- Published on 14 February 2018
Although urbanization has many advantages, one of its biggest drawbacks is the rise in socio-economic inequality. There have been some attempts at a qualitative analysis of the relationship between certain city features and social inequality, but these kinds of analyses are hard to replicate. A new research article published in EPJ Data Science proposes a new quantitative computer-based method for how to better understand the link between cites and social inequalities.
(Guest post by Alessandro Venerandi, originally published on the SpringerOpen blog)
The world is undergoing a process of fast and unprecedented urbanization. In 1950, the share of the world’s population living in cities was around 30%. Today, it is around 54%. By 2050, estimates project that the share of urban population will be 66%.
Urbanization is generally seen as a positive phenomenon by governments and institutions, as it is supposed to bring several advantages, for example a more prosperous economy and better and less expensive public services. However, researchers pointed out that urbanization also has its negative effects, such as housing crises and rise in inequality.
In order to better understand the link between urbanization and socio-economics, researchers worldwide applied both qualitative and quantitative techniques. For example, journalist and activist Jane Jacobs observed the streets of New York and came to the conclusion that street-facing buildings, mixed use, and walk-ability were important aspects for thriving neighbourhoods. More recently, Laura Vaughan studied the relationship between levels of street centrality and the spatial distribution of social classes in a London borough, and found that more segregated places, such as back streets, hosted more deprived communities, while more accessible streets, such as high streets, had better-off residents.
Both sets of works provide useful insights into the relationship between cities and socio-economics. However, qualitative works are usually hard to repeat and generalize. Quantitative studies focus on single aspects of cities, even though their complexity would require an analysis based on multiple features.
To tackle these limitations and provide a better understanding of the relationship between cities and socio-economics, we propose a quantitative method that computes metrics of urban form from openly accessible datasets and then models the relationship between such metrics and the socio-economics of neighbourhoods.
We apply this method to the six major British cities (London, Manchester, Birmingham, Liverpool, Leeds, and Newcastle) and find that our selected set of urban features explains up to 70% of the variance of an official socio-economic index, the ‘Index of Multiple Deprivation’ (IMD). In particular, our results suggest that more deprived neighbourhoods are characterised by higher population density, larger shares of unbuilt land, more dead-end roads, and a more regular street pattern.