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This is a large radius, so this will be computationnally expensive. That being said, focal stat is the good tools if you have only 1 and 2 values: make the sum of the cells with focal stat, then "percentage of cells == 1" is equal to 100*(2*(number of cells in the radius)-(focal stat result))/(number of cells in the radius)


Let's make some data: > set.seed(123) > pts = st_as_sf(data.frame(x=runif(50),y=runif(50)),coords=1:2) > pts$S = factor(sample(c("Presence","Absence"),nrow(pts),TRUE)) > plot(pts,pch=19) To do join-counts, you need to decide where the joins are. For a grid that's usually the 4- or 8- nearest neighbours (rook or queen ...


I think you've pretty much got it, except you need to ensure the settings on your AreaOnAreaOverlayer for Attribute Handling are set to merge all incoming: I think once you've got that turned on, you'll have what you want. But now that I reread this, are the names of the attributes all the same i.e. it sounds like you're reading just one table in and ...


I would convert your raster into a polygon dataset and then use the Union tool with the boundaries and this dataset, this will allow you to answer the question of proportion of overlap.


Remove 3 and 4 from your surface, i.e. make them NODATA using raster calculator: Compute cost distance and backlink raster to your islands and use departure points and 2 above rasters to Cost Path as Polyline tool: You can delete lines exceeding length limit and find destination islands by using spatial join between remaining paths and island polygons.

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