Director of Civic Analytics, Constantine Kontokosta, gave an overview of his team’s work during his keynote speech at the 2020 Seoul Big Data Forum. In his concluding remarks, he outlined several “tensions and balances in urban science,” with one of those as “state-of-the-practice v. state-of-the-art.” He noted:
The state-of-the-art in terms of how we use geolocation data, social media data, how we use more advanced computational methods or remote sensing for instance, doesn’t always connect with what city agencies are capable of implementing in practice. So, there is this disconnect between what is possible in terms of computational methods and big data analytics and what would actually be useful at the end of the day to the people who are making decisions at city agencies. So there is a tension somewhat here between what perhaps academics might want to do and what those in the public service really need.
An example of this was described in a recent piece in the Toronto Star:
New York University planning and urban analytics expert Constantine Kontokosta offers another caution. Trash bin sensors designed to monitor when a container needs emptying could, in theory, provide data that lets city officials apply algorithms to optimize collection routes by using GPS mapping tools to direct trucks only to full bins, thus saving money on fuel and labour. However, in a 2018 paper, Kontokosta writes that such analysis might conflict with other municipal policies, such as the need to abide by collective agreements. “The computing challenges are solvable,” he notes. “(T)he real uncertainty lies with how to integrate data-driven processes into public sector management.”