Hello all. I’m probably reinventing the wheel here and wonder if somebody can point me in a better direction.
I have about 10 surfaces of signal strength (z) received at a transmitter. Here I’ll provide an example with four surfaces `r1`, `r2`, `r3`, `r4`. library(raster) library(gstat) set.seed(3178) n <- 100 r0 <- raster(nrows=n, ncols=n,xmn=1, xmx=n,ymn=1,ymx=n) r0 <- as(r0,"SpatialGrid") pts <- data.frame(x=c(10,20,60,80,1,1,n,n),y=c(10,80,70,40,1,n,1,n), id=c(1:4,rep(0,4)), z=c(c(rep(1,4)),rep(0,4))) pts <- SpatialPointsDataFrame(pts[,1:2],data=pts[,3:4]) r1 <- idw(z~1,pts[c(1,5:8),],newdata=r0) r2 <- idw(z~1,pts[c(2,5:8),],newdata=r0) r3 <- idw(z~1,pts[c(3,5:8),],newdata=r0) r4 <- idw(z~1,pts[c(4,5:8),],newdata=r0) r1 <- raster(r1) r2 <- raster(r2) r3 <- raster(r3) r4 <- raster(r4) I also have a series of points with signal strengths that correspond to each surface. The locations of these points is unknown. The task is to determine a location for each point. E.g., for `unknownXY`: # Extract a point. For the real data I would have the z data but not x and y cell2get <- floor(runif(1,1,n^2)) unknownXY <- c(extract(brick(r1,r2,r3,r4),cell2get)) In this case there is a four-way intersection of the contour lines that I could find suing some kind of intersection technique (e.g., `rgeos::gIntersection`). # Here is the the contour for the unknown point on each raster plot(r1,axes=F) contour(r1,levels=unknownXY[1],add=TRUE) plot(r2,axes=F) contour(r2,levels=unknownXY[2],add=TRUE) plot(r3,axes=F) contour(r3,levels=unknownXY[3],add=TRUE) plot(r4,axes=F) contour(r4,levels=unknownXY[4],add=TRUE) # here are all 4 contours plot(r0,axes=F,col="white") contour(r1,levels=unknownXY[1],add=TRUE,drawlabels=FALSE,col="red") contour(r2,levels=unknownXY[2],add=TRUE,drawlabels=FALSE,col="blue") contour(r3,levels=unknownXY[3],add=TRUE,drawlabels=FALSE,col="purple") contour(r4,levels=unknownXY[4],add=TRUE,drawlabels=FALSE,col="green") But there are times when, I think, that there will not be a completely pure intersection (e.g., the contours will not exactly overlap). In that case I’m wondering about using a probabilistic method for finding x,y for a given vector of z. One thing I was thinking was calculating distances. E.g., # calc distances r1d <- sqrt((unknownXY[1] - r1)^2) r2d <- sqrt((unknownXY[2] - r2)^2) r3d <- sqrt((unknownXY[3] - r3)^2) r4d <- sqrt((unknownXY[4] - r4)^2) plot(brick(r1d,r2d,r3d,r4d)) And them summing them: # Straight sum? Should they be weighted in some manner? rdSum <- sum(brick(r1d,r2d,r3d,r4d)) plot(rdSum) I could find the min value to get the ``best'' point: # Use min to get possible location. Seems easy to get trapped. rdSumMin <- rdSum == minValue(rdSum) plot(rdSumMin) Or use a threshold value to get a series of locations that are the most likely: # Or use quantile to get possible locations? thresh <- quantile(rdSum, probs = c(0.01)) rdSum99 <- rdSum < thresh plot(rdSum99) What I’m wondering it whether there is a way of finding a surface that is explicitly probabilistic. What I’d like to do is be able to determine possible location with x% likelihood. Any ideas, tips, tricks, appreciated. [[alternative HTML version deleted]] _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo