A reproducible approach to generating synthetic spatial data for teaching and learning purposes

Whilst there is an ever increasing amount of openly available spatial-data for teaching and learning purposes teachers and students often wish to generate their own synthetic spatial datasets. Such datasets can be used to test algorithms, test software code, assist in
visualisation development, and be used as unseen datasets for student assessment and learning. We describe the development of a Python-based software tool called RADIAN ((RAnDom spatIal dAta geNerator)) for generating simple synthetic spatial datasets.