We’ve been playing with ways to construct networks according to graph theory, ultimately using the R package igraph to investigate wetland connectivity. I can’t reveal too much here because it’s my current research in progress! 🙂 The part of the process I’m currently trying to make more efficient is how to calculate distances for each wetland, to each wetland within a certain distance threshold. Ways I can go…
- Make the distance matrix a smaller object. Instead of storing floating point numbers, I can store integers representing our “distance bins” of interest. This might keep it from crashing, but the downside is it will probably still take the same (actually more) time. The time could be sped up with more memory free in the workspace, but it still seems a waste to calculate & consider all these distances that we don’t necessarily need.
- Create new spatial objects that represent minimum polygons around my networks, do distance analysis on these. My concern is that the “new shape” will be too imprecise, and it will skew our results (or I’ll have to use the computational power anyway to confirm a distance calculation/figure out what the nearest wetland in the shape is). I’m wondering if this will create new borders that are within 5k.
- Create an adjacency matrix for the polygons, and then exclude neighbors from consideration as they’re lumped into networks