Evolving Research Method in Three-Dimensional and Volumetric Urban Morphology of a Highly Dense City: Assessing Public and Quasi-Public Space Typologies

An appropriate urban density is a vital part of a sustainable urban fabric. However, when it comes to measuring the built urban fabric and how people walk through it and use, a difficulty has been observed in defining applicable measurement tools. With the intention of identifying the variables that will allow the best characterization of this fabric and movement, a multi-variable analysis methodology from the field of artificial intelligence (AI) is proposed. The main objective of this paper is to prove the capacity of AI as an evolving research method in urban morphology and specifically to evaluate the capacity of such a methodology to measure the way in which people travel through defined multi-levels of typologies of public urban space. The research uses the case of Hong Kong as a dense city that is three-dimensionally activated in terms of its public realm, not just at street level, but also via below ground subways and upper-level walkways, public and quasi-public spaces. This includes the three-dimensional volumetric assessment of public and quasi-public space typologies within a highly dense city. For the purpose of the study, a characterization and term definition of these spaces has been further developed: “Junctions”, “Landmarks”, “Intersections”, “Districts”, “Passages” and “Lobbies” (both outdoor and indoor) based on Lynch’s 5 main key elements (District, landmark, path, edges, node). The results obtained using AI prove to be more robust and rational than those based on a more limited range of tools, evidencing that using AI can offer operational opportunities for better understanding of morphological and typological evolution within the vertical and volumetric built urban fabric.