Data-Responsive Architecture in Urban Open Space: Sensing Social and Environmental Data and Regulating Spatial Configuration in Real-Time

The design experiment aimed to examine the application of dataprocessing techniques to regulate a responsive architectural space. The experiment involved simulating an active system of adaptive space that comprised kinetic structures capable of responding to real-time changes in social events and environmental conditions situated in an open space in New York City. The study tested the application of rule-based algorithms that utilise predefined rules to determine the behaviours of kinetic structures by manipulating data. The rule-based algorithm processed the input of urban open data that captured the phenomenon in the open space, resulting in controlling the spatial configurations of kinetic structures. This involved the act of categorisation, classifying the location, schedule, and type of social event data, as well as calculating numeric values of weather parameters such as temperature, cloud cover, humidity, and wind speed to optimise spatial qualities with respect to light/shading, weather conditions, and access. The challenge of the experiment was to create an algorithm that was not only reactive to the input data in real time but also intelligently responsive, providing optimised conditions of an architectural space that adapts to the contexts of both social and environmental changes. The results of the experiment demonstrate the potential for the development of computational techniques to create intelligently responsive spaces in the built environment.