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Urban Computing for Planning Energy Efficient and Healthier Cities

Marta Gonzalez

My work uses data science to characterize how humans interact with the built and natural environments, 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 in planning decisions by providing timing recommendations and assigning monetary values to modulations of PEV start and end charging times. According to the US Energy Information Administration, the number of PEVs in the United States doubled between 2013 and 2015 and are expected to reach 20 million by 2020. 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 datasets. The goal is to enable science-informed policy by understanding various interdependencies 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. For this session, the seminar will be available via livestream in Stanley 106 at UC Berkeley.  Dr. Gonzalez will present this talk live at NASA Ames Research Center.

Speaker: Marta Gonzalez,  UC Berkeley

Wednesday, 02/12/20

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Stanley Hall

UC Berkeley
Room 106
Berkeley, CA 94720