In sustainable high-density cities, public spaces play an important role in supporting social and community health and wellbeing. Amidst ongoing urbanisation, it is of increasing importance to study public space interaction patterns and placemaking processes that contribute to the quality of life of urban residents. This paper reports on the development of a new methodology for the computational tracking and analysis of social activities in urban spaces, using Computer Vision Object Detection (CVOD) techniques to create digitalised pedestrian trajectory data. Referring to concepts from humanistic geography and time geography, our method offers a new platform for data-driven urban place studies, detecting co-presence and social interaction in relation to urban morphology. This paper focuses on the development of Machine Learning protocols, algorithms for tracing and mapping pedestrian trajectories in a georeferenced photogrammetry model, and computational analysis of co-presence. The resulting workflow forms a foundation for future research around detecting, analysing and quantifying behavioural parameters, to evaluate the ability of public spaces to support social interaction and placemaking.

This paper discusses the design and development of scale masonry structures using robot arms, computer vision hardware and bespoke computational workflows. In parallel to the development of full-scale masonry solutions using a Cable Driven Parallel Robot (CDPR), a faster method for testing large numbers of brick elements is needed to verify buildability, mitigate collisions, and think differently about recycled materials during real-world construction activities. Additionally, by incorporating scanning and analysis technology, materials can be digitized, and their attributes translated into variables for placement within an intended structure.

Studies have shown that walkable communities reduce traffic-related pollution and the risk of chronic illnesses, promote economic growth and prosperity, and stimulate community participation and the growth of social capital. To assess the walkability of urban areas, various methodologies have been developed around shortest-distance calculations between various points of interest (POIs), yet their outcomes do not guide potential urban design improvements. The absence of appropriate measurements and procedures that may give quantitative and actionable feedback to support design decision-making is one of the primary issues in building walkable neighborhoods. The work presented in this paper revolves around a new workflow, that employed Urbano, a mobility simulation and assessment tool, and integrated it within a generative design process to allowing for the quantitative evaluation on amenity accessibility for several alternative design scenarios for a case study site in Mong Kok, Hong Kong. The results show how this data-driven urban design process benefits from generative techniques to produce solutions with improved contextual connectivity, energy-efficient urban form, and good quality public spaces that contribute to the walkability of neighbourhoods.