Actual and experiential shadow origin tagging: A 2.5D algorithm for efficient precinct-scale modelling. (March 2020)

• Record Type: Journal Article
• Authors: Kimm, Geoff
• Is Part Of: International journal of architectural computing. Volume 18:Number 1(2020)
• Page Start: 41
• Page End: 52
• Publication Date: 2020-03
• Subjects: 2.5D raster -- actual and experiential shadow origins -- generative techniques -- participatory built environment simulation -- reactive scripting for design; Architecture -- Data processing -- Periodicals; Architecture -- Informatique -- Périodiques; Virtual reality in architecture -- Periodicals; Computer-aided design -- Periodicals; Architecture -- Data processing; Periodicals; 720.2840285536
• DOI: 10.1177/1478077119895218
• Languages: English
• ISSNs: 1478-0771

Abstract

This article describes a novel algorithm for built environment 2.5D digital model shadow generation that allows identities of shadowing sources to be efficiently precalculated. For any point on the ground, all sources of shadowing can be identified and are classified as actual or experiential obstructions to sunlight. The article justifies a 2.5D raster approach in the context of modelling of architectural and urban environments that has in recent times shifted from 2D to 3D, and describes in detail the algorithm which builds on precedents for 2.5D raster calculation of shadows. The algorithm is efficient and is applicable at even precinct scale in low-end computing environments. The simplicity of this new technique, and its independence of GPU coding, facilitates its easy use in research, prototyping and civic engagement contexts. Two research software applications are presented with technical details to demonstrate the algorithm's use for participatory built environment simulation and generative modelling applications. The algorithm and its shadow origin tagging can be applied to many digital workflows in architectural and urban design, including those using big data, artificial intelligence or community participative processes.