CODEWITHSUNDEEP
rpolygon
user26480
user26480

Reputation: 387

Calculte the whole center of gravity/geometric center of a polygon list

I am looking for a method to calculate the center of gravity of each polygon in the list spatialpolygons:

I thought used a loop, but he gets me for the first polygon, I don't know the way, I am new to R, can someone please help me Code:

for ( i in 1:length(polys1_T)) { 
  xx=mean(coordinates(polys1_T[[i]])[,1])
  yy=mean(coordinates(polys1_T[[i]])[,2])
  aa<-as.data.frame(cbind(xx,yy))
}

Edit:

Code:

 inter1 <- read.table("c:/inter1.csv", header=TRUE)

# add a category (required for later rasterizing/polygonizing)
inter1 <- cbind(inter1, 
                cat
                = rep(1L, nrow(inter1)), stringsAsFactors = FALSE)

# convert to spatial points
coordinates(inter1) <- ~long + lat

# gridify your set of points
gridded(inter1) <- TRUE

# convert to raster
r <- raster(inter1)

# convert raster to polygons
sp <- rasterToPolygons(r, dissolve = T)
plot(sp)
# addition transformation to distinguish well the set of polygons
polys <- slot(sp@polygons[[1]], "Polygons")
# plot
plot(sp, border = "gray", lwd = 2) # polygonize result

inter1.csv result:

enter image description here

Polys is list of 9 polygons :is that it is possible to calculate the center of gravity for each polygon?

Upvotes: 2

Views: 2117

Answers (1)

hrbrmstr
hrbrmstr

Reputation: 78792

Give rgeos::gCentroid a look. You can apply it in many ways. If you have a SpatialPolygons object, say, from a call to readOGR, you can do:

map <- readOGR(dsn, layer)
centers <- data.frame(gCentroid(map, byid=TRUE))

to get all the centroids from it.

As an aside: while accurate—a more common term is "geometric center"/"centroid" vs "center of gravity"

EDIT

For plain, ol Polygons (the "hard" way, but slightly more accurate):

library(rgdal)
library(sp)
library(PBSmapping)
library(maptools)

do.call("rbind", lapply(polys, function(x) {
  calcCentroid(SpatialPolygons2PolySet(SpatialPolygons(list(Polygons(list(x), ID=1)))))
}))[,3:4]

##            X        Y
## 1  5.8108434 20.16466
## 2 -3.2619048 29.38095
## 3  5.5600000 34.72000
## 4  3.8000000 32.57037
## 5  6.3608108 32.49189
## 6 -2.2500000 31.60000
## 7 -8.1733333 27.61333
## 8  0.3082011 27.44444
## 9  8.6685714 26.78286

and, to use your nearly-equivalent by-hand-method:

do.call("rbind", lapply(polys, function(x) {
  data.frame(mean(coordinates(x)[,1]), mean(coordinates(x)[,2]))  
}))

##   mean.coordinates.x....1.. mean.coordinates.x....2..
## 1                  5.819892                  20.15484
## 2                 -3.242593                  29.37778
## 3                  5.539474                  34.71579
## 4                  3.815517                  32.56552
## 5                  6.323034                  32.47191
## 6                 -2.230952                  31.60000
## 7                 -8.140476                  27.61905
## 8                  0.350000                  27.40885
## 9                  8.746825                  26.92063

Each method gives you the centroid for each list element (and there are 9—not 5—in the example you provided).

If you ever have a huge list of these, consider using rbindlist from the data.table package (speedier + more memory efficient).

Upvotes: 5

Related Questions