Summary

We have introduced location data and location data intelligence in this chapter by looking from different perspectives: business, technical, and data. We have also covered applications of location data intelligence and provided some simple and concrete examples from the Foursquare dataset. Here, both customer perspectives, as well as user business perspectives, were considered in our location data intelligence applications and examples. Furthermore, we have compared and contrasted data science and location data science. Finally, we have introduced a primer on using Jupyter Notebooks and Google Colab.

We will learn to process location data and apply machine learning models in the next chapter while consuming location data like a data scientist. We will use the New York taxi trajectory data to predict trip durations for New York taxicab trips.