intro_part_ii.qmd

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title: "Part II — Spatial Data Analysis"
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## Overview

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](notebooks/05_choropleth.qmd), we discuss the workhorse of statistical visualization for geographic data: choropleths. In [Chapter 6](notebooks/06_spatial_autocorrelation.qmd), 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](notebooks/07_local_autocorrelation.qmd) will present *local* methods that can detect observations that are unlike (or too like) their neighbors. To wrap up, [Chapter 8](notebooks/08_point_pattern_analysis.qmd) 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?