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.
- Product: SAGE Research Methods Video: Data Science, Big Data Analytics, and Digital Methods
- Type of Content: Case Study
- Title: Studying Gerrymandering Using Spatial Data Analytics & Algorithms
- Publisher: SAGE Publications, Ltd.
- Publication year: 2019
- Online pub date:
- Discipline: Communication and Media Studies, Geography, Political Science and International Relations, Social Policy and Public Policy, Sociology
- Methods: Spatial analysis, Data science, Clustering, Unsupervised learning, Computational social science
- Duration: 00:19:44
- Keywords: algorithms, census data, cluster detection, coding, collaboration, computer science, data collection, data visualisation, geographical information systems, geography, gerrymandering, GIS design, heuristic methods in spatial analysis, hierarchical classification systems, large-scale research, maps and map-making, multidisciplinary teams, National Science Foundation, natural language processing, open source software, partisan realignment, political representation, principal component analysis, programming and scripting languages, redistricting, redistricting of congressional districts, regionalization, research design, research questions, Social science research, Spatial analysis, Spatial data integration, Spatial data mining, Spatial data models, Spatial econometrics, Synergy, unsupervised classification, YouTube
Academic: Julia Koschinsky
Academic: Jamie SaxonOnline ISBN:9781526496584Copyright: (c) SAGE Publications Ltd., 2019