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X-WR-CALNAME;VALUE=TEXT:Interactive Analysis of Big Geospatial Data with High-Performance Computing
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SUMMARY:Interactive Analysis of Big Geospatial Data with High-Performance Computing
DESCRIPTION:<p>	Presenter: Benjamin Lewis</p><p>	Co-authors: Devika Kakkar and Wendy Guan</p><p>	In: Esri User Conference 2022. Session: GI Science: Transactions in GIS</p><p>	Abstract: Researchers are increasingly working with large geospatial datasets that contain hundreds of millions of records. At this scale, traditional GIS methods may fall short and new approaches are needed to analyze the data. The objective of this work is to develop methods to interactively analyze and visualize big datasets and demonstrate it with a case study of partisan segregation. Historically, measurement of partisan segregation has been limited to large geographic areas such as countries, since researchers usually relied on analyzing data at aggregated levels. We have measured partisan segregation down to the level of an individual for 180 million U.S. voters using advanced geospatial data science and high performance computing. This case study highlights the use of interactive method development to create the most detailed metrics to analyze partisanship and visualize it within small geographic units such as neighborhoods.</p><p>	<a data-url="https://docs.google.com/presentation/d/1heMeGu59oJZ5hB8fCB_OMTU_qv0WyJ0zprsmfRX8qtk/" href="https://docs.google.com/presentation/d/1heMeGu59oJZ5hB8fCB_OMTU_qv0WyJ0zprsmfRX8qtk/" target="_blank" title="Slides">Slides</a></p>
LOCATION:San Diego, CA
STATUS:CONFIRMED
DTSTART:20220712T153000Z
DTEND:20220712T153000Z
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