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Abstract:
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This talk will present the challenges and opportunities of using computational methods and large-scale, high-resolution urban data to advance social justice and climate action in cities. Meeting the ambitious carbon reduction goals set by major global cities will require new policy tools based on validated, data-driven models that learn from physical, natural, and social theories of urban planning. Specific examples will focus on building energy efficiency and carbon reduction strategies, high-resolution models of disaster response and recovery, and bias and discrimination in algorithmic decision-making applied to city predictive models.
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