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Urban Computing for Planning Energy Efficient and Healthier Cities - ONLINE

Marta Gonzalez

My work uses data science to characterize how humans interact with the built and the natural environment seeking to plan for more sustainable and livable cities. Given the increasing ubiquity of plug-in electric vehicles (PEVs) in the Bay Area, I present a study that aims to assist planning decisions by providing timing recommendations and assigning monetary values to modulations of PEV start and end charging times.

According to a report of the Institute for Electric Innovation, the number of PEVs in the United States doubled between 2013 and 2015 and is expected to reach 7% of the annual sales by 2020. Measures are needed to limit the potential power grid instability following this new technology.

In the second part, I present DeepAir, a convolutional neural network platform that combines satellite imagery and urban maps with weather and air monitoring stations data. Our goal is to enable science-informed policy by understanding various inter-dependencies in the quality of the air we breathe. These methodologies are aimed to be fully scalable and open source. The presented methods can be extended to other domains that involve human and environmental interactions.

Speaker: Marta Gonzalez,  UC Berkeley

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Tuesday, 03/31/20


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UC Berkeley

, CA