This may be a long-winded and convoluted set of questions buffered by jargon, so please forgive me while you read about the problem I'm working on.
So, I'm trying to develop a predictive model for archaeological sites. I'm starting with several environmental factor datasets, including slope/aspect, geology, hydrology and the like that I've converted to rasters. Within each of the environmental factors I've given the attributes that are pertinent to my model group values for easy identification.
First, what I need to figure out is which tool to use in order to give me a total area probability (or frequency of occurrence across my entire study area) of a particular cell value. Then, I would take a probability of the environmental factor appearing at known archaeological sites.
Once I have those for each cell (guessing I would need a Python script?) I need to be able to plug it into my Bayesian probability formula to get a Bayesian score (so to speak). The resulting scores could then go into a choropleth map of sorts showing where archaeological sites are more likely to be in my study area.
I may not be wording it well enough, but if anyone has any thoughts or has done something similar I would be most grateful.