Working with Spatial Data

Instructor: Ian Percel (Research Computing Services)

An introduction to basic tools of spatial data analysis and modelling that are available as open source libraries in Python.

This course will begin with a brief introduction to the key ideas of georeferenced data and how this can be expressed as raster or vector data. Using these ideas we will introduce loading geodatabases to DataFrames, filtering using spatial coordinates, and executing spatial joins. Finally, we discuss the relationship with spatial data structures provided by SciPy.spatial and how custom joins can be defined using spatial data structures.

Target audience: researchers who need to work with spatial data sets

Duration: 3 hours

Level: advanced

Prerequisites: This course assumes a considerable familiarity with both basic python syntax and some exposure to advanced data structures (specifically DataFrames). This information will be presented in the course Python Libraries for Researchers. Some exposure to concepts from Remote Sensing or Geographic Information Systems will be useful but is not required.

Laptop software: All attendees will need to bring their laptops with wireless access and with a remote SSH client installed (on Windows laptops we recommend the free edition of MobaXterm; on Mac and Linux laptops no need to install anything).