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Introduces exploratory data analysis in environmental health, covering its definition, techniques, and applications in georeferenced health data analysis.
Explains the significance analysis of spatial autocorrelation using Moran's I and random permutations, emphasizing the importance of spatial weighting.
Covers geographic information systems, digital elevation models, derived variables, visibility analysis, shadow casting, solar radiation, drainage lines, and flood zones.
Covers discrete geographic variables and their geometric properties, including how to characterize them using indices like the Gravelius compactness index.
