Open goldma99 opened 6 months ago
The description above has it the wrong way around.
Using known factors (from the ecology literature) that influence beaver movement--as well as road and terrain layers from OS, project movement of individual-equivalent "beavers" over time, given their 2012 starting point.
This is inspired by particle dispersion path modeling like HYSPLIT.
Select the hyperparameters which minimize the 2017 and 2020 errors (relative to the true beaver locations and concentrations in those subsequent years). This might be easiest to do at a small grid-cell level.
So basically saying, if a beaver is in grid cell $i$ in year $t$, with a vector of local characteristics $\Theta$ and a matrix $M$ of the characteristics of the surrounding grid cells $j$, and a vector of preferences $\psi$ (e.g., how much beavers don't like to cross roads, or how willing they are to go up-river, etc.), assign probabilities $p_{j, t+1}$ to each of the surrounding cells.
What do I need to know:
Problem
I only have three snapshots of beaver expansion: 2012, 2017, 2021. I want to know exactly where those beavers were at each year in between.
Potential solutions
Data