如何计算R中沿列的经纬度之间的距离？ [英] How can I calculate the distance between latitude and longitude along rows of columns in R?

问题描述

我的df看起来像这样：

My df looks like this:

    bid        ts    latitude  longitude
1  827566 1999-10-07 42.40944 -88.17822
2  827566 2013-04-11 41.84740 -87.63126
3 1902966 2012-05-02 45.52607 -94.20649
4 1902966 2013-03-25 41.94083 -87.65852
5 3211972 2012-08-14 43.04786 -87.96618
6 3211972 2013-08-02 41.88258 -87.63760

我想创建一个新的df来计算距每个连续点的时间和距离之差。我想计算按出价分组的相同行。我使用以下for循环来完成此操作：

I want to create a new df that calculates the difference in time and distance from each successive point. I would like to calculate down the rows grouped by bid's that are the same. I used the following for loop to accomplish this:

library(geosphere)
   lengthdata <- nrow(twopoint)
   twopointdata <- data.frame(matrix(ncol = 4, nrow =lengthdata))
   x <- c("bid", "time", "d", "dsq")
   colnames(twopointdata) <- x
   n <- numeric()
   n <- 1

   for (i in 1:lengthdata)
   {
     if (twopoint[i+1,1] == twopoint[i,1]) 
     {
       twopointdata[n,1] <- twopoint[i+1,1]
       twopointdata[n,2] <- as.numeric(twopoint[i+1,5]-twopoint[i,5])
       twopointdata[n,3] <- distm(c(twopoint[i+1,10], twopoint[i+1,9]), 
                              c(twopoint[i,10],twopoint[i,9]), fun = 
                                   distHaversine)
       twopointdata[n,4] <- twopoint[n,3]^2
       n <- n+1
     }

   }
   attach(twopointdata)
   head(twopointdata)

（某些列n个数字已关闭，因为我取出了一些行以便更清楚地显示）

(some of the column numbers are off because I took out some rows to display more clearly)

我的结果如下：

      bid time    d          dsq
1  827566 4935  77159.8 5.677201e+11
2 1902966  327 660457.0 6.436004e+16
3 3211972  353 132494.8 3.540118e+12
4 3692174 4722 727359.6 6.394166e+16
5 4404655 4833 201644.7 1.092944e+13
6 6644203 4518 210485.9 6.721980e+16

它具有每个数据点的ID，每个数据点之间的时间差，从长和纬度计算出的距离以及距离的平方。问题：速度非常慢，最终我将在非常大的数据集上进行此操作。

It has the ids for each data point, time difference between each, distance calculated from long and lat, and the squared distance. PROBLEM: it's very slow and eventually i'll be doing this on a very large data set.

使用dplyr，我能够成功地在没有for循环的情况下完成时差，

I was able to do this without a for loop successfully with the time difference using dplyr like this:

 library(dplyr)
 library(geosphere)
 latlongdata2 <- latlongdata 
 latlongdata2 %>%
  group_by(bid)%>%
  transmute(
    bid = bid,
    t = c(NA,diff(ts)))

我不知道如何使用纬度和经度，因为与ts值不同，它们位于两个不同的列中。有人有什么建议吗？

I can't figure out how to do this with the latitude and longitude because unlike the ts values they are in two different columns. Anyone have any suggestions?

P.S。该项目的总体目标是对数据进行均方位移分析。

P.S. the overall aim of the project is to do a mean squared displacement analysis on the data.

推荐答案

我认为您过于复杂了一点。我希望 geosphere :: distHaversine 有一个更直观的调用方法（类似于 diff ），但是不难解决：

I think you're overcomplicating it a little. I wish geosphere::distHaversine had a slightly more intuitive calling method (similar to, say, diff), but it's not hard to work around it:

dat <- read.table(text = "  bid        ts    latitude  longitude
 827566 1999-10-07 42.40944 -88.17822
 827566 2013-04-11 41.84740 -87.63126
1902966 2012-05-02 45.52607 -94.20649
1902966 2013-03-25 41.94083 -87.65852
3211972 2012-08-14 43.04786 -87.96618
3211972 2013-08-02 41.88258 -87.63760", header = TRUE, stringsAsFactors = FALSE)
dat$ts <- as.Date(dat$ts)

library(dplyr)
library(geosphere)
group_by(dat, bid) %>%
  mutate(
    d = c(NA,
          distHaversine(cbind(longitude[-n()], latitude[-n()]),
                        cbind(longitude[  -1], latitude[  -1]))),
    dts = c(NA, diff(ts))
  ) %>%
  ungroup() %>%
  filter( ! is.na(d) )
# # A tibble: 3 × 6
#       bid         ts latitude longitude         d   dts
#     <int>     <date>    <dbl>     <dbl>     <dbl> <dbl>
# 1  827566 2013-04-11 41.84740 -87.63126  77159.35  4935
# 2 1902966 2013-03-25 41.94083 -87.65852 660457.41   327
# 3 3211972 2013-08-02 41.88258 -87.63760 132494.65   353

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