Dezembro 2019 vol. 7 num. 1 - 37 Education and Research in Computer Aided Architectural Design in Europe and XXIII Iberoamerican Society of Digital Graphics, Joint Conference (N. 1)
Article - Open Access.
Context-rich Urban Analysis Using Machine Learning A case study in Pittsburgh, PA
Context-rich Urban Analysis Using Machine Learning A case study in Pittsburgh, PA
Rhee, Jinmo ; Llach, Daniel Cardoso ; Krishnamurti, Ramesh ;
Article:
This paper reports on the analytical potential of machine learning methods forurban analysis. It documents a new method for data-driven urban analysis basedon diagrammatic images describing each building in a city in relation to itsimmediate urban context. By statistically analyzing architectural and contextualfeatures in this new dataset, the method can identify clusters of similar urbanconditions and produce a detailed picture of a city's morphological structure.Remapping the clusters from data to 2D space, our method enables a new kind ofurban plan that displays gradients of urban similarity. Taking Pittsburgh as acase study we demonstrate this method, and propose ``morphological types'' as anew category of urban analysis describing a given city's specific set of distinctmorphological conditions. The paper concludes with a discussion of theimplications of this method and its limitations, as well as its potentials forarchitecture, urban studies, and computation.
Article:
This paper reports on the analytical potential of machine learning methods forurban analysis. It documents a new method for data-driven urban analysis basedon diagrammatic images describing each building in a city in relation to itsimmediate urban context. By statistically analyzing architectural and contextualfeatures in this new dataset, the method can identify clusters of similar urbanconditions and produce a detailed picture of a city's morphological structure.Remapping the clusters from data to 2D space, our method enables a new kind ofurban plan that displays gradients of urban similarity. Taking Pittsburgh as acase study we demonstrate this method, and propose ``morphological types'' as anew category of urban analysis describing a given city's specific set of distinctmorphological conditions. The paper concludes with a discussion of theimplications of this method and its limitations, as well as its potentials forarchitecture, urban studies, and computation.
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DOI: 10.5151/proceedings-ecaadesigradi2019_550
Referências bibliográficas
- [1] .
Como citar:
Rhee, Jinmo; Llach, Daniel Cardoso; Krishnamurti, Ramesh; "Context-rich Urban Analysis Using Machine Learning A case study in Pittsburgh, PA", p. 343-352 . In: Proceedings of 37 eCAADe and XXIII SIGraDi Joint Conference, “Architecture in the Age of the 4Th Industrial Revolution”, Porto 2019, Sousa, José Pedro; Henriques, Gonçalo Castro; Xavier, João Pedro (eds.).
São Paulo: Blucher,
2019.
ISSN 2318-6968,
DOI 10.5151/proceedings-ecaadesigradi2019_550
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