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.

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 23186968, DOI 10.5151/proceedings-ecaadesigradi2019_550