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我有一个针对不同位置的几百个经纬度坐标对的列表。我的目标是使用 R 估计从“家”位置到每个坐标对的行驶时间。

我googleway在 R 中使用该软件包取得了一些成功，但是（可以预见）在远离道路的位置遇到了问题，例如，如果坐标是山顶。在这些情况下，我想估计到每个有问题的坐标对的最近道路的行驶时间。

为了说明，假设我的家位置是；

home <- "Edinburgh, UK"

...以及我想找到的驾驶时间的示例数据框；

location <- c("place_a", "place_b", "place_c") 
latitude <- c("56.87034", "57.69380", "57.36243")
longitude <- c("-4.199001", "-5.128715", "-5.104728")

df <- data.frame(location, latitude, longitude)

我可以获得距离/持续时间等，在homeandplace_a之间，以及 between homeandplace_b使用类似的东西；

（注意。您需要自己的 Google Maps api 密钥来复制此部分...）

library(googleway)
api_key <- [insert your Google Maps api key here!]

results <- google_distance(origins = home,
                  destinations = list(c("56.87034,-4.199001"),
                                      ("57.69380,-5.128715")),
                  mode = "driving",
                  key = api_key,
                  units = "imperial")

我得到了我想要使用的所有数据：

results$rows[[1]]

place_c然而，当我们对返回的坐标尝试相同的坐标时，我们遇到了麻烦ZERO_RESULTS；

results2 <- google_distance(origins = home,
              destinations = ("57.36243,-5.104728"),
              mode = "driving",
              key = api_key,
              units = "imperial")

在这里，我认为问题在于坐标是半山腰，所以在这种情况下，我想找到离坐标最近的道路。我希望对 的nearest_road功能有一些运气，googleway但似乎无法让它工作，例如这样的东西不起作用；

df_points <- read.table(text = "lat lon
                     57.36243 -5.104728", header = T)

nearest_road <- google_nearestRoads(df_points, key = api_key)

谁能告诉这里的问题是什么？还是完全提出更好的解决方案？！

非常感谢。

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1 回答 1

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I am working on this very problem in a package that will be available on github soon (called spaceheater). In the meantime:

I would download the Open Street Maps shapefile from geofabrik for the country you are working with. For example Nigeria: http://download.geofabrik.de/africa/nigeria.html

EDITED WITH THE SUGGESTIONS OF monkeytennis(thanks!):

library(sp)
library(rgdal)
library(raster)
library(googleway)
library(geosphere)
library(foreach)
###I did Nigeria because I have it in my file downloaded, you would use UK###
roadshp <- readOGR(dsn="nigeria-latest-free.shp", 
layer="gis.osm_roads_free_1")
#Isolate primary roads (or secondary and tertiary) if you wish#
roads <- roadshp[roadshp$fclass %in% c("primary", "secondary", "tertiary"),]
#Use SpatialPoints for your gps coords
location <- c("place_a", "place_b", "place_c")
latitude <- c(8.641, 10.892, 11.797)
longitude <- c(6.0046, 11.146, 5.477)
df <- data.frame(location, latitude, longitude)
coordinates(df)=~longitude+latitude
sp1 <- SpatialPoints(df)
proj4string(sp1)=CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84
                 +towgs84=0,0,0")
clodist <- dist2Line(sp1, roads)
df <- as.data.frame(df)
df$clodist <- clodist[,c("distance")]
df$lat <- clodist[,c("lat")]
df$lon <- clodist[,c("lon")]
iters <- nrow(df)
origin <- as.character("9.056, 7.497")
gc1 <- data.frame(round(df[,c("lat")],3), round(df[,c("lon")],3))
colnames(gc1) <- c("lat","lon")
df$lat <- as.character(gc1$lat)
df$lon <- as.character(gc1$lon)
gt2 <- paste(df[,c("lat")], df[,c("lon")], sep=",")
results <- google_distance(origins =origin, destinations= gt2,
            mode="driving",
            key="Your API Key Here")
results <-unlist(results)
results <- as.data.frame(results)
ttt <- head(results,-1)
ttt <- ttt[-c(iters+1), ]
m1 <- matrix(ttt, ncol=iters, byrow=TRUE)
distance <- as.data.frame(m1)
rownames(distance) <- c("Address", "DistanceKM", 
"DistanceM","TimeTextLow","TimeSecondsLow","TimeTextHigh","TimeSecondsHigh", 
"Status")
于 2018-01-09T23:43:58.020 回答