Emmanuel Letouzé on Big Data for Climate Change & Disaster Resilience

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Data–Pop Alliance has been conducting ongoing research on Big Data, climate change and environmental resilience. With funding from the UK’s Department for International Development (DfID), we published a synthesis report evaluating the opportunities, challenges and required steps for leveraging the new ecosystem of Big Data and its potential applications and implications for climate change and disaster resilience. The report fed into the World Humanitarian Summit to be organized in Istanbul in May 2016.

This is the first video in a series of companion pieces that offer insights from the synthesis report.


This companion video to the synthesis report “Big Data for Climate Change and Disaster Resilience: Realising the Benefits for Developing Countries,” was funded by UK’s Department for International Development (DfID) was designed to probe experts to speak more about their work and their ideas about the potential and challenges of Big Data.

Titled “Emmanuel Letouzé on Big Data for Climate Change & Disaster Resilience ” goes into detail about the work of Data-Pop Alliance at the intersection of Big Data, climate change, and disaster resilience. Emmanuel speaks about the Big Data ecosystem could be leveraged to make communities more resilient to climate change. He highlights the point that resilience is not only “bouncing back” from disasters, but also the ability to respond adaptively to climate changes.

Emmanuel Letouzé (@ManuLetouze)

    Emmanuel Letouzé is the Director and co-Founder of Data-Pop Alliance. He is a Visiting Scholar at MIT Media Lab, a Fellow at HHI, a Research Associate at ODI, a Non-Resident Adviser at the International Peace Institute (IPI). His interests are in Big Data and development, economic demography, conflict and fragile states, poverty, migration, official statistics and fiscal policy.

For more on our series:
Watch the full video
Read the summary of the DfID videos and podcasts
Watch the full playlist of podcasts