Urban densification continues unabated, even as the possible consequences for users’ eye-level experiences remain unknown. This study addresses these consequences. In a laboratory setting, images of the NDSM wharf were shown to university students primed for one of three user groups: residents, visitors and passers-by. Their visual experiences were recorded using eye-tracking and analyzed in combination with surveys on self-reported appreciation and restorativeness. On-site surveys were also administered among real users. The results reveal distinct eye-movement patterns that point to the influence of environmental roles and tasks and how architectural qualities steer people’s visual experience, valence and restoration.
DOCUMENT
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.
DOCUMENT
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.
