Johns Hopkins University 3 credits Geographic Information Systems
This course introduces the foundations of Spatial Statistics and its applications in spatial data science workflows. The course starts with an introduction to statistics and spatial statistics and introduces foundational ideas behind spatial statistics. In particular, unsupervised spatial learning methods such as density-based clustering, regionalization algorithms, and multivariate clustering will be introduced. Frequently used supervised spatial learning methods, such as geographically weighted regression, and general-purpose statistical methods used in spatial analysis, such as random forest and generalized linear regression, are also introduced. The course highlights the importance of statistical visualizations, dimension reduction, and spatial data wrangling by introducing methods and frameworks in these areas. Students will use geospatial technology to solve problems using spatial statistics and will be exposed to cutting-edge Geoprocessing methods in spatial statistics
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