I have an idea for working with geospatial rasters to predict housing prices. I have done this before using mulitivariate linear regression. I would like to try using a convolutional neural network. I have a series of rasters that represent a surface of interpolated housing prices over time. The convolutional neural network will learn from the price rasters, but I need to add in other explanatory variables. The most important is time. I might add additional scalar values such as interest rate or stock market. I might also want to add additional rasters as regressors, such as crime maps, or distance to transit. I know that in a typical neural network regression there is one neuron per explanatory variable, and in a typical CNN there is a neuron per pixel on the input layer. So I would like some regressors to be rasters, and some to be scalers.
My question is how do I add additional explanatory variables, both rasters and scalars, to a convolutional neural network? I am using Keras and TensorFlow.