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Target Practice and Spatial Point Process ModelsSubmitted by dylan on Mon, 2007-06-11 06:40.
A simple experiment was conducted, solely for the purpose of collecting semi-random coordinates on a plane, where a target was hit with 21 shots from a distance of 15 and 30 feet. The ppm() function (spatstat package) in R was used to create point density maps, along with a statistical description of the likelihood of where each target would be hit were the experiment to be conducted again (via point-process modeling). While normally used to model the occurrence of natural phenomena or biological entities, point-process models can be used to analyze one's relative accuracy at set target distances. One more way in which remote sensing or GIS techniques can be applied to smaller, non-georeferenced coordinate systems.
### load some libraries library(spatstat) library(RColorBrewer) ## read in the data t_30 <- read.csv('target_30.csv') t_15 <- read.csv('target_15.csv') ## an initial plot plot(t_30, xlim=c(0,35), ylim=c(0,50)) points(t_15, col='red') ## convert to spatstat objects t_30.ppp <- ppp(t_30$x, t_30$y, xrange=c(0,35), yrange=c(0,50) ) t_15.ppp <- ppp(t_15$x, t_15$y, xrange=c(0,35), yrange=c(0,50) ) ## check via plot plot(t_30.ppp) points(t_15.ppp, col='red') ## fit point-process model t_30_fit <- ppm(t_30.ppp, ~polynom(x,y,3), Poisson()) t_15_fit <- ppm(t_15.ppp, ~polynom(x,y,3), Poisson()) ## plot density comparisons between two ranges par(mfcol=c(1,2)) plot( density(t_30.ppp), col=brewer.pal('Blues', n=9), main="30 Feet") points(t_30.ppp, pch=4, cex=1) plot( density(t_15.ppp), col=brewer.pal('Oranges', n=9), main="15 Feet") points(t_15.ppp, pch=4, cex=1) ## ## plot a fit of the 30 foot pattern ## par(mfcol=c(2,2)) plot( density(t_30.ppp), col=brewer.pal('Blues', n=9), main="30 Feet") points(t_30.ppp, pch=4, cex=1) plot(t_30_fit, col=brewer.pal('Blues', n=9), trend=TRUE, cif=FALSE, pause=FALSE, how="image") plot(t_30_fit, trend=TRUE, cif=FALSE, pause=FALSE, how="contour") plot(t_30_fit, colmap=brewer.pal('Blues', n=9), trend=TRUE, cif=FALSE, pause=FALSE, how="persp", theta=0, phi=45) ## ## plot a fit of the 15 foot pattern ## par(mfcol=c(2,2)) plot( density(t_15.ppp), col=brewer.pal('Oranges', n=9), main="15 Feet") points(t_15.ppp, pch=4, cex=1) plot(t_15_fit, col=brewer.pal('Oranges', n=9), trend=TRUE, cif=FALSE, pause=FALSE, how="image") plot(t_15_fit, trend=TRUE, cif=FALSE, pause=FALSE, how="contour") plot(t_15_fit, colmap=brewer.pal('Oranges', n=9), trend=TRUE, cif=FALSE, pause=FALSE, how="persp", theta=0, phi=45)
## ## convert to png: for i in *.pdf ; do convert -density 300 +antialias $i `basename $i .pdf`.png ; done for i in *.png ; do mogrify -reisize 25% $i ; done
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