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一連の場所 (Points_B) があり、別のポイント セット (Points_A) からそれらに最も近いポイントとそれらの間の距離 (km) を見つけたいと考えています。カラスが飛ぶようにこれを行うことはできますが、道路網 (コード内の「Roads」オブジェクト) に沿って同じことを行う方法がわかりません。私がこれまでに持っているコードは次のとおりです。

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)

私が求めている出力は、「Points_B」に1) 道路網に沿った最も近い Point A オブジェクト IDを持ち、2) ネットワークに沿った km の距離を持つ2 つの追加の列になります。どんな助けでも大歓迎です。ありがとう。

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2 に答える 2

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私は一日中この種の問題に取り組んできました。パッケージを試しmapdist()て、これが機能するかどうかを確認します。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()

私はまだこれに慣れていないので、データのラングリングを行うためのより良い方法があるかもしれません。これが役に立てば幸いです。幸運を祈ります

于 2016-06-24T19:29:01.960 に答える