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- W3173541876 abstract "Urban morphological measures applied at a high-resolution of analysis may yield a wealth of data describing varied characteristics of the urban environment in a substantial degree of detail; however, these forms of high-dimensional datasets are not immediately relatable to broader constructs rooted in conventional conceptions of urbanism. Data science and machine learning methods provide an opportunity to explore such forms of data by applying unsupervised machine learning methods. The dimensionality of the data can thereby be reduced while recovering latent themes and identifying characteristic patterns which may resonate with urbanist discourse more generally. Dimensionality reduction and clustering methods, including Principal Component Analysis (PCA), Variational Autoencoders, and an Autoencoder based Gaussian Mixture Model, are discussed and demonstrated for purposes of `untangling' urban datasets, revealing themes bridging quantitative and qualitative descriptions of urbanism. The methods are applied to a morphological dataset for Greater London. The spatial aggregations and morphological measures are computed at pedestrian walking tolerances at a 20m network resolution using the cityseer-api Python package, which utilises a local windowing-methodology with distances computed directly over the network and with aggregations performed dynamically and with respect to the direction of approach, thus preserving the relationships between the variables and retaining contextual precision. Whereas the demonstrated methods hold tremendous potential, their power is difficult to convey or fully exploit using conventional lower-dimensional visualisation methods, thus underscoring the need for subsequent research into how such methods may be coupled to interactive visualisation methods to elucidate further the richness of the data and its potential implications." @default.
- W3173541876 created "2021-07-05" @default.
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- W3173541876 date "2021-06-26" @default.
- W3173541876 modified "2023-09-27" @default.
- W3173541876 title "Untangling urban data signatures: unsupervised machine learning methods for the detection of urban archetypes at the pedestrian scale" @default.
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