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Quantitatively measuring U.S. legislative district compactness using survey methods and machine learning models.

Aaron Kaufman, PhD candidate at Harvard University's Department of Government and the Institute for Quantitative Social Science, discusses using survey methods and machine learning models to quantitatively measure U.S. legislative district compactness, including what legislative district compac...

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Bibliographic Details
Other Authors: Kaufman, Aaron (Academic.)
Format: Video
Language:English
Language Notes:Closed-captions in English.
Published: London : SAGE Publications Ltd, 2019.
Subjects:
Quantitative research.
Legislators > United States.
Social surveys.
Machine learning.
Recherche quantitative.
Parlementaires > États-Unis.
Enquêtes sociales.
Apprentissage automatique.
members of Congress.
Legislators
Machine learning
Quantitative research
Social surveys
United States
Online Access:Connect to this streaming video
Description
Summary:Aaron Kaufman, PhD candidate at Harvard University's Department of Government and the Institute for Quantitative Social Science, discusses using survey methods and machine learning models to quantitatively measure U.S. legislative district compactness, including what legislative district compactness is and how it is measured, the purpose of this research, data collection and analysis, challenges faced and overcome, advice for those interested in similar research, and the impact of this research.
Physical Description:1 online resource (1 video file (00:11:12)) : sound, colour
ISBN:9781526499264
1526499266