| id |
ecaadesigradi2019_550 |
| authors |
Rhee, Jinmo, Cardoso Llach, Daniel and Krishnamurti, Ramesh |
| year |
2019 |
| title |
Context-rich Urban Analysis Using Machine Learning - A case study in Pittsburgh, PA |
| doi |
https://doi.org/10.52842/conf.ecaade.2019.3.343
|
| source |
Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 3, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 343-352 |
| summary |
This paper reports on the analytical potential of machine learning methods for urban analysis. It documents a new method for data-driven urban analysis based on diagrammatic images describing each building in a city in relation to its immediate urban context. By statistically analyzing architectural and contextual features in this new dataset, the method can identify clusters of similar urban conditions 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 of urban plan that displays gradients of urban similarity. Taking Pittsburgh as a case study we demonstrate this method, and propose "morphological types" as a new category of urban analysis describing a given city's specific set of distinct morphological conditions. The paper concludes with a discussion of the implications of this method and its limitations, as well as its potentials for architecture, urban studies, and computation. |
| keywords |
Urban Morphology; Machine Learning; Architectural Contexts; Urban Analysis; GIS |
| series |
eCAADeSIGraDi |
| email |
|
| full text |
 file.pdf (20,000,036 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:56 |
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