Summary

In this chapter we learned how to work with geographical information and maps, how to manipulate geometry data (clip and merge polygons data, cluster data to generate maps with less details, remove subsets of geospatial data), superpose several layers of data over maps. We also learned how to modify and extract information from shapefile using geopandas and custom code as well as creating or calculating geospatial features, like terrain area or geospatial objects density. Additionally, we extracted reusable functions and grouped them in two utility scripts, which is Kaggle wording for independent Python modules. These utility scripts can be imported as any other library and integrated with your Notebook code. In the next Chapter we will put at work some of these tools and techniques for a data analytics competition.

References

1. Every Pub in England, Kaggle Datasets, https://www.kaggle.com/datasets/rtatman/every-pub-in-england
2. Starbucks Locations Worldwide, Kaggle Datasets, https...