如何用多边形数据进行克里金法？ （不想要块克里金）

Question

我正在尝试应用kriging来插入空气污染浓度（目标变量） 当我运行如下的krige函数时，R返回错误。

RSPAVE是目标变量; air是包含RSPAVE的数据集; TPU是shapefile

k.o <- krige(RSPAVE ~1, locations=air, newdata=TPU, model=m.RSPAVE.f)

Error in predict.gstat(g, newdata = newdata, block = block, nsim = nsim, :
  gstat: value not allowed for: block kriging for long-lat data undefined

可能是因为我的网格数据是shapefile。但我不想要块克里金法，如何将多边形转为点，并应用普通克里金？

非常感谢！

Answer 1

假设RSPAVE是一个SpatialPolygonsDataFrame`并且您希望基于地理质心进行克里奇，这应该有效：

Rpoint <- SpatialPointsDataFrame(coordinates(RSPAVE), data = RSPAVE@data, proj4string = CRS(proj4string(RSPAVE)))

将其转换为点图层。

然后和以前一样：

k.o <- krige(Rpoint ~1, locations=air, newdata=TPU, model=m.RSPAVE.f)

Answer 2

You say your TPU grid is a shapefile, but what data class is it? If TPU is not a SpatialGridDataFrame, krige may not know how to predict on it and default to block.

To grid TPU, I recommend using something like spsample followed by gridded() to overlay a grid on the polygon with dimensions of SomeDimension.

grid.TPU = spsample(TPU, type = "regular", cellsize = c(SomeDimension, SomeDimension))

gridded(grid.TPU) = TRUE

Then

k.o <- krige(RSPAVE ~1, locations=air, newdata=grid.TPU, model=m.RSPAVE.f)