The Berkeley Institute for Data Science: Part II
In the first part of this blog post, I reported on the opening of the new Berkeley Institute for Data Science (BIDS). Today, I am going to share with you some of the ways that UC Berkeley scientists are using and analyzing “big data.” At the BIDS event, there were both talks and poster presentations highlighting recent projects. Here’s just a small taste of what was happening!
Solomon Hsiang, Assistant Professor of Public Policy
Hsiang’s work focuses on the effect of the environment on human society, and recently his lab reconstructed dozens of storms in the Philippines and linked that information to detailed, household survey data. They found that in a given year after a storm, there was an increased risk of not having basic assets such as walls, electricity and plumbing. These storms cause a localized economic depression, and people significantly reduce their expenses on nutritious food, education and medical care, while infant mortality significantly increases. Matching data to the distribution of where people live in the Philippines, and based on hidden costs and economic losses, these events tend to be “roughly 15 times more costly than what you see in the newspaper” — highlighting the importance of rebuilding efforts after storms.
Rosemary Gillespie, Professor of Environmental Sciences
The Berkeley Eco-informatics Engine will integrate biological and environmental data to learn more about how organisms respond to global changes. Currently, the default is to use the physiological constraints of organisms, such as temperature and precipitation tolerance, to predict where organisms might go, but this method does not always predict behavior well. The Berkeley Eco-Informatics Engine will bring together huge amounts of diverse data in an open API development to allow integration and analysis of huge amounts of data. This will include looking at museum specimens, each of which is associated with a space and time, and using geo-spatial base layers such as land cover, climate, and the history of the landscape to predict response to climate change.
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