Smart cities, big data and urban policy: Towards urban analytics for the long run

The analysis of big data is deemed to define a new era in urban research, planning and policy. Real-time data mining and pattern detection in high-frequency data can now be carried out at a large scale. Novel analytical practices promise smoother decision-making as part of a more evidence-based and smarter urbanism, while critical voices highlight the dangers and pitfalls of instrumental, data-driven city making to urban governance. Less attention has been devoted to identifying the practical conditions under which big data can realistically contribute to addressing urban policy problems. In this paper, we discuss the value and limitations of big data for long-term urban policy and planning. We first develop a theoretical perspective on urban analytics as a practice that is part of a new smart urbanism. We identify the particular tension of opposed temporalities of high-frequency data and the long duree of structural challenges facing cities. Drawing on empirical studies using big urban data, we highlight epistemological and practical challenges that arise from the analysis of high-frequency data for strategic purposesand formulate propositions on the ways in which urban analytics can inform long-term urban policy.

Automated summary

The analysis of big data is expected to define a new era in urban research, planning, and policy-making, promising smoother decision-making processes as part of a more evidence-based and smarter urbanism. However, there are also risks and challenges associated with over-reliance on data. Currently, there is limited research on how big data can realistically contribute to addressing urban policy problems.