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
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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