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Spatial interpolation from categorical data in R
The 2019 Stack Overflow Developer Survey Results Are In
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)How to grid unevenly sampled categorical data?Method to compute and visualize the closest set of facilities from OSM dataUnderstanding the cell size argument in the kriging tool, ArcGISInterpolation of categorical data from a polygon surface with gapsHow to Convert from Point to Continuous Raster in ArcGIS 10.1?How to perform loop on raster files?Accounting for spatial autocorrelation in rastersMost suitable interpolation method - regriddingCreating polygon grid from SpatialPoints in R?Choosing interpolation method for grid data?
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;
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
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 = 1for points where that pronunciation is used,yi = 0if 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.
raster r interpolation kernel-density
asked May 31 '17 at 6:06
wnstnsmthwnstnsmth
287320
add a comment |
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
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 = 1for points where that pronunciation is used,yi = 0if 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.
raster r interpolation kernel-density
asked May 31 '17 at 6:06
wnstnsmthwnstnsmth
287320
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
add a comment |
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
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 = 1for points where that pronunciation is used,yi = 0if 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.
raster r interpolation kernel-density
asked May 31 '17 at 6:06
wnstnsmthwnstnsmth
287320
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
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 = 1for points where that pronunciation is used,yi = 0if 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.
raster r interpolation kernel-density
raster r interpolation kernel-density
asked May 31 '17 at 6:06
wnstnsmthwnstnsmth
287320
asked May 31 '17 at 6:06
wnstnsmthwnstnsmth
287320
asked May 31 '17 at 6:06
wnstnsmthwnstnsmth
287320
asked May 31 '17 at 6:06
wnstnsmthwnstnsmth
287320
asked May 31 '17 at 6:06
wnstnsmthwnstnsmth
287320
287320
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
add a comment |
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
2
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
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
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
add a comment |
1 Answer
1
active
oldest
votes
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.
answered Apr 7 at 16:01
wnstnsmthwnstnsmth
287320
add a comment |
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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.
answered Apr 7 at 16:01
wnstnsmthwnstnsmth
287320
add a comment |
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.
answered Apr 7 at 16:01
wnstnsmthwnstnsmth
287320
add a comment |
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.
answered Apr 7 at 16:01
wnstnsmthwnstnsmth
287320
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.
answered Apr 7 at 16:01
wnstnsmthwnstnsmth
287320
answered Apr 7 at 16:01
wnstnsmthwnstnsmth
287320
answered Apr 7 at 16:01
wnstnsmthwnstnsmth
287320
answered Apr 7 at 16:01
wnstnsmthwnstnsmth
287320
287320
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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