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Data for Development

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The recent explosion in rich data sources and powerful technologies for analyzing them is radically reshaping development research and strategies for building sustainable economies around the world. For example, satellite imagery combined with call data records can track disease outbreaks in even the remotest villages. Social media data are advancing what is known about the genesis of political protests and government responses to them.

The Data for Development (DDI) initiative at the King Center, home to some of the world’s leading development researchers, is uniquely positioned to explore the relevance of these often low-cost, unconventional data streams and to help academics, policymakers, and business leaders craft solutions with real-world impacts on global poverty.

Four Pillars: 

  • Research and collaboration - Enabling and supporting sustained interactions between experts in both data science and development are crucial to harnessing the enormous potential of new data streams. Faculty-led research efforts, for instance, are using high-resolution satellite imagery and machine learning to study “hidden” pockets of poverty globally. A separate project is designing a scalable, low-cost method for accurately predicting crop yields amid climate change. 
  • Student training - A project-based class brings together Stanford undergraduates studying machine learning and development, while a graduate summer school course convenes students in the social sciences from around the world. 
  • External partnerships - As private-sector companies and public agencies increasingly open up their data to development researchers, the Initiative will host an annual meeting to foster communication and the sharing of ideas among partners, which include Google, Facebook, and the World Bank. 
  • Data creation and curation - The Initiative identifies the best methods for generating datasets that lead to scalable development programs and is constructing an open-source web platform for development experts worldwide.

Leadership:

Affiliated Faculty:

For more information about the Data for Development initiative, please contact King Center Executive Director Jessica Leino at jleino@stanford.edu.

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Putting machine learning to work for development

Researchers of the Data for Development initiative created a new tool to measure poverty by combining machine learning with street-level imagery from 48 countries.  

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satellite image of a border town
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Satellites enhance sustainable development inquiries

Data for Development King Center researchers document progress in using satellite imagery and machine learning techniques to study development trends.

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