Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/28335
Author(s): | Lopes, J. V. |
Editor: | David Leite Viana Franklim Morais Jorge Vieira Vaz |
Date: | 2018 |
Title: | A data mining based methodology for the multidimensional study of public open spaces |
Book title/volume: | Formal methods in architecture and urbanism |
Reference: | Lopes, J. V. (2018). A data mining based methodology for the multidimensional study of public open spaces. EM D. L. Viana., F. Morais, & J. V. Vaz (Eds.). Formal methods in architecture and urbanism. Cambridge Scholars Publishing. http://hdl.handle.net/10071/28335 |
ISBN: | 978-1-5275-0762-3 |
Keywords: | Urban morphology Projecto urbano -- Urban design Public open space Parametric-algorithmic design Data mining -- |
Abstract: | Public open spaces can only be apprehended from multiple simultaneous perspectives. Urban morphology traditional descriptive methods have recognized limitations in relating the polymorphic and polysemantic nature of these spaces’ attributes, derived from the different standpoints on their formal, historical and geographic idiosyncrasies. Identities and similarities may be disclosed by multivariate statistical analysis and data mining techniques by studying the relations between formal and intangible spatial properties in a multidimensional space. In an ongoing PhD research project we outline a method for the synchronic analysis and classification of the public open spaces, departing from a corpus of 126 Portuguese urban squares, whose analysis is intended to interactively (re)define it. Part of the work done so far is presented: (i) firming the concepts, criteria and attributes to extract; (ii) adaptation and/or creation of new analytical methods and tools; and (iii) research on multivariate analysis, data mining and data visualization techniques. |
Peerreviewed: | no |
Access type: | Open Access |
Appears in Collections: | ISTAR-CLI - Capítulos de livros internacionais |
Files in This Item:
File | Size | Format | |
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bookPart_67919.pdf | 2,63 MB | Adobe PDF | View/Open |
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