大数据集的空间匹配

Question

我有一个包含大约100000个点的数据集和另一个包含大约3000个多边形的数据集.对于每个点我需要找到最近的多边形(空间匹配).多边形内的点应与该多边形匹配.

计算所有对距离是可行的,但需要的时间比必要的长.是否有一个R包将利用空间索引来解决这种匹配问题？

我知道sp包和over函数,但文档没有告诉任何关于索引.

Answer 1

您可以尝试使用包中的gDistance功能rgeos.作为一个例子,请看下面的例子,我从这个旧线程重新编写.希望能帮助到你.

require( rgeos )
require( sp )

# Make some polygons
grd <- GridTopology(c(1,1), c(1,1), c(10,10))
polys <- as.SpatialPolygons.GridTopology(grd)

# Make some points and label with letter ID
set.seed( 1091 )
pts = matrix( runif( 20 , 1 , 10 ) , ncol = 2 )
sp_pts <- SpatialPoints( pts )
row.names(pts) <- letters[1:10]

# Plot
plot( polys )
text( pts , labels = row.names( pts ) , col = 2 , cex = 2 )
text( coordinates(polys) , labels = row.names( polys ) , col = "#313131" , cex = 0.75 )

# Find which polygon each point is nearest
cbind( row.names( pts ) , apply( gDistance( sp_pts , polys , byid = TRUE ) , 2 , which.min ) )
#   [,1] [,2]
#1  "a"  "86"
#2  "b"  "54"
#3  "c"  "12"
#4  "d"  "13"
#5  "e"  "78"
#6  "f"  "25"
#7  "g"  "36"
#8  "h"  "62"
#9  "i"  "40"
#10 "j"  "55"