Measuring socioeconomic deprivation of cities in an accurate and timely fashion has become a priority for governments around the world, as the massive urbanization process we are witnessing is causing high levels of inequalities which require intervention. Traditionally, deprivation indexes have been derived from census data, which is however very expensive to obtain, and thus acquired only every few years. Alternative computational methods have been proposed in recent years to automatically extract proxies of deprivation at a fine spatio-temporal level of granularity; however, they usually require access to datasets (e.g., call details records) that are not publicly available to governments and agencies. To remedy this, we propose a new method to automatically mine deprivation at a fine level of spatio-temporal granularity that only requires access to freely available user-generated content. More precisely, the method needs access to datasets describing what urban elements are present in the physical environment; examples of such datasets are Foursquare and OpenStreetMap. Using these datasets, we quantitatively describe neighborhoods by means of a metric, called {\em Offering Advantage}, that reflects which urban elements are distinctive features of each neighborhood. We then use that metric to {\em (i)} build accurate classifiers of urban deprivation and {\em (ii)} interpret the outcomes through thematic analysis. We apply the method to three UK urban areas of different scale and elaborate on the results in terms of precision and recall.Comment: CSCW'15, March 14 - 18 2015, Vancouver, BC, Canad
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.
Research in Urban Morphology has long been exploring the form of cities and their changes over time, especially by establishing links with the parallel dynamics of these cities' social, economic and political environments. The capacity of an adaptable and resilient urban form to provide a fertile environment for economic prosperity and social cohesion is at the forefront of discussion. Gentrification has emerged in the past few decades as an important topic of research in urban sociology, geography and economy, addressing the social impact of some forms of urban evolution. To some extent, these studies emphasise the form of the environment in which gentrification takes place. However, a systematic and quantitative method for a detailed characterization of this type of urban form is still far from being achieved. With this paper, we make a first step towards the establishment of an approach based on "urban morphometrics". To this end, we measure and compare key morphological features of five London neighbourhoods that have undergone a process of piecemeal gentrification. Findings suggest that these five case studies display similar and recognisable morphological patterns in terms of their built form, geographical location of main and local roads and physical relationships between street fronts and street types. These initial results, while not implying any causal or universal relationship between morphological and social dynamics, nevertheless contribute to; a) highlight the benefits of a rigorous quantitative approach towards interpreting urban form beyond the disciplinary boundaries of Urban Morphology and b) define the statistical recurrence of a few, specific morphological features amongst the five cases of gentrified areas in London.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.