2个经纬度点列表(坐标)之间的地理/地理空间距离 [英] Geographic / geospatial distance between 2 lists of lat/lon points (coordinates)
问题描述
我有2个列表(list1，list2)，其中包含各个位置的纬度/经度.一个列表(list2)具有list1没有的位置名称.
I have 2 lists (list1, list2) with latitude / longitudes of various locations. One list (list2) has locality names that list1 does not have.
我也想要list1中每个点的近似位置.所以我想在list1中取一个点，尝试在list2中查找最近的点并采用该局部性.我对list1中的每个点重复一遍.它还需要距离(以米为单位)和点的索引(在list1中)，因此我可以围绕它建立一些业务规则-本质上，这是应该添加到list1(near_dist， indx).
I want an approximate locality for every point in list1 as well. So I want to take a point in list1, try to look for the nearest point in list2 and take that locality. I repeat for every point in list1. It also want the distance (in meters) and the index of the point (in list1) so I can build some business rules around it - essentially these are 2 new cols that should be added to list1 (near_dist, indx).
我正在使用gdist函数，但是无法将其用于数据框输入.
I am using the gdist function, but I'm unable to get this to work with data frame inputs.
示例输入列表:
list1 <- data.frame(longitude = c(80.15998, 72.89125, 77.65032, 77.60599,
72.88120, 76.65460, 72.88232, 77.49186,
72.82228, 72.88871),
latitude = c(12.90524, 19.08120, 12.97238, 12.90927,
19.08225, 12.81447, 19.08241, 13.00984,
18.99347, 19.07990))
list2 <- data.frame(longitude = c(72.89537, 77.65094, 73.95325, 72.96746,
77.65058, 77.66715, 77.64214, 77.58415,
77.76180, 76.65460),
latitude = c(19.07726, 13.03902, 18.50330, 19.16764,
12.90871, 13.01693, 13.00954, 12.92079,
13.02212, 12.81447),
locality = c("A", "A", "B", "B", "C", "C", "C", "D", "D", "E"))
推荐答案
要使用纬度/经度坐标计算两个点之间的地理距离，可以使用多个公式.包geosphere具有用于计算距离的distCosine，distHaversine，distVincentySphere和distVincentyEllipsoid.其中，distVincentyEllipsoid被认为是最准确的一个，但是在计算上比其他的更为密集.
To calculate the geographic distance between two points with latitude/longitude coordinates, you can use several formula's. The package geosphere has the distCosine, distHaversine, distVincentySphere and distVincentyEllipsoid for calculating the distance. Of these, the distVincentyEllipsoid is considered the most accurate one, but is computationally more intensive than the other ones.
使用这些功能之一，您可以创建一个距离矩阵.然后，基于该矩阵，您可以根据最短距离(which.min)和min(请参见答案的最后部分)为最短距离分配locality名称，如下所示:
With one of these functions, you can make a distance matrix. Based on that matrix you can then assign locality names based on shortest distance with which.min and the corresponding distance with min (see for this the last part of the answer) like this:
library(geosphere)
# create distance matrix
mat <- distm(list1[,c('longitude','latitude')], list2[,c('longitude','latitude')], fun=distVincentyEllipsoid)
# assign the name to the point in list1 based on shortest distance in the matrix
list1$locality <- list2$locality[max.col(-mat)]
这给出了:
> list1
longitude latitude locality
1 80.15998 12.90524 D
2 72.89125 19.08120 A
3 77.65032 12.97238 C
4 77.60599 12.90927 D
5 72.88120 19.08225 A
6 76.65460 12.81447 E
7 72.88232 19.08241 A
8 77.49186 13.00984 D
9 72.82228 18.99347 A
10 72.88871 19.07990 A
另一种可能性是根据list2中locality的平均经度和纬度值来分配locality:
Another possibility is to assign the locality based on the average longitude and latitude values of the localitys in list2:
library(dplyr)
list2a <- list2 %>% group_by(locality) %>% summarise_each(funs(mean)) %>% ungroup()
mat2 <- distm(list1[,c('longitude','latitude')], list2a[,c('longitude','latitude')], fun=distVincentyEllipsoid)
list1 <- list1 %>% mutate(locality2 = list2a$locality[max.col(-mat2)])
或使用data.table:
library(data.table)
list2a <- setDT(list2)[,lapply(.SD, mean), by=locality]
mat2 <- distm(setDT(list1)[,.(longitude,latitude)], list2a[,.(longitude,latitude)], fun=distVincentyEllipsoid)
list1[, locality2 := list2a$locality[max.col(-mat2)] ]
这给出了:
> list1
longitude latitude locality locality2
1 80.15998 12.90524 D D
2 72.89125 19.08120 A B
3 77.65032 12.97238 C C
4 77.60599 12.90927 D C
5 72.88120 19.08225 A B
6 76.65460 12.81447 E E
7 72.88232 19.08241 A B
8 77.49186 13.00984 D C
9 72.82228 18.99347 A B
10 72.88871 19.07990 A B
如您所见，这在大多数情况下(十分之七)导致了另一个已分配的locality.
As you can see, this leads in most (7 out of 10) occasions to another assigned locality.
您可以使用以下方法添加距离:
You can add the distance with:
list1$near_dist <- apply(mat2, 1, min)
或使用max.col的另一种方法(可能更快):
or another approach with max.col (which is highly probable faster):
list1$near_dist <- mat2[matrix(c(1:10, max.col(-mat2)), ncol = 2)]
# or using dplyr
list1 <- list1 %>% mutate(near_dist = mat2[matrix(c(1:10, max.col(-mat2)), ncol = 2)])
# or using data.table (if not already a data.table, convert it with 'setDT(list1)' )
list1[, near_dist := mat2[matrix(c(1:10, max.col(-mat2)), ncol = 2)] ]
结果:
> list1
longitude latitude locality locality2 near_dist
1: 80.15998 12.90524 D D 269966.8970
2: 72.89125 19.08120 A B 65820.2047
3: 77.65032 12.97238 C C 739.1885
4: 77.60599 12.90927 D C 9209.8165
5: 72.88120 19.08225 A B 66832.7223
6: 76.65460 12.81447 E E 0.0000
7: 72.88232 19.08241 A B 66732.3127
8: 77.49186 13.00984 D C 17855.3083
9: 72.82228 18.99347 A B 69456.3382
10: 72.88871 19.07990 A B 66004.9900
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