Handbook of Urban Segregation
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Handbook of Urban Segregation

Edited by Sako Musterd

The Handbook of Urban Segregation scrutinises key debates on spatial inequality in cities across the globe. It engages with multiple domains, including residential places, public spaces and the field of education. In addition it tackles crucial group-dimensions across race, class and culture as well as age groups, the urban rich, middle class, and gentrified households. This timely Handbook provides a key contribution to understanding what urban segregation is about, why it has developed, what its consequences are and how it is measured, conceptualised and framed.
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Chapter 22: Integrating infrastructure and accessibility in measures of bespoke neighbourhoods

John Östh and Umut Türk


The use of k-nearest neighbour (k-nn) approaches for the creation of bespoke neighbourhoods has become more common in segregation research in recent years. The reasons are manifold but include increased availability of high-resolution data, increasing computational power and the development of software designed to process huge numbers of k-nn commands. In this chapter, we present and test a new geo-computational add-on that has been introduced in the latest version of EquiPop (EquiPop Flow). An important novelty is that bespoke neighbourhoods do not necessarily need to grow radially until they reach a designated k-value but can make use of user-defined networks to grow at different speeds at different locations, such as following street and transportation infrastructure. We compare the geographical compositions of two different k-nn based bespoke neighbourhood techniques and discuss the pros and cons of expanding traditional k-nn computations to include data on infrastructure. Results indicate that infrastructure-integrating bespoke neighbourhoods are considerably better in depicting neighbourhoods, especially in areas with complex geographies that restrict mobility in some directions. However, the increase in computational time and complexity in setting up a network k-nn model makes a traditional radial growth approach attractive in areas where variation in connectivity between locations is limited.

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