A scalable method to quantify the relationship between urban form and socio-economic indexes

Abstract

The world is undergoing a process of fast and unprecedented urbanisation. It is reported that by 2050 66% of the entire world population will live in cities. Although this phenomenon is generally considered beneficial, it is also causing housing crises and more inequality worldwide. In the past, the relationship between design features of cities and socio-economic levels of their residents has been investigated using both qualitative and quantitative methods. However, both sets of works had significant limitations as the former lacked generalizability and replicability, while the latter had a too narrow focus, since they tended to analyse single aspects of the urban environment rather than a more complex set of metrics. This might have been caused by the lack of data availability. Nowadays, though, larger and freely accessible repositories of data can be used for this purpose. In this paper, we propose a scalable method that delves deeper into the relationship between features of cities and socio-economics. The method uses openly accessible datasets to extract multiple metrics of urban form and then models the relationship between urban form and socio-economic levels through spatial regression analysis. We applied this method to the six major conurbations (i.e., London, Manchester, Birmingham, Liverpool, Leeds, and Newcastle) of the United Kingdom (UK) and found that urban form could explain up to 70% of the variance of the English official socio-economic index, the Index of Multiple Deprivation (IMD). In particular, results suggest that more deprived UK neighbourhoods are characterised by higher population density, larger portions of unbuilt land, more dead-end roads, and a more regular street pattern.

Comparing patterns of hurricane washover into built and unbuilt environments

“…We illustrate control on sediment hazard by built fabric in the context of coastal hurricanes, but the premise of our analysis extends to flow-driven hazards in tsunami, fluvial, debris-flow, and volcanic contexts. Research is beginning to demonstrate links between fabrics and socioeconomic metrics as a means of informing spatial planning and urban design (Venerandi et al, 2018). Two key goals of the Sendai Framework for Disaster Risk Reduction (2015-2030) are to "reduce direct economic loss in relation to [gross domestic product]" and to "reduce disaster damage to critical infrastructure and disruption of basic services" (UN, 2015).…”

A Fine-Grain Multi-Indicator Analysis of the Urban Form of Five Informal Settlements in East Africa

“…In recent years, quantitative studies have emerged, in part as a result of technological development, which have advanced the understanding of urban environments through digital processing and statistical analysis of large-scale fine-grained data sets. Such studies have found links between urban form and a wide range of issues, such as health, energy consumption, and liveability of neighbourhoods [5][6][7]. However, such analyses have only been applied to the study of informal settlements to a limited degree, particularly in the context of Sub-Saharan Africa.…”

A Machine Learning Approach to Study the Relationship between Features of the Urban Environment and Street Value

“…However, the urban environment is a much more complex entity, characterised by multiple factors that act together rather than in isolation [7,8]. More recently, a methodology to study socioeconomic indexes that accounted for multiple urban features and utilised multivariate spatial regression rather than correlation has been proposed [9]. It was tested on six UK cities and showed that more population density, unbuilt land, and dead-end roads, as well as a more regular street layout were associated with more socioeconomically deprived neighbourhoods.…”