Spatial Statistics in R

Learn how to make sense of spatial data and deal with various classes of statistical problems associated with it.

Everything happens somewhere, and increasingly the place where all these things happen is being recorded in a database. There is some truth behind the oft-repeated statement that 80% of data have a spatial component. So what can we do with this spatial data? Spatial statistics, of course! Location is an important explanatory variable in so many things - be it a disease outbreak, an animal's choice of habitat, a traffic collision, or a vein of gold in the mountains - that we would be wise to include it whenever possible. This course will start you on your journey of spatial data analysis. You'll learn what classes of statistical problems present themselves with spatial data, and the basic techniques of how to deal with them. You'll see how to look at a mess of dots on a map and bring out meaningful insights.

Introduction
-After a quick review of spatial statistics as a whole, you'll go through some point-pattern analysis. You'll learn how to recognize and test different types of spatial patterns.

Point Pattern Analysis
-Point Pattern Analysis answers questions about why things appear where they do. The things could be trees, disease cases, crimes, lightning strikes - anything with a point location.

Areal Statistics
-So much data is collected in administrative divisions that there are specialized techniques for analyzing them. This chapter presents several methods for exploring data in areas.

Geostatistics
-Originally developed for the mining industry, geostatistics covers the analysis of location-based measurement data. It enables model-based interpolation of measurements with uncertainty estimation.