Summary
Chapters
Video Info
Summary
University of Chicago's Julia Koschinsky, PhD, Executive Director of the Center for Spatial Data Science, and Jamie Saxon, PhD, post doctoral fellow, discuss their research on gerrymandering using spatial data analytics and algorithms, including research applications of spatial data science, relationship between spatial analytics and data science, applying computational methods to spatial analysis, gerrymandering, clustering methodology, data sources, data collection and analysis, advice to a novice in computational social science.
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Chapter 1: Spatial Data Science and its Research Application in Gerrymandering
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Chapter 2: How Do Data Science and Spatial Analytics Impact Each Other?
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Chapter 3: How Do You Apply Computational Methods to Spatial Analytics?
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Chapter 4: What Are Some Overlapping Concepts in Spatial Analytics and Computational Methods?
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Chapter 5: How Would You Define Gerrymandering?
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Chapter 6: Why Was It Important to Use Multiple Clustering Methods for This Project and How Do You Obtain the Data for Your Clustering Tests?
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Chapter 7: Why Is There Such a Focus on Compactness in Redistricting?
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Chapter 8: What Is the Data Collection and Analysis Process for This Project? Where Did the Data Come From?
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Chapter 9: What Advice Would You Give to Someone New to Computational Social Science?
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