越过两组坐标以计算最小距离？ tidyr地球圈R

如何解决越过两组坐标以计算最小距离？ tidyr地球圈R

在同一边界区域中，我有一组2,220个嵌套坐标（var1）和另一组26个界标坐标（var2）。我想找到2624组中每个点之间的2,224个坐标之间的距离，以便创建带有列（嵌套坐标，最小距离界标坐标，以m为单位的距离）的新数据框。

我被困在尝试交叉这两个集合以生成一个集合，其中所有界标坐标与每个嵌套坐标配对。

**nest**                **landmark**         **distance**

lat1,lon1              lat1,lon1            34
lat1,lon1              lat2,lon2            18
lat1,lon1              lat3,lon3            82
....
lat1,lon1              lat26,lon26           61
lat2,lon2              lat1,lon1            94
lat2,lon2              lat2,lon2            38
...
lat2,220,lon 2,220     lat 26,lon26          46

我尝试过交（var1，var2），其中var1和var2都是包含经度和纬度值的矩阵，然后计算每个结果行之间的Haversine距离（请参见下文）。这似乎可行，但是我认为这并没有给我期望的确切结果。交叉产生的行数与这些集合的nrow的乘积不一致。

我还希望能够将具有所有距离值的结果集分成26个组，其中每个组包含嵌套坐标（每行重复），26个界标坐标之一以及两点。从那里，我将选择距离最小的行。

newset <- crossing(nests,landmarks)
mindist <- distHaversine(newset[1],newset[2],r=6378137)
newsetwdist <- cbind(newset,mindist)

sv <- split(newsetwdist,rep(1:56056,each=26))
#56056 was the resulting number of rows,even though I expected 57,720.

var3 <- lapply(sv,"[",3) #returns a nested list of all distances for each nest
var4 <- lapply(var2,"[[","mindist")

df = as.data.frame(do.call(rbind,lapply(var4,unlist)))
min.dist.from.landmark <- apply(df,1,FUN=min)

似乎应该是一个简单的解决方法，我们将不胜感激。

解决方法

使用我为此创建的数据和数据格式，您可以执行以下操作。

N = 10      # Number of generations (years)

S = 50       # Number of seeds each pea plant produces

P0 = 20      # Number of pea plants in year 0

w = 0.25      # Fraction of seeds that survive the winter (survival rate)

g = [0.0,0.02,0.04,0.06,0.08,0.10,0.12,0.14,0.16]       # Fraction of seeds that germinate (germination rate)


P = []                # Number of pea plants

P.append(P0)          # Append initial number of pea plants

t = range(N)       # Years


remaining_seeds = []      # One-year-old seeds that did not germinate and remained in the ground

remaining_seeds.append(0) # In year 0 this is 0



n = 1  # Generation (year)

while n < N:

    produced_seeds = S * P[n-1]

    surviving_seeds = produced_seeds*w

    plants_1_year = choice(g) * surviving_seeds

    
    remaining_seeds_n = (1- choice(g))*surviving_seeds

    remaining_seeds.append(remaining_seeds_n)


    surviving_seeds_2 = w * remaining_seeds[n-1]

    plants_2_year = choice(g)*surviving_seeds_2


    P_n = plants_1_year+plants_2_year

    P.append(P_n)


    n = n + 1



plot(t,P,"-o")

xlabel("Generation,t")

ylabel("Number of pea plants,P")

title("Plant population when seeds survive two winters,with variable germination rate")

show()

数据

library(dplyr)
library(purrr)
library(tidyr)
library(geosphere)

crossing(nest,landmark) %>%
  mutate(nest_long_lat = map2(nest_long,nest_lat,~ c(.x,.y)),mark_long_lat = map2(mark_long,mark_lat,distance = unlist(map2(mark_long_lat,nest_long_lat,~ distGeo(.x,.y)))) %>%
  group_by(nest_long_lat) %>%
  mutate(min_distance = distance == min(distance)) %>%
  ungroup() %>%
  select(-nest_long_lat,-mark_long_lat)

# # A tibble: 57,720 x 6
#          nest_lat nest_long mark_lat mark_long distance min_distance
#          <dbl>    <dbl>     <dbl>    <dbl>     <dbl>    <lgl>       
# 1        46.5      49.1     48.4      49.8     215350.  TRUE        
# 2        46.5      49.1     48.6      48.7     229592.  FALSE       
# 3        46.5      49.1     48.8      49.9     255689.  FALSE       
# 4        46.5      49.1     48.9      48.4     268789.  FALSE       
# 5        46.5      49.1     49.3      50.1     312691.  FALSE       
# 6        46.5      49.1     49.3      49.2     309549.  FALSE       
# 7        46.5      49.1     49.6      51.6     390862.  FALSE       
# 8        46.5      49.1     49.7      50.8     371686.  FALSE       
# 9        46.5      49.1     49.8      50.6     377182.  FALSE       
# 10       46.5      49.1     49.9      49.9     376530.  FALSE       
# # … with 57,710 more rows