CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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id acadia17_266
authors Gonzalez Rojas,Paloma
year 2017
title Space and Motion: Data-Driven Model of 4D Pedestrian Behavior
source ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 266-273
summary The understanding of space relies on motion, as we experience space by crossing it in time, space’s fourth dimension. However, architects lack the necessary tools to incorporate people's motion into their design of space. As a consequence, architects fail to connect space with the motion of the people that inhabit their buildings, creating disorienting environments. Further, what if augmentation technology changes how we inhabit space and the static built environment does not fit people anymore? This paper explores the problem of developing a model from people's motion, to inform and augment the architecture design process in the early stages. As an outcome, I have designed a model based on data from human-space interaction obtained through field work. First, relevant behavior was identified and recorded. Second, a metric was extracted from the data and composed by speed, the 4th D dimension as time, and gestures. Third, the original behavior was rebuilt, producing a set of rules. The rules were combined to form the model of human-space interaction. This generalizable model provides a novel approach to designing space based on data from people. Moreover, this paper presents a means of incorporating inhabitants' behavior into digital design. Finally, the model contributes to the advancement of people's motion research for general applications, such as in transport engineering, robotics, and cognitive sciences.
keywords design methods; information processing; simulation & optimization; data visualization
series ACADIA
full text file.pdf (2,537,116 bytes)
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