GIS Data Science

High Performance Computing for Address Level Climate Data Extraction

A key objective of multiple public health researchers the CGA works with is to find ways to improve the health of cohort members by calculating various social and environmental exposures at cohort member address locations. To aid this project objective, the CGA processed daily precipitation, temperature, and humidity estimates for 4,796 cohort address locations for the years 1999 – 2017, resulting in over 73 million patient/days of calculations. Input climate data was the 800-meter resolution...

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Measurement of partisan segregation for 180 million U.S. voters using advanced geospatial data science

Partisan segregation among people has important political and social implications. Historically, such measurements have been limited to county levels but this innovative work enabled Harvard researchers to analyze partisanship down to the level of individuals for the first time. In this work, CGA along with the Department of Government Professor Ryan Enos and graduate student Jacob Brown have leveraged advances in geospatial data science to measure partisan segregation down to the...

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