Description Usage Arguments Details Value Author(s) See Also Examples

Given two sets of longitude/latitude locations, `rdist.earth`

computes
the Great circle (geographic) distance matrix among all pairings and
`rdist.earth.vec`

computes a vector of pairwise great circle distances
between corresponding elements of the input locations using the Haversine
method and is used in empirical variogram calculations.

1 2 | ```
rdist.earth(x1, x2, miles = TRUE, R = NULL)
rdist.earth.vec(x1, x2, miles = TRUE, R = NULL)
``` |

`x1` |
Matrix of first set of lon/lat coordinates first column is the longitudes and second is the latitudes. |

`x2` |
Matrix of second set of lon/lat coordinates first column is the longitudes and second is the latitudes. If missing x1 is used. |

`miles` |
If true distances are in statute miles if false distances in kilometers. |

`R` |
Radius to use for sphere to find spherical distances. If NULL the radius is either in miles or kilometers depending on the values of the miles argument. If R=1 then distances are of course in radians. |

Surprisingly the distance matrix is computed efficiently in R by dot products of the direction cosines. Thanks to Qing Yang for pointing this out a long time ago.

The great circle distance matrix if nrow(x1)=m and nrow( x2)=n then the returned matrix will be mXn.

Doug Nychka, John Paige

rdist, stationary.cov, fields.rdist.near

1 2 3 4 5 6 7 8 9 10 11 12 | ```
data(ozone2)
out<- rdist.earth ( ozone2$lon.lat)
#out is a 153X153 distance matrix
upper<- col(out)> row( out)
# histogram of all pairwise distances.
hist( out[upper])
#get pairwise distances between first 10 and second 10 lon/lat points
x1 = ozone2$lon.lat[1:10,]
x2 = ozone2$lon.lat[11:20,]
dists = rdist.earth.vec(x1, x2)
print(dists)
``` |

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