.282), visual complexity (0.212), safety perception (0.21), and fence ratio (0.183) are identified as the most influential indicators. For example, high traffic flow tends to evoke negative emotions due to safety concerns for children, while an optimized visual complexity stimulates positive emotional responses. Areas like Guangming Port, which underwent child-friendly renovations, were found to have low emotional feedback, indicating the need for both design and residents' practical usage to align for effective urban space development. Conversely, streets like Pu Shang Avenue, which have not undergone specific child-friendly modifications, showed positive emotional responses, highlighting the importance of balancing urban functionality with child-friendliness.SHAP analysis further reveals the nonlinear and complex effects of various factors on emotional responses. Traffic flow, visual complexity, safety perception, and fence ratio are critical, while other factors like pavement quality and openness contribute moderately. Visual complexity was found to positively affect emotional responses when balanced, but excessive complexity negatively impacted residents’ feelings. Additionally, safety features like pedestrian crossing signals and fences enhance emotional well-being, especially in areas with high foot traffic. Conclusion The study concludes that optimizing street space perception in child-friendly cities requires a comprehensive approach. Key suggestions include regulating traffic flow, enhancing safety measures, and optimizing visual complexity to foster positive emotional responses among residents. Urban planners must ensure that child-friendly city designs not only meet children's physical needs but also consider the emotional and practical experiences of parents and other residents. Child-friendly projects must emphasize both functionality and emotional well-being by improving public safety, fostering engaging visual environments, and creating balanced spaces that serve diverse needs.The research provides theoretical insights and practical recommendations for the ongoing development of child-friendly cities in China, emphasizing that successful urban designs are those that integrate emotional feedback from residents. Furthermore, the use of advanced machine learning models like XGBoost and SHAP allows urban planners to more accurately predict the emotional impacts of specific urban features, providing a valuable tool for future city planning efforts. The study highlights the necessity of involving residents' emotions in the design process to ensure a holistic approach to child-friendly urban development.

     

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