Evaluation on nature-connected environment in building embedded landscape: theory, detection, and case design

Abstract

Introduction: A nature-connected environment is important for constructing building infrastructures in a sustainable city. However, its realistic application is rarely utilized in urban planning, primarily due to a lack of accurate regression of emotional perceptions of nature connectedness against existing landscape metrics. Method: In this study, Changsha was selected as the study area, targeting 50 urban parks as plots, with 165 left unselected for prediction. Twenty pedestrians were randomly approached to assess their emotional perceptions of nature connectedness. Self-reported data were used to evaluate positive and negative emotion scores (Cronbach α: 0.803) and the emotional nonparametric relation index (ENRI) (Pearson correlation in retest: R square = 66.37%). Results: Three machine-learning algorithms (random forest, AdaBoost, and gradient boosting trees [GBT]) were compared, with GBT being selected (R square: 76.49 ‒ 88.64 %) for further comparison with multivariate linear regression (MLR). Principal component analysis was used for verification, and the GBT algorithm surpassed MLR in regression performance. The green view index (GVI) in large and open green spaces was found to correlate with perceptions of positive emotions (parameter estimate [PE]: 0.02 ‒ 0.13). Conversely, large areas of green and blue spaces with low GVI on the flat plane were found to evoke negative emotions (PE: -0.13 and -0.04, respectively). Discussion: Artificial intelligence was used for describing cases of design using our findings. For the purpose to evoke positive emotion of visitors, building embedded landscape can be designed in distant view with large tree canopies in closer view. Large area of green and blue spaces should not be recommended no matter whether buildings background the view.