Varoudis, T;
Penn, A;
(2021)
Spectral Clustering and Integration: The Inner Dynamics of Computational Geometry and Spatial Morphology.
In:
Formal Methods in Architecture. Advances in Science, Technology & Innovation (IEREK Interdisciplinary Series for Sustainable Development).
(pp. pp. 243-250).
Springer: Cham, Switzerland.
Preview |
Text
Varoudis_Penn_Spectral Clustering and Integration_Uploaded.pdf - Accepted Version Download (9MB) | Preview |
Abstract
Deviating from common evaluation strategies of spatial networks that are realised through numerical comparison of single floating-point numbers such as global and local space syntax measures (centralities, connectivity, etc.) we aim to present a new computational methodology for creating detailed topo-geometric encodings of spaces that encapsulate some of the fundamental ideas about spatial morphology by Hillier (Space is the Machine: A Configurational Theory of Architecture, London, UK, Space Syntax, 2007 [1]). In most cases, space syntax measures try to capture a particular quality of the space for comparison but they lose much of the detail of the spatial topo-geometry and morphology by mainly aggregating graph path traversals and not retaining any other information. This research explores the use of weighted graph spectra, in a composite form, for the purpose of characterising the spatial structure as a whole. The new methodology focuses on the three primary space syntax graph modelling concepts, ‘angular’, ‘metric’ and ‘topological’, from the point of view of the resulting spatial geometries and develops new computational innovations in order to map spatial penetration of local neighbourhood spectra in different scales, dimensions and built environment densities in a continues way. The result is a new composite vector of high dimensionality that can be easily measured against others for detailed comparison. The proposed methodology is then demonstrated with the complete road-network dataset of Great Britain. The main dataset together with subsets is then used in a series of unsupervised machine learning analyses, including clustering and a form of Euclidian ‘spectral integration’.
| Type: | Proceedings paper |
|---|---|
| Title: | Spectral Clustering and Integration: The Inner Dynamics of Computational Geometry and Spatial Morphology |
| Event: | 5th International Symposium on Formal Methods in Architecture (5FMA), Lisbon 2020 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-030-57509-0_22 |
| Publisher version: | https://doi.org/10.1007/978-3-030-57509-0_22 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Graph analysis, Graph spectra, Unsupervised clustering, Space syntax, Spectral integration |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > The Bartlett School of Architecture |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10113050 |
Archive Staff Only
 |
View Item |
