The explosion of geolocation sensors and spatial data has unlocked a wealth of potential for visualization and analytics. This talk provides an introduction to how to work with spatial data using the excellent python tooling.
Whether looking at trends across location or augmenting your data sets with the copious available data, the ability to handle spatial data is a key skill for a data scientist. Unfortunately, accessing this data has a steep learning curve. This talk aims to give a background and basic tooling to get started working with spatial data in python. Examples will be drawn from agriculture in East Africa with an emphasis on leveraging publicly available data.
Though it is often possible to have success without delving deep into the theory behind coordinate reference systems, having a familiarity with the concepts makes understanding the tools much easier. We'll define the terms and data structures seen in the often opaque user docs as well as a very brief diversion into mapping and projection theory.
Discuss briefly the tools available in python focusing on