两个长度不同的数据集之间的地理空间距离的平均值。

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英文:

Mean of geospatial distance between two datasets of different lengths

问题

我想计算toy1数据集中所有点与toy2数据集中最近点之间的平均距离。

所以，对于toy1数据集中的每个点，我想找到toy2数据集中距离最近的点，然后计算所有距离的平均值。

我希望距离以米为单位。

[![点击此处进入图像描述][1]][1]

toy1数据集的结构：

  1. 结构(list(random = c(0.424676549155265, 7.62225863989443, 
  2. 2.49231160385534, 8.09510331135243, 9.83170835534111, 0.697977161034942
  3. ), geometry = structure(list(structure(c(1219790.00733145, 6050277.88033381
  4. ), class = c("XY", "POINT", "sfg")), structure(c(1217790.00733145, 
  5. 6050777.88033381), class = c("XY", "POINT", "sfg")), structure(c(1218290.00733145, 
  6. 6050777.88033381), class = c("XY", "POINT", "sfg")), structure(c(1219290.00733145, 
  7. 6050777.88033381), class = c("XY", "POINT", "sfg")), structure(c(1219790.00733145, 
  8. 6050777.88033381), class = c("XY", "POINT", "sfg")), structure(c(1220290.00733145, 
  9. 6050777.88033381), class = c("XY", "POINT", "sfg"))), class = c("sfc_POINT", 
  10. "sfc"), precision = 0, bbox = structure(c(xmin = 1217790.00733145, 
  11. ymin = 6050277.88033381, xmax = 1220290.00733145, ymax = 6050777.88033381
  12. ), class = "bbox"), crs = structure(list(input = NA_character_, 
  13.     wkt = "PROJCS[\"RGF_1993_Lambert_93\",\n    GEOGCS[\"GCS_RGF_1993\",\n        DATUM[\"Reseau_Geodesique_Francais_1993\",\n            SPHEROID[\"GRS_1980\",6378137.0,298.257222101]],\n        PRIMEM[\"Greenwich\",0.0],\n        UNIT[\"Degree\",0.0174532925199433]],\n    PROJECTION[\"Lambert_Conformal_Conic_2SP\"],\n    PARAMETER[\"False_Easting\",700000.0],\n    PARAMETER[\"False_Northing\",6600000.0],\n    PARAMETER[\"Central_Meridian\",3.0],\n    PARAMETER[\"Standard_Parallel_1\",49.0],\n    PARAMETER[\"Standard_Parallel_2\",44.0],\n    PARAMETER[\"Latitude_Of_Origin\",46.5],\n    UNIT[\"Meter\",1.0]]"), class = "crs"), n_empty = 0L), 
  14.     long = c(1219790.00733145, 1217790.00733145, 1218290.00733145, 
  15.     1219290.00733145, 1219790.00733145, 1220290.00733145), lat = c(6050277.88033381, 
  16.     6050777.88033381, 6050777.88033381, 6050777.88033381, 6050777.88033381, 
  17.     6050777.88033381)), row.names = c(NA, -6L), sf_column = "geometry", agr = structure(c(random = NA_integer_, 
  18. long = NA_integer_, lat = NA_integer_), .Label = c("constant", 
  19. "aggregate", "identity"), class = "factor"), class = c("sf", 
  20. "tbl_df", "tbl", "data.frame"))

toy2数据集的结构：

  1. 结构(list(random = c(8.91735465731472, 1.18594053201377, 
  2. 3.9330403204076, 9.10003933124244, 9.85382732702419, 7.3299086955376, 
  3. 7.75479080388322, 0.621909373439848, 9.07300168415532, 2.00036991853267
  4. ), geometry = structure(list(structure(c(1215290.00733145, 6053277.88033381
  5. ), class = c("XY", "POINT", "sfg")), structure(c(1212790.00733145, 
  6. 6053777.88033381), class = c("XY", "POINT", "sfg")), structure(c(1216790.00733145, 
  7. 6053777.88033381), class = c("XY", "POINT", "sfg")), structure(c(1217290.00733145, 
  8. 6053777.88033381), class = c("XY", "POINT", "sfg")), structure(c(1217790.00733145, 
  9. 6053777.88033381), class = c("XY", "POINT", "sfg")), structure(c(1218290.00733145, 
  10. 6053777.88033381), class = c("XY", "POINT", "sfg")), structure(c(1219290.00733145, 
  11. 6053777.88033381), class = c("XY", "POINT", "sfg")), structure(c(1215790.00733145, 
  12. 6054277.88033381), class = c("XY", "POINT", "sfg")), structure(c(1216790.00733145, 
  13. 6054277.88033381), class = c("XY", "POINT", "sfg")), structure(c(1221290.00733145, 
  14. 6054277.88033381), class = c("XY", "POINT", "sfg"))), class = c("sfc_POINT", 
  15. "sfc"), precision = 0, bbox = structure(c(xmin = 1212790.00733145, 
  16. ymin = 6053277.88033381, xmax = 1221290.00733145, ymax = 6054277.88033381
  17. ), class = "bbox"), crs = structure(list(input = NA_character_, 
  18.     wkt = "PROJCS[\"RGF_1993_Lambert_93\",\n    GEOGCS[\"GCS_RGF_1993\",\n        DATUM[\"Reseau_Geodesique_Francais_1993\",\n            SPHEROID[\"GRS_1980\",6378137.
  20. <details>
  21. <summary>英文:</summary>
  23. I want to compute the mean distance between all points of the toy1 dataset with the closest point of the toy2 dataset.
  25. So for each point of the toy1 dataset, I want to find the closest point among the points of the toy2 dataset, and then compute a mean with all the distances.
  27. I want the distances to be in meters.
  29. [![enter image description here][1]][1]
  31. the structure of toy1 dataset :

structure(list(random = c(0.424676549155265, 7.62225863989443,
2.49231160385534, 8.09510331135243, 9.83170835534111, 0.697977161034942
), geometry = structure(list(structure(c(1219790.00733145, 6050277.88033381
), class = c("XY", "POINT", "sfg")), structure(c(1217790.00733145,
6050777.88033381), class = c("XY", "POINT", "sfg")), structure(c(1218290.00733145,
6050777.88033381), class = c("XY", "POINT", "sfg")), structure(c(1219290.00733145,
6050777.88033381), class = c("XY", "POINT", "sfg")), structure(c(1219790.00733145,
6050777.88033381), class = c("XY", "POINT", "sfg")), structure(c(1220290.00733145,
6050777.88033381), class = c("XY", "POINT", "sfg"))), class = c("sfc_POINT",
"sfc"), precision = 0, bbox = structure(c(xmin = 1217790.00733145,
ymin = 6050277.88033381, xmax = 1220290.00733145, ymax = 6050777.88033381
), class = "bbox"), crs = structure(list(input = NA_character_,
wkt = "PROJCS[&quot;RGF_1993_Lambert_93&quot;,\n GEOGCS[&quot;GCS_RGF_1993&quot;,\n DATUM[&quot;Reseau_Geodesique_Francais_1993&quot;,\n SPHEROID[&quot;GRS_1980&quot;,6378137.0,298.257222101]],\n PRIMEM[&quot;Greenwich&quot;,0.0],\n UNIT[&quot;Degree&quot;,0.0174532925199433]],\n PROJECTION[&quot;Lambert_Conformal_Conic_2SP&quot;],\n PARAMETER[&quot;False_Easting&quot;,700000.0],\n PARAMETER[&quot;False_Northing&quot;,6600000.0],\n PARAMETER[&quot;Central_Meridian&quot;,3.0],\n PARAMETER[&quot;Standard_Parallel_1&quot;,49.0],\n PARAMETER[&quot;Standard_Parallel_2&quot;,44.0],\n PARAMETER[&quot;Latitude_Of_Origin&quot;,46.5],\n UNIT[&quot;Meter&quot;,1.0]]"), class = "crs"), n_empty = 0L),
long = c(1219790.00733145, 1217790.00733145, 1218290.00733145,
1219290.00733145, 1219790.00733145, 1220290.00733145), lat = c(6050277.88033381,
6050777.88033381, 6050777.88033381, 6050777.88033381, 6050777.88033381,
6050777.88033381)), row.names = c(NA, -6L), sf_column = "geometry", agr = structure(c(random = NA_integer_,
long = NA_integer_, lat = NA_integer_), .Label = c("constant",
"aggregate", "identity"), class = "factor"), class = c("sf",
"tbl_df", "tbl", "data.frame"))

  2. The structure of toy2 dataset :

structure(list(random = c(8.91735465731472, 1.18594053201377,
3.9330403204076, 9.10003933124244, 9.85382732702419, 7.3299086955376,
7.75479080388322, 0.621909373439848, 9.07300168415532, 2.00036991853267
), geometry = structure(list(structure(c(1215290.00733145, 6053277.88033381
), class = c("XY", "POINT", "sfg")), structure(c(1212790.00733145,
6053777.88033381), class = c("XY", "POINT", "sfg")), structure(c(1216790.00733145,
6053777.88033381), class = c("XY", "POINT", "sfg")), structure(c(1217290.00733145,
6053777.88033381), class = c("XY", "POINT", "sfg")), structure(c(1217790.00733145,
6053777.88033381), class = c("XY", "POINT", "sfg")), structure(c(1218290.00733145,
6053777.88033381), class = c("XY", "POINT", "sfg")), structure(c(1219290.00733145,
6053777.88033381), class = c("XY", "POINT", "sfg")), structure(c(1215790.00733145,
6054277.88033381), class = c("XY", "POINT", "sfg")), structure(c(1216790.00733145,
6054277.88033381), class = c("XY", "POINT", "sfg")), structure(c(1221290.00733145,
6054277.88033381), class = c("XY", "POINT", "sfg"))), class = c("sfc_POINT",
"sfc"), precision = 0, bbox = structure(c(xmin = 1212790.00733145,
ymin = 6053277.88033381, xmax = 1221290.00733145, ymax = 6054277.88033381
), class = "bbox"), crs = structure(list(input = NA_character_,
wkt = "PROJCS[&quot;RGF_1993_Lambert_93&quot;,\n GEOGCS[&quot;GCS_RGF_1993&quot;,\n DATUM[&quot;Reseau_Geodesique_Francais_1993&quot;,\n SPHEROID[&quot;GRS_1980&quot;,6378137.0,298.257222101]],\n PRIMEM[&quot;Greenwich&quot;,0.0],\n UNIT[&quot;Degree&quot;,0.0174532925199433]],\n PROJECTION[&quot;Lambert_Conformal_Conic_2SP&quot;],\n PARAMETER[&quot;False_Easting&quot;,700000.0],\n PARAMETER[&quot;False_Northing&quot;,6600000.0],\n PARAMETER[&quot;Central_Meridian&quot;,3.0],\n PARAMETER[&quot;Standard_Parallel_1&quot;,49.0],\n PARAMETER[&quot;Standard_Parallel_2&quot;,44.0],\n PARAMETER[&quot;Latitude_Of_Origin&quot;,46.5],\n UNIT[&quot;Meter&quot;,1.0]]"), class = "crs"), n_empty = 0L),
long = c(1215290.00733145, 1212790.00733145, 1216790.00733145,
1217290.00733145, 1217790.00733145, 1218290.00733145, 1219290.00733145,
1215790.00733145, 1216790.00733145, 1221290.00733145), lat = c(6053277.88033381,
6053777.88033381, 6053777.88033381, 6053777.88033381, 6053777.88033381,
6053777.88033381, 6053777.88033381, 6054277.88033381, 6054277.88033381,
6054277.88033381)), row.names = c(NA, -10L), sf_column = "geometry", agr = structure(c(random = NA_integer_,
long = NA_integer_, lat = NA_integer_), .Label = c("constant",
"aggregate", "identity"), class = "factor"), class = c("sf",
"tbl_df", "tbl", "data.frame"))

  2. I found some topics related, but as the two datasets do not have the same number of points I got errors in the functions.
  3. The function distHaversine from geospatial package seems to require the same number of points in the two arguments...
  4. [1]: https://i.stack.imgur.com/qqHGJ.png
  5. </details>
  6. # 答案1
  7. **得分**: 2
  8. one approach:
  9. ```R
  10. library(dplyr)
  11. library(sf)
  12. toy_1_with_distances <- 
  13. toy_1 %>%
  14. rowwise() %>%
  15. mutate(closest_point_row_toy_2 = st_nearest_feature(geometry, toy_2),
  16. closest_point = toy_2[closest_point_row_toy_2,],
  17. dist_to_closest = st_distance(geometry, closest_point)
  18. ) 
  1. > toy1_with_distances %>% pull(dist_to_closest) %>% mean()
  2. 3123.199 [m]
英文:

one approach:

  1. library(dplyr)
  2. library(sf)
  3. toy_1_with_distances &lt;- 
  4. toy_1 |&gt;
  5. rowwise() |&gt;
  6. mutate(closest_point_row_toy_2 = st_nearest_feature(geometry, toy_2),
  7. closest_point = toy_2[closest_point_row_toy_2,],
  8. dist_to_closest = st_distance(geometry, closest_point)
  9. ) 
  1. &gt; toy1_with_distances |&gt; pull(dist_to_closest) |&gt; mean()
  2. 3123.199 [m]

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  • 本文由 发表于 2023年6月6日 17:20:25
  • 转载请务必保留本文链接：https://go.coder-hub.com/76413164.html
  • distance
  • geospatial
  • nearest-neighbor
  • r
  • r-sf
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