Numerous studies have shown that the emotional wellbeing of urban populations is influenced by various aspects of urban development, such as social factors and the presence of walkable areas and green spaces. However, there is a lack of research that closely integrates urban design and cognition, particularly in the context of vertical and volumetric urbanism. This disconnect between design and science disciplines is evident when reviewing the limited research on emotional and spatial cognition in this specific urban context. This paper seeks to address that disconnect by proposing a comprehensive framework for the cognitive measurement and modelling of the built environment. This will involve exploring and measuring neural mechanisms, employing electroencephalogram (EEG) equipment to measure user responses in vertical and volumetric public spaces. The aim is to create a foundation for further studies in this field that is consistent and rigorous and can facilitate collaboration with cognitive neuroscientists by establishing a shared conceptual basis. The goal of this research is to develop a human-centric approach for urban design that is scientific and measurable, producing a set of urban design guidelines that incorporate cognitive measurement and modelling, with the broader intention to prioritize human needs and wellbeing within urban environments to make them more liveable.

This paper focuses on the character of multi-level pedestrian accessibility and walkability within the public and quasi-public realm in Hong Kong, using the particular case of the redevelopment of Taikoo Place in Quarry Bay. In an era when the value of public space as a space for public activity and debate is coming under increasing scrutiny, both in Hong Kong and internationally, this paper explores multi-level interior and exterior public and quasi-public realm, as ahybridform of interior open and transit space in a commercial setting that has becomes a central part of the city’s planning, designed to both serve and profit from the high level of pedestrian traffic. This paper draws on empirical research of pedestrian behaviour patterns to interrogate the value and significance of linked-spaces within commercial districts, both to the users and the real estate travelled through, using the city of Hong Kong as a reference and the specific case of Taikoo Place.

Urban interior environments (UIEs) are interior or semi-interior urban public spaces, including shopping malls, atriums, vertical circulation spaces and enclosed stations. The increasing prevalence...

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.