Data Science and Cities: A Critical Approach · Harvard Data Science Review

Sensors increasingly permeate our lives and generate a plethora of data, which has transformed the way we live in cities. Planners have been using data-science to improve our understanding of urban issues. While other domains have highlighted concerns with big data collection, aggregation, and analytical methods to understand different phenomena, urban planning has an additional aspiration: not only to understand, but to transform society through planning. Thus, on top of critically approaching data collection and analytical methods, for the emergent field of urban science to become a distinctively unique body of knowledge, it must examine the ontological and epistemological boundaries of the big data paradigm and how it affects urban decision-making processes and their short- and long-term consequences in cities. Data-driven approaches have transformed the way we analyze, design and make policy decisions in cities. This has been true during the COVID-19 pandemic, where countries have used self-reported information and tracing apps to map infected people. South Korea Corona Map, for example provides the addresses of all infected residents, and Singapore COVID19 maps each case and their social networks, to help other people identify if they had contact with an infected person, took the same flight or used the same urban facilities to be aware of their risk of contagion.