# mat_geo_dist: Compute Euclidean geographic distances between points In graph4lg: Build Graphs for Landscape Genetics Analysis

## Description

The function computes Euclidean geographic distance between points given their spatial coordinates either in a metric projected Coordinate Reference System or in a polar coordinates system.

## Usage

 ```1 2 3 4 5 6 7 8``` ```mat_geo_dist( data, ID = NULL, x = NULL, y = NULL, crds_type = "proj", gc_formula = "vicenty" ) ```

## Arguments

 `data` An object of class : `data.frame` with 3 columns: 2 columns with the point spatial coordinates and another column with point IDs `SpatialPointsDataFrame` `ID` (if `data` is of class `data.frame`) A character string indicating the name of the column of `data` with the point IDs `x` (if `data` is of class `data.frame`) A character string indicating the name of the column of `data` with the point longitude `y` (if `data` is of class `data.frame`) A character string indicating the name of the column of `data` with the point latitude `crds_type` A character string indicating the type of coordinate reference system: 'proj' (default): a projected coordinate reference system 'polar': a polar coordinate reference system, such as WGS84 `gc_formula` A character string indicating the formula used to compute the Great Circle distance: 'vicenty'(default): Vincenty inverse formula for ellipsoids 'slc': Spherical Law of Cosines 'hvs': Harversine formula

## Details

When a projected coordinate reference system is used, it calculates classical Euclidean geographic distance between two points using Pythagora's theorem. When a polar coordinate reference system is used, it calculates the Great circle distance between points using different methods. Unless `method = "polar"`, when `data` is a `data.frame`, it assumes projected coordinates by default.

## Value

A pairwise matrix of geographic distances between points in meters

P. Savary

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```# Projected CRS data(pts_pop_simul) mat_dist <- mat_geo_dist(data=pts_pop_simul, ID = "ID", x = "x", y = "y") #Polar CRS city_us <- data.frame(name = c("New York City", "Chicago", "Los Angeles", "Atlanta"), lat = c(40.75170, 41.87440, 34.05420, 33.75280), lon = c(-73.99420, -87.63940, -118.24100, -84.39360)) mat_geo_us <- mat_geo_dist(data = city_us, ID = "name", x = "lon", y = "lat", crds_type = "polar") ```

graph4lg documentation built on March 16, 2021, 5:09 p.m.