options to allow heavily-weighted points on a map to overwhelm other points with low weights

Question

what are some good kriging/interpolation idea/options that will allow heavily-weighted points to bleed over lightly-weighted points on a plotted R map?

the state of connecticut has eight counties. i found the centroid and want to plot poverty rates of each of these eight counties. three of the counties are very populated (about 1 million people) and the other five counties are sparsely populated (about 100,000 people). since the three densely-populated counties have more than 90% of the total state population, i would like those the three densely-populated counties to completely "overwhelm" the map and impact other points across the county borders.

the Krig function in the R fields package has a lot of parameters and also covariance functions that can be called, but i'm not sure where to start?

here is reproducible code to quickly produce a hard-bordered map and then three differently-weighted maps. hopefully i can just make changes to this code, but perhaps it requires something more complex like the geoRglm package? two of the three weighted maps look almost identical, despite one being 10x as weighted as the other..

https://raw.githubusercontent.com/davidbrae/swmap/master/20141001%20how%20to%20modify%20the%20Krig%20function%20so%20a%20huge%20weight%20overwhelms%20nearby%20points.R

thanks!!

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edit: here's a picture example of the behavior i want-

Answer 1

disclaimer - I am not an expert on Krigging. Krigging is complex and takes a good understanding of the underlying data, the method and the purpose to achieve the correct result. You may wish to try to get input from @whuber [on the GIS Stack Exchange or contact him through his website (http://www.quantdec.com/quals/quals.htm)] or another expert you know.

That said, if you just want to achieve the visual effect you requested and are not using this for some sort of statistical analysis, I think there are some relatively simple solutions.

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EDIT:

As you commented, though the suggestions below to use theta and smoothness arguments do even out the prediction surface, they apply equally to all measurements and thus do not extend the "sphere of influence" of more densely populated counties relative to less-densely populated. After further consideration, I think there are two ways to achieve this: by altering the covariance function to depend on population density or by using weights, as you have. Your weighting approach, as I wrote below, alters the error term of the krigging function. That is, it inversely scales the nugget variance.

As you can see in the semivariogram image, the nugget is essentially the y-intercept, or the error between measurements at the same location. Weights affect the nugget variance (sigma²) as sigma²/weight. Thus, greater weights mean less error at small-scale distances. This does not, however, change the shape of the semivariance function or have much effect on the range or sill.

I think that the best solution would be to have your covariance function depend on population. however, I'm not sure how to accomplish that and I don't see any arguments to Krig to do so. I tried playing with defining my own covariance function as in the Krig example, but only got errors.

Sorry I couldn't help more!

Another great resource to help understand Krigging is: http://www.epa.gov/airtrends/specialstudies/dsisurfaces.pdf

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As I said in my comment, the sill and nugget values as well as the range of the semivariogram are things you can alter to affect the smoothing. By specifying weights in the call to Krig, you are altering the variance of the measurement errors. That is, in a normal use, weights are expected to be proportional to the accuracy of the measurement value so that higher weights represent more accurate measurements, essentially. This isn't actually true with your data, but it may be giving you the effect you desire.

To alter the way your data is interpolated, you can adjust two (and many more) parameters in the simple Krig call you are using: theta and smoothness. theta adjusts the semivariance range, meaning that measured points farther away contribute more to the estimates as you increase theta. Your data range is

range <- data.frame(lon=range(ct.data$lon),lat=range(ct.data$lat))
range[2,]-range[1,]
       lon       lat
2 1.383717 0.6300484

so, your measurement points vary by ~1.4 degrees lon and ~0.6 degrees lat. Thus, you can play with specifying your theta value in that range to see how that affects your result. In general, a larger theta leads to more smoothing since you are drawing from more values for each prediction.

Krig.output.wt <- Krig( cbind(ct.data$lon,ct.data$lat) , ct.data$county.poverty.rate ,
                        weights=c( size , 1 , 1 , 1 , 1 , size , size , 1 ),Covariance="Matern", theta=.8)  
r <- interpolate(ras, Krig.output.wt)
r <- mask(r, ct.map)
plot(r, col=colRamp(100) ,axes=FALSE,legend=FALSE)
title(main="Theta = 0.8", outer = FALSE)
points(cbind(ct.data$lon,ct.data$lat))
text(ct.data$lon, ct.data$lat-0.05, ct.data$NAME, cex=0.5)

Gives:

Krig.output.wt <- Krig( cbind(ct.data$lon,ct.data$lat) , ct.data$county.poverty.rate ,
                        weights=c( size , 1 , 1 , 1 , 1 , size , size , 1 ),Covariance="Matern", theta=1.6)  
r <- interpolate(ras, Krig.output.wt)
r <- mask(r, ct.map)
plot(r, col=colRamp(100) ,axes=FALSE,legend=FALSE)
title(main="Theta = 1.6", outer = FALSE)
points(cbind(ct.data$lon,ct.data$lat))
text(ct.data$lon, ct.data$lat-0.05, ct.data$NAME, cex=0.5)

Gives:

Adding the smoothness argument, will change the order of the function used to smooth your predictions. The default is 0.5 leading to a second-order polynomial.

Krig.output.wt <- Krig( cbind(ct.data$lon,ct.data$lat) , ct.data$county.poverty.rate ,
                        weights=c( size , 1 , 1 , 1 , 1 , size , size , 1 ),
                        Covariance="Matern", smoothness = 0.6)  
r <- interpolate(ras, Krig.output.wt)
r <- mask(r, ct.map)
plot(r, col=colRamp(100) ,axes=FALSE,legend=FALSE)
title(main="Theta unspecified; Smoothness = 0.6", outer = FALSE)
points(cbind(ct.data$lon,ct.data$lat))
text(ct.data$lon, ct.data$lat-0.05, ct.data$NAME, cex=0.5)

Gives:

This should give you a start and some options, but you should look at the manual for fields. It is pretty well-written and explains the arguments well. Also, if this is in any way quantitative, I would highly recommend talking to someone with significant spatial statistics know how!

Answer 2

Kriging is not what you want. (It is a statistical method for accurate--not distorted!--interpolation of data. It requires preliminary analysis of the data--of which you do not have anywhere near enough for this purpose--and cannot accomplish the desired map distortion.)

The example and the references to "bleed over" suggest considering an anamorph or area cartogram. This is a map which will expand and shrink the areas of the county polygons so that they reflect their relative population while retaining their shapes. The link (to the SE GIS site) explains and illustrates this idea. Although its answers are less than satisfying, a search of that site will reveal some effective solutions.

Answer 3

lot's of interesting comments and leads above.

I took a look at the Harvard dialect survey to get a sense for what you are trying to do first. I must say really cool maps. And before I start in on what I came up with...I've looked at your work on survey analysis before and have learned quite a few tricks. Thanks.

So my first take pretty quickly was that if you wanted to do spatial smoothing by way of kernel density estimation then you need to be thinking in terms of point process models. I'm sure there are other ways, but that's where I went.

So what I do below is grab a very generic US map and convert it into something I can use as a sampling window. Then I create random samples of points within that region, just pretend those are your centroids. After I attach random values to those points and plot it up.

I just wanted to test this conceptually, which is why I didn't go through the extra steps to grab cbsa's and also sorry for not projecting, but I think these are the fundamentals. Oh and the smoothing in the dialect study is being done over the whole country. I think. That is the author is not stratifying his smoothing procedure within polygons....so I just added states at the end.

code:

library(sp)
    library(spatstat)
    library(RColorBrewer)
    library(maps)
    library(maptools)

    # grab us map from R maps package
    usMap <- map("usa")
    usIds <- usMap$names

    # convert to spatial polygons so this can be used as a windo below
    usMapPoly <- map2SpatialPolygons(usMap,IDs=usIds)

    # just select us with no islands
    usMapPoly <- usMapPoly[names(usMapPoly)=="main",]

    # create a random sample of points on which to smooth over within the map
    pts <- spsample(usMapPoly, n=250, type='random')

    # just for a quick check of the map and sampling locations
    plot(usMapPoly)
    points(pts)

    # create values associated with points, be sure to play aroud with
    # these after you get the map it's fun
    vals <-rnorm(250,100,25)
    valWeights <- vals/sum(vals)
    ptsCords <- data.frame(pts@coords)

    # create window for the point pattern object  (ppp) created below
    usWindow <- as.owin(usMapPoly)

    # create spatial point pattern object
    usPPP <- ppp(ptsCords$x,ptsCords$y,marks=vals,window=usWindow)

    # create colour ramp
    col <- colorRampPalette(brewer.pal(9,"Reds"))(20)

    # the plots, here is where the gausian kernal density estimation magic happens
    # if you want a continuous legend on one of the sides get rid of ribbon=FALSE
    # and be sure to play around with sigma
    plot(Smooth(usPPP,sigma=3,weights=valWeights),col=col,main=NA,ribbon=FALSE)
    map("state",add=TRUE,fill=FALSE)

example no weights:

example with my trivial weights

There is obviously a lot of work in between this and your goal of making this type of map reproducible at various levels of spatial aggregation and sample data, but good luck it seems like a cool project.

p.s. initially I did not use any weighting, but I suppose you could provide weights directly to the Smooth function. Two example maps above.