Urban dwellers enjoy nature exposure in the neighborhood built environment through visual and physical ways, such as window views and outdoor activities. However, existing studies and analytics examine these pathways separately, leading to underinformed urban planning practices such as difficult prioritizing urban areas with both low-level nature exposures. The underinformation problem is particularly severe for high-rise, high-density cities that embrace high-level vertical diversity. This study aims to propose bi-objective analytics of 3D visual-physical nature exposures, for holistic - rather than separated - assessments. First, a floor-level Nature Exposure Index (NEI) is defined with visual and physical components. The visual component NEIv is assessed by window view imagery and deep transfer learning, while the physical component NEIp reflects the mean time from the floor to the nearest natural sites (e.g., nature parks and seaside) through the 3D pedestrian network. Then, bi-objective optimization-based analytics is designed for (i) identifying buildings and blocks with holistically low-level visual-physical nature exposures using NEI and (ii) examining probabilistic outputs and robustness of linear weighting schemes. A case study of 519 buildings showed that the NEI-enabled bi-objective analytics is automatic, effective, and inexpensive. Interviews with field experts confirmed that the analytics provides comprehensive evidence for a holistic identification of high-rise, high-density areas in need of nature exposure for landscape management and urban planning.
