Understanding COVID-lockdowns through urban management systems: a novel application of administrative data

The COVID-19 pandemic led to unprecedented ‘lockdowns’ and stay-at-home orders to prevent the spread of infection. Social scientists have analysed mobility during these lockdowns to understand compliance at a population-level, and whether there were systematic barriers to compliance for certain population groups. Much of this analysis has used mobility data from private companies, gathered via smartphones. In this paper, we consider an unexplored source of such data – urban management administrative data – and demonstrate its usefulness for understanding mobility, and what these patterns might reveal about socio-spatial inequality and local economic activity and suggest greater imagination when analysing such data.