  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 that distinguishing `artificial'
towns from their more walkable and mixed-use `historical' equivalents. The
dataset is developed from network centrality, mixed-use, land-use
accessibility, and population density measures as proxies for spatial
complexity, which are computed at the pedestrian-scale for 931 towns and cities
in Great Britain. Using officially designated `New Towns' as a departure point,
a series of clues is then developed. First, using an iterative
human-in-the-loop procedure, a supervised classifier (Extra-Trees) is
cultivated from which 185 `artificial' locations are identified based on data
aggregated to respective town or city boundaries. This information is then used
to train supervised and semi-supervised (M2) deep neural network classifiers
against the higher resolution dataset. The models broadly align with intuitions
expressed by urbanists and show potential for continued development to broach
ensuing challenges pertaining to: selection of curated training exemplars;
further development of techniques to accentuate localised scales of analysis;
and methods for the calibration of model probabilities to align with the
intuitions of domain experts.
