Focal Statistics for Special Neighborhood

Question

I'm looking to calculate focal statistics for each cell of a raster, within a neighborhood that changes shape.

Background - I have three binary rasters, each representing a single vegetation type of interest. I'd like to calculate the percent coverage of each vegetation type within (e.g.) 20 km^2 of any cell in my study area (sum/total cells in neighborhood). The problem is that I can't use a simple circle or square neighborhood around each cell because, if I did, the search area used to calculate the sum would incorporate areas outside my study area. This exception is important because the statistics will be used as inputs for a habitat model, and the areas outside of my study area cannot be considered possible habitat - they're urbanized. Including them would give me erroneous statistics. So, what I'm looking to do is come up with a way to choose a neighborhood representing the n nearest cells (n determined by number of cells required to cover an area equal to my desired neighborhood size) that meet my criteria: that they do not fall within an urbanized area. I'm thinking that some code that mimics cellular automata neighborhood decisions should be used. I've never worked with CA though.

Example - Let's say I'm calculating this statistic for a cell on the boundary of my study site. If I assign all areas outside of my study area to zero (or ignore NoData), then I will get a statistic that represents roughly half of the areal coverage I'm interested in. So, percent coverage in a ~10 km^2 area, instead of 20 km^2 area. Since I'm studying home range sizes this is important. The neighborhood has to change shape, since that is how the animal views/uses the landscape. If they need 20 km^2, they'll change the shape or their home territory so that in encompasses 20 km^2. If I do not check ignore NoData, cell output will be NoData - and NoData is no help.

Any ideas?

curtvprice · Answer

A few years ago I worked with a friend of mine to work with different wedge azimuths for each cell by creating a collection of rasters for each angle (you could create a collection of rasters calculated for each buffer size, chosen by percent inside study area) and then used the Pick tool to select the stats result she wanted for each individual cell.

Just another option I thought I'd throw at you, if you weren't overwhelmed by your options already!

curtisbattle · Answer

i have no algorithm to calculate desirable neighborhood in the sense you use it above, though i plan to work on one in the future. right now it's more of a (very simple) available neighborhood, with developed areas designated as not available. i should replace the above "desirable" with "available". poor choice of words!

last night a friend and i came up with this workflow:

1.) create binary mask raster where 1 = available pixels,  0 = non-available pixels (developed areas)

2.) for any pixel in binary mask generate circular buffer that gives desired neighborhood size n (where n = number of pixels needed, given pixel dimensions, to reach desired area) -> calculate sum x within buffer (sum = number of "available" cells) -> if x < n increase buffer size by y until n = x.

3.) for each corresponding cell in binary veg raster, use above buffer to calculate sum (will give sum of target veg type cells within appropriate buffer)

4.) assign that count to corresponding cell in new raster.

basically a lot like what you suggested above. thanks for the code BTW!

i'll post my code when i think i have something that works.

curtvprice · Answer

> but obviously the shape should be able to change given the availability of desirable landscape attributes

Do you have some sort of algorithm in mind to calculate "desirability"? That's starting to sound more like a cost allocation problem than a neighborhood analysis problem. So definitely read up on the weighted distance tools...