Predicting Gentrification in England: A Data Primitive Approach

Abstract

Geodemographic classifications are useful tools for segmenting populations and have many applications but are not suitable for measuring neighbourhood change over time. There is a need for an approach that uses data of a higher spatiotemporal resolution to capture the fundamental dimensions of processes driving local changes. Data primitives are measures that capture the fundamental drivers of neighbourhood processes and therefore offer a suitable route. In this article, three types of gentrification are conceptualised, and four key data primitives are applied to capture them in a case study region in Yorkshire, England. These areas are visually validated according to their temporal properties to confirm the presence of gentrification and are then assigned to a high-level gentrification type. Ensemble modelling is then used to predict the presence, type, and temporal properties of gentrification across the rest of England. The results show an alignment of the spatial extent of gentrification types with previous gentrification studies throughout the country but may have made an overprediction in London. The periodicities of (1) residential, (2) rural, and (3) transport-led gentrification also vary throughout the country, but regardless of type, gentrification in areas within close proximity to one another have differing velocities such that they peak and complete within similar times. These temporal findings offer new, more timely tools for authorities in devising schedules of interventions and for understanding the intricacies of neighbourhood change.