4

I have a spatial point data set, where each point represents a person pronouncing a word in a specific fashion. Let's say I have six different pronunciations.

In an earlier iteration, I aggregated the point data to a hexagonal grid, the color represents the dominant pronunciation in the hexagon while the opacity represents its dominance, see: https://www.srf.ch/static/srf-data/data/2016/dialektkarten-karte/

Now I'd like to spatially interpolate the data to get a continuous surface, similar as Josh Katz did: http://www4.ncsu.edu/~jakatz2/files/dialectposter.png

enter image description here

He uses something he calls "k-nearest neighbor kernel smoothing" but he's not publishing his R code so I don't fully understand how that is supposed to work with categorical data. I perfectly understand on how to do it with continuous variables, but not with the latter.

The pseudo-algorithm I came up with so far:

  • Repeat the "final estimate" formula for each pronunciation, using yi = 1 for points where that pronunciation is used, yi = 0 if not. By doing so, you get a grid for each pronunciation. The cells represent the probabilities of the respective pronunciation appearing at that grid cell.
  • Now, find the most probable pronunciation in each grid cell. This determines the cell color (hue). Its probability determines the color value (i.e.: the higher, the darker, and vice versa).
  • Find a way to map that resulting grid / raster - might be tricky because it contains two variables that need to be mapped to visual variables (dominant pronunciation and its probability).

But maybe this is completely off - and also, I wonder whether there are R packages for spatial interpolation of categorical data. So far I have only found knn methods and the like and I suppose I have to build something myself.

  • 2
    Have you tried kknn? cran.rstudio.com/web/packages/kknn/kknn.pdf This package looks like it implements exactly what Katz has done. – Liam G May 31 '17 at 10:14
  • Seems like what I'm looking for, yes. Thanks a lot! Right now I'm trying to figure out what the different parameters exactly mean - do you know of any tutorial or paper apart from that manual? I will try to post an answer to my own question once I come up with a good solution for my case. – wnstnsmth Jun 1 '17 at 19:02
0

In the end, I figured out a method with the kknn package. If somebody is still interested in this answer, I recommend reading my blog post on the topic.

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.