In this chapter, we looked at how to perform spatial operations in a real-world dataset, the Foursquare check-ins. We first created a pandas DataFrame from the text file and processed this text file into a GeoDataFrame. During this transformation process, we touched on geometries and CRS, as well as geographic coordinate projections. After this, we carried out spatial operations on the GeoDataFrame, such as using buffers to calculate distances around subway points and using spatial joins to derive points with NYC district boundaries. Finally, we covered some interactive geographic data visualization techniques using the Folium library in Python.
In the next chapter, we will learn how to aggregate data using machine learning clustering techniques with spatial data. We will use a dataset of reviews about Airbnb properties in Amsterdam to see how to aggregate and cluster...
