Urban form and Covid-19 in Greater London: an urban morphometric approach

The Covid-19 pandemic has generated a voluminous debate on whether urban density causes more propagation and deaths from the virus. On the one hand, such a debate brought out the long-standing 19th century assumption that density is dangerous, even though it was confused with overcrowding, and thus must be reduced. On the other hand, recent scientific research is inconclusive in determining whether density is related to more virus propagation and deaths. Furthermore, any other features of urban form which might help explaining the phenomenon are not accounted for.

Figure 1. Covid-19 deaths per 1,000 residents (left) and Covid-19 cases per 100,000 residents in Greater London (March to June 2021) (right). 

In this work, we provide a more thorough understanding of the relationship between urban form, including density, and Covid-19-related cases and deaths at the small scale by focusing on Greater London. First, the city is described through a comprehensive set of 69 morphometrics, computed via moempy, the urban morphometric toolkit used in Urban MorphoMetric Analysis (UMM). Such morphometrics include, for example, building footprint, local closeness and floor area ratio. Second, data on Covid-19-related deaths and cases are obtained for official neighbourhoods from openly accessible governmental datasets (Figure 1). Having aggregated the 69 morphometrics for such areas, feature selection techniques are used to narrow them down. The relationship between the selected sets and both target variables is then assessed through linear regression, while controlling for socioeconomic and age factors.

Figure 2. Control model for Covid-19 deaths per 1k residents (top) and model of the residuals of the control model with selected morphometrics (bottom).

Results show that both Covid-19-related deaths and cases are much more associated with the control factors (around 20% of explained variance) than to features of urban form (around 4% of remaining variance) (Figure 2). In the marginal role played by the latter, urban density, measured as floor area ratio, is one of the strongest predictors and is inversely correlated with both target variables, meaning that less density is associated with more cases and deaths. The overall picture of the London area most hit by the negative effects of the Covid-19 pandemic is that of a low-density suburb, with poor street network connectivity, an ordinary urban fabric with relatively small street sections, dotted by free-standing structures, such as tower blocks and isolated large buildings (Figure 3).

Figure 3. Street views of worst affected neighbourhoods in terms of Covid-19-related deaths (top row) and cases (bottom row), where models perform the best. Source: Google Street View.

Read the full paper

Venerandi, A., Aiello, L., & Porta, S. (under review). Urban form and COVID-19 deaths and cases in Greater London: an urban morphometric approach. Environment and Planning B: Urban Analytics and City Science.