rdist.earth | R Documentation |

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.

rdist.earth(x1, x2, miles = TRUE, R = NULL) RdistEarth(x1, x2=NULL, 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 or NULL 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. This is the calculation in `rdist.earth`

. Thanks to Qing Yang for pointing this out a long time
ago. A more efficient version has been implemented in C with the
R function `RdistEarth`

by Florian Gerber who has also experimented with parallel versions of fields functions.
The main advantage of `RdistEarth`

is the largely reduce memory usage.
The speed seems simillar to `rdist.earth`

. As Florian writes:

"The current fields::rdist.earth() is surprisingly fast. In the case where only the argument 'x1' is specified, the new C implementation is faster. In the case where 'x1' and 'x2' are given, fields::rdist.earth() is a bit faster. This might be because fields::rdist.earth() does not check its input arguments and uses a less complicated (probably numerically less stable) formula."

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

Doug Nychka, John Paige, Florian Gerber

rdist, stationary.cov, fields.rdist.near

data(ozone2) out<- rdist.earth ( ozone2$lon.lat) #out is a 153X153 distance matrix out2<- RdistEarth ( ozone2$lon.lat) all.equal(out, out2) 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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