Urban Shape and Built Density Metrics through the Analysis of European Urban Fabrics Using Artificial Intelligence

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.

Impact of AI-Based Tools and Urban Big Data Analytics on the Design and Planning of Cities

“…This study is based on a systematic review described by Cook, Mulrow, and Haynes [57], aiming to adopt a replicable, scientific process to minimise bias through an exhaustive literature search and by providing an audit trail of procedures and conclusions [48]. Integrative reviews, as the broadest type of research review method, allow for the simultaneous inclusion of experimental and non-experimental research to fully understand the phenomenon of concern [58].…”

“…The conducted review shows that the types of AI-based tools that are most widely used in urban planning are those from the evolutionary computing and spatial DNA group: mostly artificial neural network [4,44,45] both of the convolutional [27,46] and recurrent [47] types but also unsupervised machine learning, mainly self-organising maps (SOMs) [48,49]. The next most numerous group contains examples of the Knowledge-based intelligent systems group, where the most important tools are fuzzy logic [29,50] and rough sets [50].…”

A century of decoupling size and structure of urban spaces in the United States

“…These scaling and allometric relationships, in terms of urban size, density, and form [13][14][15] are highly consequential for a wide range of socio-economic and environmental outcomes [16][17][18] . Unfortunately, the paucity of long-term urban spatial data generally confined studies to relatively short time horizons or selected geographic contexts 19,20 , making it difficult to fully understand urban growth, or (changes in) urban form [21][22][23][24] and similarity [25][26][27] . As such, much of our knowledge of urban change rests on cross-sectional data and relatively short windows of observation that do not fully encompass the complete development trajectories of cities.…”