Interaction between the characteristics of urban three-dimensional landscape pattern and social-environmental factors based on boosted regression tree

Abstract

Vertical expansion makes the structure and pattern of the city more complicated. Traditional two-dimensional landscape pattern cannot completely reflect the ecological structure and functional characteristics of urban landscape. In this study, we used the three-dimensional landscape pattern metrics to quantify the regional three-dimensional landscape pattern, and used boosted regression tree (BRT) machine learning algorithms to comprehensively analyze the interaction between social-environmental factors and urban landscape patterns in the central part of Shanghai. Results showed that high building ratio, mean architecture height, and architecture height standard deviation had higher values in the surrounding area of the inner ring. The number of buildings and landscape shape index were higher in the outer ring than those in other area. Building coverage ratio, floor area ratio and Shannon's diversity index had higher values in the central part, with the metrics of Puxi being generally higher than those of Pudong. Population density and normalized vegetation index (NDVI) interacted most significantly with the three-dimensional landscape pattern, with GDP as the least influential factor. Within a certain range, the three-dimensional landscape pattern metrics increased with larger population density in the social factors, and decreased with lower rate of NDVI and water surface ratio in the environmental factors. Our results demonstrated that the BRT method was effective in quantifying the interaction between landscape pattern and social-environmental factors. Our results help improve the understanding of the relationship between ecological environment and human well-being in the central part of Shanghai and provide a scientific basis for the urban three-dimensional expansion planning.