Summary
In this chapter, we introduced you to the concept of spatial feature engineering. Recall that spatial feature engineering falls into two classes: summary spatial features and proximity spatial features. These two classes are respectively based on summarizing spatial data based on preexisting spatial relationships, and the distance, or proximity, between observations.
During the chapter, we performed two exercises based on data pertaining to Dollar General stores and its competitor, Family Dollar, and also based on Manhattan Airbnb locations and nearby NYC attractions. Throughout these exercises, we leveraged concepts we introduced you to in previous chapters, such as filtering based on masks, converting pandas DataFrames into GeoDataFrames, and working with projected coordinate reference systems.
Finally, we went over the concept of geospatial magic and the power that geography has as a universal link between data and objects. We hope that you are beginning to see the...
