Johns Hopkins University 1 credit Biostatistics
Provides a practical introduction to geospatial data and methods, with an emphasis on understanding and analyzing real-world spatial datasets. Begins with the basics of geospatial data types and formats, coordinate systems, map projections, and spatial data structures. Covers exploratory spatial data analysis and visualization, including map-making, spatial summaries, and detecting spatial patterns. Introduces core methods for geospatial analysis such as spatial smoothing and interpolation (e.g., variograms, Gaussian process and basic kriging ideas), spatial regression, Bayesian methods, and methods for large spatial data. Includes examples of usage of spatial software such as R, giving students practical skills to import, clean, visualize, and analyze geospatial data for applications in environmental science, public health, and urban studies.
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