Now that we understand geographic processes and the data that measures them, we will introduce exploratory spatial data analysis (ESDA). ESDA augments Tukey’s exploratory data analysis, and involves a large collection of techniques used to “orient yourself” (find structure) inside of your dataset. For geographical problems, this often involves understanding whether our data displays a geographical pattern. We cover such topics in this part.
First, in Chapter 5, we discuss the workhorse of statistical visualization for geographic data: choropleths. In Chapter 6, we introduce spatial autocorrelation, the concept that formally connects geographical and statistical similarity. This allows us to characterize the “strength” of a geographical pattern and is at the intellectual core of many explicitly spatial techniques. All patterns have exceptions, however, and Chapter 7 will present local methods that can detect observations that are unlike (or too like) their neighbors. To wrap up, Chapter 8 discusses methods for visualising, characterising and analysing points, the raw locational data. Taken altogether, this part provides methods to explore most of the fundamental questions involved in geographical analysis: whatever the nature of my data, is there a geographical pattern, and are there places where this pattern does not hold?