Cities, COVID-19, and counting

Abstract

The COVID-19 pandemic had immediate and potentially long-lasting impacts on cities. Yet, the ability to assess, monitor, and analyze the wide-ranging effects of the pandemic has been stymied by data challenges. The pandemic elevated the need for, and reliance on, a wide range of data sources. We discuss four data challenges related to understanding the impact of the pandemic on cities. First, we explore how shifts in public policy and the decisions of private companies altered data collection priorities, availability, and reliability. Second, we discuss temporal dimensions, including the speed of data retrieval and frequency of data collection. Third, we identify the growing use of unexpected sources, which often feature a lack of rigor and consistency. Fourth, we explore the spatial scale of study and highlight questions about the interpretation of boundaries constituting the city. We use examples from the City of Toronto to ground our observations while also pointing to broader issues. We note that the tension between rapid, novel data and slow, consistent data continues to evolve and argue that a deeper appreciation and analysis of, and access to, myriad sources of data are necessary to understand the immediate and long-term impacts of COVID-19 on cities. Beyond the pandemic, our essay contributes to ongoing and emerging debates regarding the use of big data to understand the challenges facing cities and society.