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I would like to get the areas related to the polygons resulting from the intersection between two SpatialPolygonsDataFrames: 'A' and 'B'. SpatialPolygonsDataFrame 'B' has 3 different 'classes', which represent polygons with different shape and size. In reality, 'A' is PRIO-GRID data (regular polygons) and 'B' is GREG ethnic database (with 3 layers that indicate different ethnic groups). In ArcGIS, the command is 'Tabulate Intersection" with option 'classes' where we can put the different class layers'. Is there any alternative in R?

PolyGeo
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user26486
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    Could you explain more what you want to do. Maybe providing a reproducible example of your data? And the desired output? For example: is this questions related to what you are trying to do: http://gis.stackexchange.com/questions/64537/clip-polygon-and-retain-data/116350#116350? Because it might happen folks working with R do not know how the 'Tabulate Intersection' tool from ArcGIS works. – Andre Silva Apr 06 '16 at 12:49

1 Answers1

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Here is how you can do that, I think. I am following this example:

library(raster)
library(rgeos)
library(rgdal)

# example data 
p <- shapefile(system.file("external/lux.shp", package="raster"))[, 1]
p$Color <- rep(c('blue', 'green', 'red'), 4)
p <- p[,2]

z <- raster(p, nrow=2, ncol=2, vals=1:4)
names(z) <- 'Zone'
z <- as(z, 'SpatialPolygonsDataFrame')

# inspect
p
z
plot(p)
plot(z, add=TRUE, border='blue')


# intersect
i <- intersect(p, z)
# compute area
i$area <- abs(area(i))/1000000

# get the attribute table
d <- data.frame(i)

# aggregate and sum the areas
a <- aggregate(d[, 'area', drop=FALSE], d[, c('Color', 'Zone')], sum)

# get the total area by zone
aa <- aggregate(d[, 'area', drop=FALSE], d[, 'Zone', drop=FALSE], sum)
colnames(aa)[2] <- 'zonearea'

# merge that to the data
m <- merge(a, aa)

# compute percentage
m$percentage <- 100 * m$area / m$zonearea

# drop intermediate variable
m$zonearea <- NULL

m
##   Zone Color      area percentage
##1     1  blue 329.80536   39.52691
##2     1 green 391.08141   46.87080
##3     1   red 113.49501   13.60229
##4     2  blue  58.67825   33.56140
##5     2 green  79.69006   45.57924
##6     2   red  36.47019   20.85936
##7     3  blue 224.19645   28.88015
##8     3 green  79.20874   10.20338
##9     3   red 472.89422   60.91647
##10    4  blue 156.11708   20.03322
##11    4 green 379.33556   48.67702
##12    4   red 243.83826   31.28976
Robert Hijmans
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