The coronavirus pandemic highlighted the vital role urban areas play in supporting citizens’ health and well-being (Ribeiro et al., 2021). In times of (personal) vulnerability, citizens depend on their neighbourhood for performing daily physical activities to restore their mental state, but public spaces currently fall short in fulfilling the appropriate requirements to achieve this. The situation is exacerbated by Western ambitions to densify through high-rise developments to meet the housing demand. In this process of urban densification, public spaces are the carriers where global trends, local ambitions and the conditions for the social fabric materialise (Battisto & Wilhelm, 2020). High-rise developments in particular will determine users’ experiences at street-level. Consequently, they have an enduring influence on the liveability of neighbourhoods for the coming decades but, regarding the application of urban design principles, their impact is hard to dissect (Gifford, 2007).Promising emerging technologies and methods from the new transdisciplinary field of neuroarchitecture may help identify and monitor the impact of certain physical characteristics on human well-being in an evidence-based way. In the two-year Sensing Streetscapes research study, biometric tools were tested in triangulation with traditional methods of surveys and expert panels. The study unearthed situational evidence of the relationship between designed and perceived spaces by investigating the visual properties and experience of high-density environments in six major Western cities. Biometric technologies—Eye-Tracking, Galvanic Skin Response, mouse movement software and sound recording—were applied in a series of four laboratory tests (see Spanjar & Suurenbroek, 2020) and one outdoor test (see Hollander et al., 2021). The main aim was to measure the effects of applied design principles on users’ experiences, arousal levels and appreciation.Unintentionally, the research study implied the creation of a 360° built-environment assessment tool. The assessment tool enables researchers and planners to analyse (high-density) urban developments and, in particular, the architectural attributes that (subliminally) affect users’ experience, influencing their behaviour and perception of place. The tool opens new opportunities for research and planning practice to deconstruct the successes of existing high-density developments and apply the lessons learned for a more advanced, evidence-based promotion of human health and well-being.ReferencesBattisto, D., & Wilhelm, J. J. (Eds.). (2020). Architecture and Health Guiding Principles for Practice. Routledge, Taylor & Francis Group. Gifford, R. (2007). The Consequences of Living in High-Rise Buildings. Architectural Science Review, 50(1), 2–17. https://doi.org/https://doi.org/10.3763/asre.2007.5002 Hollander, J. B., Spanjar, G., Sussman, A., Suurenbroek, F., & Wang, M. (2021). Programming for the subliminal brain: biometric tools reveal architecture’s biological impact. In K. Menezes, P. de Oliveira-Smith, & A. V. Woodworth (Eds.), Programming for Health and Wellbeing in Architecture (pp. 136–149). Routledge, Taylor & Francis Group. https://doi.org/https://doi.org/10.4324/9781003164418 Ribeiro, A. I., Triguero-Mas, M., Jardim Santos, C., Gómez-Nieto, A., Cole, H., Anguelovski, I., Silva, F. M., & Baró, F. (2021). Exposure to nature and mental health outcomes during COVID-19 lockdown. A comparison between Portugal and Spain. Environment International, 154, 106664. https://doi.org/https://doi.org/10.1016/j.envint.2021.106664 Spanjar, G., & Suurenbroek, F. (2020). Eye-Tracking the City: Matching the Design of Streetscapes in High-Rise Environments with Users’ Visual Experiences. Journal of Digital Landscape Architecture (JoDLA), 5(2020), 374–385. https://gispoint.de/gisopen-paper/6344-eye-tracking-the-city-matching-the-design-of-streetscapes-in-high-rise-environments-with-users-visual-experiences.html?IDjournalTitle=6
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Purpose: The purpose of this paper is to inform the reader of some emerging trends in placemaking and digital destination management, while providing a conceptual background on shifts in architectural design. Design/methodology/approach: The trend paper is based on a fundamental bibliographic view on evolutions in placemaking, from architectural design to spatial agency, integrated by and contextualized in tourism trends, however possibly anecdotal. Findings: The trend paper identifies a fundamental shift from architectural processes to spatial agency as organizing principle for placemaking, discussing how digital tourism trends are formed or forming change in this. Originality/value: The trend paper newly relates otherwise distant and unrelated fields, namely architectural design theory and tourism trends, by connecting at the level of IoT and IT digital technologies, exploring the impact and the mutual role played by its two constituencies.
This project assists architects and engineers to validate their strategies and methods, respectively, toward a sustainable design practice. The aim is to develop prototype intelligent tools to forecast the carbon footprint of a building in the initial design process given the visual representations of space layout. The prediction of carbon emission (both embodied and operational) in the primary stages of architectural design, can have a long-lasting impact on the carbon footprint of a building. In the current design strategy, emission measures are considered only at the final phase of the design process once major parameters of space configuration such as volume, compactness, envelope, and materials are fixed. The emission assessment only at the final phase of the building design is due to the costly and inefficient interaction between the architect and the consultant. This proposal offers a method to automate the exchange between the designer and the engineer using a computer vision tool that reads the architectural drawings and estimates the carbon emission at each design iteration. The tool is directly used by the designer to track the effectiveness of every design choice on emission score. In turn, the engineering firm adapts the tool to calculate the emission for a future building directly from visual models such as shared Revit documents. The building realization is predominantly visual at the early design stages. Thus, computer vision is a promising technology to infer visual attributes, from architectural drawings, to calculate the carbon footprint of the building. The data collection for training and evaluation of the computer vision model and machine learning framework is the main challenge of the project. Our consortium provides the required resources and expertise to develop trustworthy data for predicting emission scores directly from architectural drawings.
