Use the Cost Distance and Cost Path Tools from the Spatial Analyst Extension.
You can create a Raster from your Hydrography Polygons. Then assure that the raster cells that represent water get a low value (e.g. 1) and the other raster cells get a high value (e.g. 1000). You can then use this raster as cost distance raster in the Cost Path Tool.
One of the fastest and easiest possible solutions uses a short program written with the free open source program R (the R project for statistical computing). The following code computes the distance matrix (using spherical distances) between two arrays of (lon, lat) coordinates named customers and facilities and stores it in an array distances (with rows for customers and columns for facilities).
dist <- function(x, y=c(0,0), radius=6366710) {
# `x` and `y` are both (lon, lat) in radians.
# `x` is an `n` by 2 matrix.
# Returns distances on a sphere of radius `radius`; the default value
# is a spherical model in meters.
return (2 * radius * asin( sqrt(sin((x[,2]-y[2])/2)^2 +
cos(x[,2])*cos(y[2])*(sin(x[,1]-y[1])/2)^2)))
}
distances <- apply(facilities * pi/180, 1, function(y) dist(customers * pi/180, y)
It runs reasonably quickly: your problem with 150 customers and 30 facilities generates a matrix of all 150*30 = 4500 distances in 2.5 milliseconds.
To extract the coordinates from a Google Earth file, save the locations directly in KML format or unzip the KMZ file (which will produce a KML version). This is an ASCII file containing (lon, lat) coordinates.. The maptools package reads such files directly. Here is a working example that reads KML files of customers and facilities and computes all distances between them.
read.kml <- function(s) {
require(maptools)
x <- getKMLcoordinates(s, ignoreAltitude=TRUE)
return (matrix(unlist(x), ncol=2))
}
customers <- read.kml("F:/temp/customers.kml")
facilities <- read.kml("F:/temp/facilities.kml")
distances <- apply(facilities * pi/180, 1, function(y) dist(customers * pi/180, y))
At this point you can perform just about any kind of calculation you might like with the distances and--of course--you can write them to a file for post-processing on another platform.
Best Answer
Alternatively you can use the Generate Near Table tool to do the same thing as GISGe's suggestion, but with some additional attributes (such as xy of the near feature and the angle between source and near features) if those would be useful in your study. It also allows you to specify a number of nearest matches so it isn't all or just nearest.