Visualising urban gentrification and displacement in Greater London

Abstract

Gentrification has long been a contentious issue which has prompted debate among scholars due to variations in its location, timing, context and types of measurements used. Therefore, it is worth seeking a simple and effective approach to measure the processes of gentrification, which enables comparative studies to be conducted across different cities around the world. Using six sets of thematic data from 2001 and 2011 at the neighbourhood level, this study proposes five types of gentrification and displacement by using Chapple and Zuk’s theoretical framework. London was selected as a case study. The results show that gentrification was sweeping in many ways during the 2000s in London, particularly in Inner East London. Some areas in North West London are identified as vulnerable neighbourhoods at risk of displacement and gentrification. Furthermore, it was found that most of the neighbourhoods experiencing ongoing displacement are concentrated in Outer London and Inner South London. The typology provides a useful starting point for planners and policymakers to gain deeper insights into the progress of gentrification in London. Additionally, this work can serve as an example to illustrate the potential for using similar types of open source code and census data to estimate the degree of gentrification in other cities.

Keywords

Gentrification displacement socio-spatial inequality London

Gentrification refers to the process whereby, through an influx of capital and higher-income, higher-educated residents transform a low-income neighbourhood (Chapple and Loukaitou-Sideris, 2019). It is not only a spatial manifestation of socio-economic inequalities, but also acts to exacerbate socio-spatial divisions via the displacement of existing residents (Hochstenbach and Musterd, 2018). Therefore, identifying the progress of gentrification is of great importance, particularly for cities seeking to cope with rapidly growing polarisation. The increasing availability of open data sources enables the discovery of the landscapes of gentrification, while the development of open source code enables replication by planners, researchers and policymakers (Reades et al., 2019). This then makes it possible to carry out a comparative analysis between different cities or conduct longitudinal evaluations to delineate the gentrification trajectories within one city. Accordingly, the main aim of this article was to take Greater London as an example to illustrate the potential for using open data and open source code (see Author’s note) in order to produce a general picture of the gentrification typologies at the neighbourhood level (Lower Super Output Areas (LSOA)).

This analysis used data obtained from: (a) the Office for National Statistics (ONS), (b) the Greater London Authority (GLA) and (c) the Land Registry. Following Chapple and Zuk’s (2016) analytical framework, we first visualised six key thematic maps to depict gentrification related landscapes in 2011. Figure 1 reveals that the percentage of non-white residents and the percentage of people with a higher education follow a similar pattern to that of household income, that is, the lower-income households are located in the eastern and north-western regions of suburban London.

Figure 1.

Cartogram of key themes related to gentrification by populations in Greater London in 2011 (the distortion is based on residential population in 2011). The original maps are provided at the bottom left of each of the embedded six diagrams for comparison.

We combine six indicators to produce a typology of gentrification and displacement in Greater London (see the Appendix for further details). Figure 2 displays the five types of neighbourhoods in the typology: (a) those that are not losing low-income households, (b) those at risk of gentrification, (c) neighbourhoods experiencing ongoing displacement, (d) neighbourhoods experiencing ongoing gentrification and (e) those containing mainly moderate- to high-income households. Overall, gentrification has had far-reaching effects during the 2000s in London. The neighbourhoods experiencing ongoing gentrification are mostly located in Inner London and are distributed across traditionally working-class areas of East London (Freeman et al., 2016; e.g. Barking and Dagenham, and the Olympic development area in Hackney). The neighbourhoods experiencing ongoing displacement are mainly located in Outer London and Inner South London, particularly in the Lambeth and Lewisham areas, accounting for 2.91% of Londoners in total. However, we cannot presume that this is gentrification-induced displacement. These areas experienced a significant loss of low-income households while showing few signs of gentrification between 2001 and 2011. Swathes of neighbourhoods in north-western areas (e.g. Brent, Ealing and Barnet) of London are identified as being at risk of gentrification. Meanwhile, 129 neighbourhoods, accounting for nearly 2.8% of Londoners, are particularly vulnerable to displacement and gentrification. The typology map can further enhance our understanding of the patterns of neighbourhood change in addition to potentially helping to predict future trends.

Figure 2.

Gentrification and displacement typologies for Greater London in 2011 at neighbourhood level (the distortion is based on residential population in 2011). The original map is provided at the bottom left of the diagram for comparison.

Although this work can neither specify the precise magnitude of gentrification nor uncover the mechanism (or causality) of gentrification, it attempts to develop a replicable typology of gentrification and displacement that can serve as the basis for direct comparison across UK cities, as well as other cities around the world with similar census variable availability. Further research could use similar data and codes to replicate the London case in other cities in order to estimate the degree of gentrification using the same comparative basis.

Footnotes

Author’s note

Software: These visualisations were produced using the graphics packages tmap, rgdal, tmaptools and cartogram within the R Studio programming language. The code available to reproduce these figures is available from the following url:

Data details can be found at:

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship and/or publication of this article.

References

Chapple

Loukaitou-Sideris

(2019) Transit-Oriented Displacement or Community Dividends? Understanding the Effects of Smarter Growth on Communities. Cambridge, MA: MIT Press.

Chapple

Zuk

(2016) Forewarned: the use of neighborhood early warning systems for gentrification and displacement. Cityscape 18(3): 109–130.

Freeman

Cassola

Cai

(2016) Displacement and gentrification in England and Wales: a quasi-experimental approach. Urban Studies 53(13): 2797–2814.

Hochstenbach

Musterd

(2018) Gentrification and the suburbanization of poverty: changing urban geographies through boom and bust periods. Urban Geography 39(1): 26–53.

Reades

De Souza

Hubbard

(2019) Understanding urban gentrification through machine learning. Urban Studies 56(5): 922–942.