使用R識別到某個位置的最近點並計算沿網絡/道路的距離

Question

我有一系列位置（Points_B），想從一組不同的點（Points_A）中找到最接近它們的點，以及它們之間的距離（以kms為單位）。 我可以在烏鴉飛翔時做到這一點，但無法弄清楚如何在道路網（代碼中的“ Roads”對象）上做同樣的事情。 到目前為止，我的代碼如下：

library(sp)
library(rgdal)
library(rgeos)

download.file("https://dl.dropboxusercontent.com/u/27869346/Road_Shp.zip", "Road_Shp.zip")
#2.9mb 
unzip("Road_Shp.zip")
Roads <- readOGR(".", "Subset_Roads_WGS")

Points_A <- data.frame(ID = c("A","B","C","D","E","F","G","H","I","J","K","L"), ID_Lat  = c(50.91487, 50.92848, 50.94560, 50.94069, 50.92275, 50.94109, 50.92288, 50.92994, 50.92076, 50.90496, 50.89203, 50.88757), ID_Lon  = c(-1.405821, -1.423619, -1.383509, -1.396910, -1.441801, -1.459088, -1.466626, -1.369458, -1.340104, -1.360153, -1.344662, -1.355842))
rownames(Points_A) <- Points_A$ID

Points_B <- data.frame(Code = 1:30, Code_Lat  = c(50.92658, 50.92373, 50.93785, 50.92274, 50.91056, 50.88747, 50.90940, 50.91328, 50.91887, 50.92129, 50.91326, 50.91961, 50.91653, 50.90910, 50.91432, 50.93742, 50.91848, 50.93196, 50.94209, 50.92080, 50.92127, 50.92538, 50.88418, 50.91648, 50.91224, 50.92216, 50.90526, 50.91580, 50.91203, 50.91774), Code_Lon  = c(-1.417311, -1.457155, -1.400106, -1.374250, -1.335896, -1.362710, -1.360263, -1.430976, -1.461693, -1.417107, -1.426709, -1.439435, -1.429997, -1.413220, -1.415046, -1.440672, -1.392502, -1.459934, -1.432446, -1.357745, -1.374369, -1.458929, -1.365000, -1.426285, -1.403963, -1.344068, -1.340864, -1.399607, -1.407266, -1.386722))
rownames(Points_B) <- Points_B$Code

Points_A_SP <- SpatialPoints(Points_A[,2:3])
Points_B_SP <- SpatialPoints(Points_B[,2:3])
Distances <- (gDistance(Points_A_SP, Points_B_SP, byid=TRUE))*100

Points_B$Nearest_Points_A_CF <- colnames(Distances)[apply(Distances,1,which.min)]
Points_B$Distance_Points_A_CF <- apply(Distances,1,min)

我得到的輸出將是'Points_B'中的另外兩列，其中1）沿道路網絡具有最近的Point A對象ID ， 2）沿網絡的距離為km 。 任何幫助，將不勝感激。 謝謝。

Answer 1

我整天都在研究這種問題。 在ggmap包中嘗試mapdist() ，看看是否ggmap ：

library(dplyr)
library(ggmap)
#Your data
   Points_A <- data.frame(ID = c("A","B","C","D","E","F","G","H","I","J","K","L"), ID_Lat  = c(50.91487, 50.92848, 50.94560, 50.94069, 50.92275, 50.94109, 50.92288, 50.92994, 50.92076, 50.90496, 50.89203, 50.88757), ID_Lon  = c(-1.405821, -1.423619, -1.383509, -1.396910, -1.441801, -1.459088, -1.466626, -1.369458, -1.340104, -1.360153, -1.344662, -1.355842))
   Points_B <- data.frame(Code = 1:30, Code_Lat  = c(50.92658, 50.92373, 50.93785, 50.92274, 50.91056, 50.88747, 50.90940, 50.91328, 50.91887, 50.92129, 50.91326, 50.91961, 50.91653, 50.90910, 50.91432, 50.93742, 50.91848, 50.93196, 50.94209, 50.92080, 50.92127, 50.92538, 50.88418, 50.91648, 50.91224, 50.92216, 50.90526, 50.91580, 50.91203, 50.91774), Code_Lon  = c(-1.417311, -1.457155, -1.400106, -1.374250, -1.335896, -1.362710, -1.360263, -1.430976, -1.461693, -1.417107, -1.426709, -1.439435, -1.429997, -1.413220, -1.415046, -1.440672, -1.392502, -1.459934, -1.432446, -1.357745, -1.374369, -1.458929, -1.365000, -1.426285, -1.403963, -1.344068, -1.340864, -1.399607, -1.407266, -1.386722))

#Combine coords into one field (mapdist was doing something funny with the commas so I had to specify "%2C" here)
   Points_A$COORD <- paste(ID_Lat, ID_Lon, sep="%2C")
   Points_B$COORD <- paste(Code_Lat, Code_Lon, sep="%2C")

#use expand grid to generate all combos
   get_directions <- expand.grid(Start = Points_A$COORD,
                                 End = Points_B$COORD,
                                 stringsAsFactors = F,
                                 KEEP.OUT.ATTRS = F) %>%
                     left_join(select(Points_A, COORD, ID), by = c("Start" = "COORD")) %>%
                     left_join(select(Points_B, COORD, Code), by = c("End" = "COORD"))

#make a base dataframe
   route_df <- mapdist(from = get_directions$Start[1], 
                       to = get_directions$End[1], 
                       mode = "driving") %>% 
               mutate(Point_A = get_directions$ID[1],
                      Point_B = get_directions$Code[1])

#get the rest in a for-loop
  start <- Sys.time()
    for(i in 2:nrow(get_directions)){
      get_route <- mapdist(from = get_directions$Start[i], 
                           to = get_directions$End[i], 
                           mode = "driving") %>% 
                  mutate(Point_A = get_directions$ID[i],
                         Point_B = get_directions$Code[i])

      route_df <<- rbind(route_df, get_route) #add to your original file

      Sys.sleep(time = 1) #so google doesn't get mad at you for speed

      end <- Sys.time()

      print(paste(i, "of", nrow(get_directions), 
                  round(i/nrow(get_directions),4)*100, "%", sep=" "))
      print(end-start)
  }

#save if you want   
write.csv(route_df, "route_df.csv", row.names = F)    

#Route Evaluation
   closest_point <-route_df %>% 
                     group_by(Point_A) %>%
                     filter(km == min(km)) %>%
                     ungroup()

我對此還是有點陌生​​，因此可能會有更好的方法來處理數據。 希望這有幫助，祝你好運

Answer 2

如今，igraph，osmr和walkalytics軟件包似乎都提供了此功能。 存在特定於模式的路由網絡（具有不同程度的功能）。