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Prediction of 'artificial' urban archetypes at the pedestrian-scale through a synthesis of domain expertise with machine learning methods
[article]
2022
arXiv
pre-print
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
arXiv:2106.15364v3
fatcat:vfzkvvbe6fetvoi4il2bp3mlki