基于人工智能技术的植被色彩感知与焦虑关联分析

The correlation between plant color perception and anxiety based on artificial intelligence technology

城市绿色空间对焦虑的缓解作用受到城市地理学、城市规划学和景观生态学等多学科学者的关注。然而,受限于数据采集技术的精确程度,很少有研究分析绿色空间中的植被色彩对降低焦虑水平的作用。基于此,本文结合公民科学和人工智能技术建立高精度城市植被数据库,从植被色彩视觉感知的角度入手分两个层次评价植被色彩,并构建有序Logistic模型探究不同地理背景下植被色彩与个体焦虑的关系。研究表明:丰富的植被色彩确实能有效缓解焦虑,但同时受可塑性面积单元(MAUP)和地理背景不确定性问题(UGCoP)的影响。具体而言,提升居住环境的基底色彩水平、增加工作环境中植被色彩的多样性有助于缓解焦虑,并且植被色彩与焦虑的关联主要在小规模缓冲区中观察到。上述结论证实了植被色彩对焦虑缓解作用,能够为城市绿色空间设计提供具体的优化建议。

The alleviating effect of urban green space on anxiety has attracted the attention of scholars from multiple disciplines such as urban geography,urban planning and landscape ecology.However,whether plant color,as an important feature of green space,plays a key role in alleviating anxiety has not been examined thus far due to the limitation of the accuracy of data collection technology.Accordingly,this research attempts to innovatively construct a system to describe the colors of plant so as to explore the relationship between plant color and residents'anxiety under different geographical backgrounds.Through the combination of public citizen science and artificial intelligence,we constructed a high-precision urban forestry database and subsequently evaluated plant color in two levels from the perspective of visual perception,and compared the difference of plant colors in residential areas and workplaces.Ordinal logistic regression models were established to explore the relationship between plant color and anxiety.Research shows that plant color diversity can effectively relieve anxiety,but it was influenced by modifiable areal unit problem(MAUP)and uncertain geographic context problem(UGCoP).Specifically,improving the base color level of the living environment and increasing the diversity of plant colors in the workplace can help alleviate anxiety,and the correlation between plant color and anxiety is mainly observed in small-scale buffer zones.The above results confirm the correlation between plant color and anxiety,and can provide specific optimization suggestions for urban green space design.