Introduction

In the last chapter, you explored distributions and relationships in a dataset and learned how to identify patterns within a given dataset. This chapter will focus on the geographic aspect of data and how location affects those distributions and relationships.

Understanding geographic patterns is critical for many datasets, whether they are revenue patterns around the world for a global corporation or local purchase patterns for a small business. This type of data is especially useful for explaining patterns to internal or external customers with maps, in which you can show patterns at the region or country level all the way down to postal code or even smaller geographic levels, depending on how the data is collected. This can be highly useful for visualizing purchase, voting, or demographic patterns, as just a few examples.

One of the most powerful aspects of using geographic data and maps lies in the intuitive understanding of location data many users are...