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Walkacross: Space–Motion Metric for Responsive Architecture

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Computational Morphologies
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Abstract

Nowadays it is possible to collect more data than ever before. Yet, producing meaningful insight from such data is perhaps the most difficult task. This is particularly true when collecting data from people as for attempting to make architecture responsive to them, or for informing the design process. For example, some buildings respond to the environment temperature or light in real time. In such cases sensors trigger precise responses; high radiation equals to producing more shade; radiation is a straightforward indicator. However, determining how architecture might become responsive to people’s spatial behavior is far from being a straightforward matter. Human–space interaction has been barely studied and usually is defined in not quantifiable terms. To address this quantification difficulty, I identified motion as the main indicator of human–space interaction. Motion is quantifiable and is composed by space and time. Motion is understood as a sign of people’s perception of space. Furthermore, the goal of this study is the development of empirical research and data analysis of how architectural spatial configuration affects people’s motion. The architecture design process is the focus to assess meaningful insight. A metric, denominated space-motion, was developed as a result of the need to categorize data. The space–motion metric is composed by 6 indicators that correlate people’s motion with architecture features. The data collections for the analysis were recorded with Microsoft Kinect, tested in several data collections at the Massachusetts Institute of Technology and the world. Kinect data, assessed with space–motion metric, might bring unforeseen advancements for contributing to responsive architecture and architecture design process in general.

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Correspondence to Paloma Gonzalez Rojas .

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Gonzalez Rojas, P. (2018). Walkacross: Space–Motion Metric for Responsive Architecture. In: Rossi, M., Buratti, G. (eds) Computational Morphologies. Springer, Cham. https://doi.org/10.1007/978-3-319-60919-5_12

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  • DOI: https://doi.org/10.1007/978-3-319-60919-5_12

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  • Publisher Name: Springer, Cham

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