I am trying to figure out how isolated certain points are within my data set. I am using two methods to determine isolation, the distance of the closest neighbor and the number of neighboring sites within a given radius. All my coordinates are in latitude and longitude
This is what my data looks like:
pond lat long area canopy avg.depth neighbor n.lat n.long n.distance n.area n.canopy n.depth n.avg.depth radius1500
A10 41.95928 -72.14605 1500 66 60.61538462
AA006 41.96431 -72.121 250 0 57.77777778
Blacksmith 41.95508 -72.123803 361 77 71.3125
Borrow.Pit.1 41.95601 -72.15419 0 0 41.44444444
Borrow.Pit.2 41.95571 -72.15413 0 0 37.7
Borrow.Pit.3 41.95546 -72.15375 0 0 29.22222222
Boulder 41.918223 -72.14978 1392 98 43.53333333
I want to put the name of the nearest neighboring pond in the column neighbor, its lat and long in n.lat and n.long, the distance between the two ponds in n.distance, and the area, canopy and avg.depth in each of the appropriate columns.
Second, I want to put the number of ponds within 1500m of the target pond into radius1500.
Does anyone know of a function or package that will help me calculate the distances/numbers that I want? If it's an issue, it won't be hard to enter the other data I need, but the nearest neighbor's name and distance, plus the number of ponds within 1500m is what I really need help with.
Thank you.
Best option is to use libraries sp
and rgeos
, which enable you to construct spatial classes and perform geoprocessing.
library(sp)
library(rgeos)
Read the data and transform them to spatial objects:
mydata <- read.delim('d:/temp/testfile.txt', header=T)
sp.mydata <- mydata
coordinates(sp.mydata) <- ~long+lat
class(sp.mydata)
[1] "SpatialPointsDataFrame"
attr(,"package")
[1] "sp"
Now calculate pairwise distances between points
d <- gDistance(sp.mydata, byid=T)
Find second shortest distance (closest distance is of point to itself, therefore use second shortest)
min.d <- apply(d, 1, function(x) order(x, decreasing=F)[2])
Construct new data frame with desired variables
newdata <- cbind(mydata, mydata[min.d,], apply(d, 1, function(x) sort(x, decreasing=F)[2]))
colnames(newdata) <- c(colnames(mydata), 'neighbor', 'n.lat', 'n.long', 'n.area', 'n.canopy', 'n.avg.depth', 'distance')
newdata
pond lat long area canopy avg.depth neighbor n.lat n.long n.area n.canopy n.avg.depth
6 A10 41.95928 -72.14605 1500 66 60.61538 Borrow.Pit.3 41.95546 -72.15375 0 0 29.22222
3 AA006 41.96431 -72.12100 250 0 57.77778 Blacksmith 41.95508 -72.12380 361 77 71.31250
2 Blacksmith 41.95508 -72.12380 361 77 71.31250 AA006 41.96431 -72.12100 250 0 57.77778
5 Borrow.Pit.1 41.95601 -72.15419 0 0 41.44444 Borrow.Pit.2 41.95571 -72.15413 0 0 37.70000
4 Borrow.Pit.2 41.95571 -72.15413 0 0 37.70000 Borrow.Pit.1 41.95601 -72.15419 0 0 41.44444
5.1 Borrow.Pit.3 41.95546 -72.15375 0 0 29.22222 Borrow.Pit.2 41.95571 -72.15413 0 0 37.70000
6.1 Boulder 41.91822 -72.14978 1392 98 43.53333 Borrow.Pit.3 41.95546 -72.15375 0 0 29.22222
distance
6 0.0085954872
3 0.0096462277
2 0.0096462277
5 0.0003059412
4 0.0003059412
5.1 0.0004548626
6.1 0.0374480316
Edit: if coordinates are in degrees and you would like to calculate distance in kilometers, use package geosphere
library(geosphere)
d <- distm(sp.mydata)
# rest is the same
This should provide better results, if the points are scattered across the globe and coordinates are in degrees