如何通过数据值对 Voronoi 图案进行着色？

Question

我想：

1. 从SpatialPointDataFrame中在R中创建泰森多边形（Voronoi tesselation） OK
2. 获取SpatialPolygonDataFrame OK
3. 通过原始SpatialPointDataFrame中的值对其进行着色 如何实现？

目前为止，我已经按照以下链接创建和更新了Voronoi tesselation，并进行了更新：https://gis.stackexchange.com/questions/190917/r-voronoi-tesselation-from-long-lat-data.

我知道可以借助库“dismo”来对它进行着色：https://gis.stackexchange.com/questions/136542/r-function-for-thiessen-polygons

然而，使用上述Voronoi函数时，在我的voronoipolygons中只有一个变量：“dummy”。然而，我希望根据变量“z”对我的多边形进行着色，这个变量不再包含在我的.voro多边形中。

voronoipolygons = function(layer) {
  require(deldir)
  crds = layer@coords
  z = deldir(crds[,1], crds[,2])
  w = tile.list(z)
  polys = vector(mode='list', length=length(w))
  require(sp)
  for (i in seq(along=polys)) {
    pcrds = cbind(w[[i]]$x, w[[i]]$y)
    pcrds = rbind(pcrds, pcrds[1,])
    polys[[i]] = Polygons(list(Polygon(pcrds)), ID=as.character(i))
  }
  SP = SpatialPolygons(polys)
  voronoi = SpatialPolygonsDataFrame(SP, data=data.frame(dummy = seq(length(SP)), 
                                                         row.names=sapply(slot(SP, 'polygons'), 
                                                                                                   function(x) slot(x, 'ID'))))
}

我的问题是：如何通过"z"变量对我的.voro多边形进行着色，或/和如何直接将其包含在上述的voronoipolygons()函数中？我不能只是将"z"变量添加到.voro@data中，因为值的顺序被改变了。我的R技巧还不够强大..非常感谢！

虚拟数据：

x <- c(32.5, 32.1, 33.5, 32.2, 33.0)
y <- c(-2.2, -3.3, -2.3, -2.9, -3.0)
z <- c(1, 2, 5, 8, 4)

# make df
df<-as.data.frame(cbind(x,y,z))
coordinates(df)<- ~ x + y  #make SPDF 

df.voro <- voronoipolygons(df)   # calculated VORONOI

require('dismo')
spplot(df.voro, "dummy")   # colorize Polygons

# add z variable to newly created data
df.voro@data$z<-df$z    ## !!! can't use this, because this change order of values in df !!!
spplot(df.voro, "z")

- maycca

Answer 1

我明白了！如何修改Voronoi函数

首先，我需要从我的数据框中读取my.variable：my.variable = layer@data[,1]，然后将其添加到我的SP对象中： y.data = my.variable。

voronoipolygons2 = function(layer) {
  require(deldir)
  crds = layer@coords
  z = deldir(crds[,1], crds[,2])
  w = tile.list(z)
  my.variable = layer@data[,1]   ## HERE
  polys = vector(mode='list', length=length(w))
  require(sp)
  for (i in seq(along=polys)) {
    pcrds = cbind(w[[i]]$x, w[[i]]$y)
    pcrds = rbind(pcrds, pcrds[1,])
    polys[[i]] = Polygons(list(Polygon(pcrds)), ID=as.character(i))
  }
  SP = SpatialPolygons(polys)
  voronoi = SpatialPolygonsDataFrame(SP, data=data.frame(dummy = seq(length(SP)), 
                                                         my.data = my.variable, # HERE add new column to my voronoi data 
                                                         row.names=sapply(slot(SP, 'polygons'), 
                                                                          function(x) slot(x, 'ID'))))
}

用修改后的 Voronoi 函数创建 Voronoi Tesselation 多边形：

df.voro2 <- voronoipolygons2(df)  

检查一下我的Voronoi2数据是什么样子。

> df.voro2@data
  dummy my.data
1     1       1
2     2       2
3     3       5
4     4       8
5     5       4

以及它们与voronoi1数据的不同之处

> df.voro@data
  dummy
1     1
2     2
3     3
4     4
5     5

在一个表格上显示两个spplots

require(gridExtra)
grid.arrange(spplot(df.voro, "dummy", xlab = "x", ylab = "y", main = "original" ),
             spplot(df.voro2, "my.data", xlab = "x", ylab = "y", main = "z value applied !;-)"))

TRADAAA ;)

- maycca