如何计算R中沿线的两个点之间的地理距离? [英] How to calculate geographic distance between two points along a line in R?

问题描述

输入

我有两个导入到R中的shapefile，所以最终得到了.包含公交路线的空间线数据框.包含公交车站的spacepointsdataframe.

使用停靠点绘制给定的路线，如下所示.

样本数据

此

 #沿路线的距离d<-sort(gProject(l，p))d#[1] 0 3051 3057 7221 10379 15657 20326 20326 22141 24262#站点之间的距离差异(d)#[1] 3050.9166 5.9720 4164.2480 3157.7702 5278.5812 4668.1810 0.5878#[8] 1814.9612 2120.8470 

Inputs

I have two shapefiles that I Import into R, so that I end up with. A spatiallinesdataframe containing bus routes. A spatialpointsdataframe containing bus stops.

Plotting a given route with its stops looks like this.

Sample Data

This link includes two shapefiles to download as a zip with a sample two routes.

Target

My aim is to calculate the geographic distance in meters between every pair of stops: Stop 1 to Stop 2, Stop 2 to Stop 3, etc. across the length of the underlying bus route.

Most methods I found calculate the euclidean distance, or as 'the crow flies'; which will not work here.

This post mentions the PBSmapping which has a calcLength function that does a great job calculating the total distance of the route, but I can't find a way to match it to the stop pairs situation, nor can I find a way to actually subset the shapefile by its attributes.

The riverdist package is equally interesting, but highly optimized for rivers that I can't find a way to apply it.

解决方案

Try gProject from the rgeos package:

library("rgdal")
library("rgeos")

# read shapefile and transfrom to UTM zone 36N (distances in m)
route <- spTransform(readOGR(".", "Trips"), CRS("+init=epsg:32636"))
stops <- spTransform(readOGR(".", "Stops"), CRS("+init=epsg:32636"))

l <- subset(route, route_id==1137)
p <- subset(stops, grepl("^1137_", UID))

plot(l, axes=TRUE, col="orange")
plot(p, add=TRUE, pch=19, cex=0.1)
text(p)

# distance along route
d <- sort(gProject(l, p))
d
# [1]     0  3051  3057  7221 10379 15657 20326 20326 22141 24262

# distance between stops
diff(d)
#[1] 3050.9166    5.9720 4164.2480 3157.7702 5278.5812 4668.1810    0.5878
#[8] 1814.9612 2120.8470

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