Summary

In this chapter, we exposed you to a handful of more advanced topics that weren’t covered in prior chapters of the book, including spatial indexing and spatial interpolation. Within the section on spatial indexing, we discussed how spatial indexes can be used to improve the efficiencies of spatial queries and spatial operations. In this section, you were introduced to the R-tree index as well as Uber’s H3 spatial index, which was used to filter and summarize Airbnb locations in Manhattan. In the section on spatial interpolation, you learned how to infer missing values using sampled points through the application of IDW interpolation and Kriging. In our discussion on Kriging, you were also exposed to variography and semivariograms.

In the last section of this book, we briefly discussed the topic of ethics in spatial data science. We walked you through numerous examples where spatial data has been used in potentially unethical ways, including an altered hurricane...