@misc{10481/59549,
year = {2019},
month = {11},
url = {http://hdl.handle.net/10481/59549},
abstract = {In recent decades, the concept of urban density has been considered key to the creation of
sustainable urban fabrics. However, when it comes to measuring the built density, a difficulty has
been observed in defining valid measurement indicators universally. With the intention of identifying
the variables that allow the best characterization of the shape of urban fabrics and of obtaining the
metrics of their density, a multi-variable analysis methodology from the field of artificial intelligence
is proposed. The main objective of this paper was to evaluate the capacity and interest of such a
methodology from standard indicators of the built density, measured at various urban scales, (i) to
cluster differentiated urban profiles in a robust way by assessing the results statistically, and (ii) to
obtain the metrics that characterize them with an identity. As a case study, this methodology was
applied to the state of the art European urban fabrics (N = 117) by simultaneously integrating 13
regular parameters to qualify urban shape and density. It was verified that the profiles obtained
were more robust than those based on a limited number of indicators, evidencing that the proposed
methodology offers operational opportunities in urban management by allowing the comparison of a
fabric with the identified profiles.},
organization = {This research was funded by the University of Granada, grant number PP2016-PIP09 and their authors.},
publisher = {MDPI},
keywords = {Urban shape},
keywords = {Built density},
keywords = {Urban fabric},
keywords = {Artificial neural network},
keywords = {Self-Organizing Maps},
title = {Urban Shape and Built Density Metrics through the Analysis of European Urban Fabrics Using Artificial Intelligence},
doi = {10.3390/su11236622},
author = {Abarca Álvarez, Francisco Javier and Campos Sánchez, Francisco Sergio and Osuna Pérez, Fernando},
}