COVID-19 as an accelerator of spatial (in)justices: a global comparative project

Around the end of 2019, in Wuhan, Hubei Province, China, the first confirmed cases of COVID-19 were identified, and from then on, the world we were used to knowing changed globally. The pandemic had and will have enormous economic, social, and structural impacts, and the consequences are yet to be defined. It is precisely these unanswered questions that capture the interest of many researchers in various research fields. Besides the focus on medical studies, a relevant share of research connected the dynamics of the pandemic to cities and how they responded, bringing back the debate on their potential vulnerabilities. Indeed, the outbreak has drawn significant global attention to how starkly differentiated the spread was across different neighbourhoods. This phenomenon triggers questions regarding the conditions of "the urban" even before the pandemic kicked in. It is no longer solely a matter of who was most affected, but also where. The thesis, partly developed at the Urban Morphology and Complex Systems Institute, aims to research the uneven impact that COVID-19 has had on different cities (New York City, London, Rome, and Sao Paulo) at a spatially granular level. It attempts to understand if spatial features (including distance to urban infrastructure and lack of facilities) have directly or indirectly exposed the less advantaged part of the population to the outbreak. Specifically, the approach, in unity with the claims of Jennifer Robinson, was to compare cities that are to a greater or lesser extent different, to shed light on general and shared processes, as well as divergences and singularities. Whereas most studies have focused on density as the main contributor to the spread of the pandemic, with inconclusive results, the thesis aims at "complexify" the discourse around the socio-spatial "determinants" of the pandemic, providing a more multifaceted picture of the factors influencing the spread or mortality of the outbreak. Methodologically, this dissertation proposes a hybrid approach: quantitative - statistical inference via Ordinary Least Squares (OLS) and Geographically Weighted (GWR) regression models - and spatial analytics (GIS), and qualitative (to link the different case studies). The analysis was structured in four distinct models: a first qualitative spatial analysis of the pandemic's spread and mortality patterns. Then, a set of regression models was used, initially, to analyse the different components, and then, a last comprehensive model drawing together all the elements identified. The power of a comparative global project, especially when championing diversity rather than rejecting it, is that it leads to the emergence of difference, which, as a result, generates questions. It has, therefore, the power to foster research and shed light on urban dynamics by looking "elsewhere". This thesis displays relevant findings concerning the role of urban infrastructure and facilities during the pandemic, both in terms of spread and mortality. It brings forward divergencies across the different case studies, which generate questions that are pivotal to investigate in future research to be a little closer to understanding spatial inequalities and the outbreak.