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Synopsis

Cities are under growing pressure to become more sustainable and equitable as they face rapid urbanisation and climate change. While advances in urban sensing and volunteered geographic information are opening new doors for data-driven planning, most models remain fragmented and struggle to make sense of large-scale, diverse urban data. This research introduces the concept of cities as graphs, expanding on the familiar idea of street networks to unlock advanced geospatial machine learning across multiple data sources. It introduces open-source tools to generate urban graphs for any city worldwide, along with open datasets that support a diverse urban challenges such as predicting building function at the scale of entire cities, decarbonising multiple cities, and improving population health at the national scale. By addressing real-world problems, urban graph analytics can deliver more holistic, scalable insights and give planners new ways to design cities that are both liveable and sustainable.

Speaker

Winston Yap is a PhD Candidate in Urban Analytics at the National University of Singapore, where he researches urban complexity and geospatial data science. His work develops open-source computational tools and urban datasets to support scalable machine learning for understanding socio-spatial inequalities in health and sustainability across cities worldwide. His work has been published in leading journals including Nature Sustainability, Scientific Data, npj Urban Sustainability, CEUS, and EPB, and presented at major international urban science conferences. He was a visiting scholar at the MIT Senseable City Lab and a World Cities Summit Young Leader. Previously, he worked as a Research Associate at the Lee Kuan Yew Centre for Innovative Cities, contributing to projects on ageing urbanism and citizen-led urban science in Southeast Asia.