Visual Change in the Urban Landscape: Taste, Gentrification & Displacement

Abstract

Measuring the pace, characteristics and spatial distribution of gentrification is important to developing policies to mitigate its negative consequences, most crucially displacement. Typically, this is done through an analysis of census data on demographic, socioeconomic or housing change. However, this approach has numerous shortcomings, including the homogenizing effect on differences within neighbourhoods and the infrequency of census data collection. Visual analysis, particularly when examining multiple temporal views of the same location, has the potential to render visible fine-grained detail about spatial, economic and cultural changes within the urban landscape. Google Street View (GSV) is emerging as a source of repeat photography data. In this thesis, GSV is employed for analysis within a number of neighbourhoods and retail streets in Hamilton, Ontario. Coding and analyzing GSV images between 2009 and 2021 reveals an array of specific home upgrades, retail turnover as well as aesthetic changes that reflect middle-class tastes, values and lifestyles that suggest more upgrading than found within conventional statistics or dominant narratives about the city. Mapping these changes paints a complex, and fine-grained, block-by-block picture of gentrification that reveals why some areas are more conducive to gentrification than others and how retail gentrification can lead to both direct and indirect displacement. This analysis is important for critical visual methodologies, gentrification and neighbourhood change theories in addition to planning and policymaking.