Abstract

As climate change, health, and social inequities call for more integrated planning approaches, data-driven urban analytics can support more socially sustainable urban design strategies. This article reports on an innovative pedagogical initiative, which introduced data-driven methods in academic teaching practices. Developed as a library of self-learning resources to complement existing study methods, the tools have been tested for their capacity to address social science issues, extracting insights from data on the underlying causes of urban problems. Our approach helped bridge the gap between research and schematic design phases and produced valuable learning outcomes related to liveability in disadvantaged urban neighborhoods.

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