To generate (or 'hallucinate') neighborhoods based on the distribution of neighborhoods, and the roads and buildings within them.
- Because it is very interesting to see what a deep neural net can learn as a representation of some kind of high-dimensional data distribution
- Also: generating semi-real world geospatial information objects could help spatial planners to be inspired by auto-generated neighbourhood layouts.
- Get the neighborhoods data
- Create a vector representation of neighborhoods to use in a machine learning model
- Create a train/test split for the neighborhoods
- Persist the training and test data