Prediction of 'artificial' urban archetypes at the pedestrian-scale through a synthesis of domain expertise with machine learning methods release_kz6jpwefwjdxjliizjufjk5ycm

by Gareth D. Simons

Released as a article .

2021  

Abstract

The vitality of urban spaces has been steadily undermined by the pervasive adoption of car-centric forms of urban development as characterised by lower densities, street networks offering poor connectivity for pedestrians, and a lack of accessible land-uses; yet, even if these issues have been clearly framed for some time, the problem persists in new forms of planning. It is here posited that a synthesis of domain knowledge and machine learning methods allows for the creation of robust toolsets against which newly proposed developments can be benchmarked in a more rigorous manner in the interest of greater accountability and better-evidenced decision-making. A worked example develops a sequence of machine learning models generally capable of distinguishing 'artificial' towns from the more walkable and mixed-use 'historical' equivalents. The dataset is developed from morphological measures computed for pedestrian walking tolerances at a 20m network resolution for 931 towns and cities in Great Britain. It is computed using the cityseer-api Python package which retains contextual precision and preserves relationships between the variables for any given point of analysis. Using officially designated 'New Towns' as a departure point, a series of clues is developed. First, a supervised classifier (Extra-Trees) is cultivated from which 185 'artificial' locations are identified based on data aggregated to respective town or city boundaries through a process of iterative feedback. This information is then used to train supervised and semi-supervised (M2) deep neural network classifiers against the full resolution dataset, where locations are assessed at a 20m network resolution using only pedestrian-scale information available to each point of analysis. The models broadly align with intuitions expressed by urbanists and show strong potential for continued development.
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Date   2021-07-02
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arXiv  2106.15364v2
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