rgeos :: gCentroid（）和sf :: st_centroid（）返回的值是否不同？

时间：2018-03-18 02:34:58

标签： r equality centroid sf

问题

rgeos::gCentroid()和sf::st_centroid()返回的值是否有所不同？如果是这样，怎么样？

上下文

在阅读relevant commands exported by rgeos中的r-spatial/sf wiki部分后，我很高兴看到我只需要sf个包 - 而不再需要导入rgeos个包 - 计算给定几何体的质心。

但是，sf::st_centroid()的使用给了我这个警告，which is addressed here：

Warning message:
   In st_centroid.sfc(x = comarea606$geometry) :
   st_centroid does not give correct centroids for longitude/latitude data

这个警告让我测试了两个质心检索方法之间的相等性，只是为了确保坐标是相同的，无论方法如何。

虽然我使用identical()和%in%导致了不相同的匹配，all.equal()以及绘制每个方法的质心的地图显示为这两种方法几乎相同。

有没有理由为什么一个方法会返回一组不同于另一个方法的值？

SS of Centroid Comparison

可重复的示例

# load neccessary packages
library( sf )
library( rgeos )

# load data
comarea606 <-
    read_sf( 
      dsn = "https://data.cityofchicago.org/api/geospatial/cauq-8yn6?method=export&format=GeoJSON" 
      , layer = "OGRGeoJSON" 
    )

# find the centroid of each polygon
comarea606$centroids <-
  st_centroid( x = comarea606$geometry ) %>%
  st_geometry()

# Warning message:
#   In st_centroid.sfc(x = comarea606$geometry) :
#   st_centroid does not give correct centroids for longitude/latitude data

# Ensure the st_centroid() method
# contains identical values to gCentroid()
sf.centroids <-
  st_coordinates( x = comarea606$centroids )

rgeos.centroids <-
  gCentroid(
    spgeom = methods::as( 
      object = comarea606
      , Class = "Spatial"
    )
    , byid = TRUE 
  )@coords


# ensure the colnames are the same
colnames( rgeos.centroids ) <-
  colnames( sf.centroids )

# Test for equality
identical(
  x = sf.centroids
  , y = rgeos.centroids
) # [1] FALSE

all.equal(
  target = sf.centroids
  , current = rgeos.centroids
) # [1] TRUE

# View the first six results
head( sf.centroids )
#           X        Y
# 1 -87.61868 41.83512
# 2 -87.60322 41.82375
# 3 -87.63242 41.80909
# 4 -87.61786 41.81295
# 5 -87.59618 41.80892
# 6 -87.68752 41.97517
head( rgeos.centroids )
#           X        Y
# 1 -87.61868 41.83512
# 2 -87.60322 41.82375
# 3 -87.63242 41.80909
# 4 -87.61786 41.81295
# 5 -87.59618 41.80892
# 6 -87.68752 41.97517

# Identify the numbers which aren't identical
sf.centroids[ , "X" ] %in% rgeos.centroids[ , "X" ]
# [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
# [11] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
# [21] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
# [31] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
# [41] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
# [51] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
# [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
# [71] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
sf.centroids[ , "Y" ] %in% rgeos.centroids[ , "Y" ]
# [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
# [11] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
# [21] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
# [31] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
# [41] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
# [51] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
# [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
# [71] FALSE FALSE FALSE FALSE FALSE FALSE FALSE

# view results
par( 
  mar = c( 2, 0, 3, 0 )
  , bg = "black"
)
plot( 
  x = comarea606$geometry
  , main = "`sf` v. `rgeos`:\nComparing Centroid Coordinates"
  , col.main = "white"
  , col = "black"
  , border = "white"
)
plot( 
  x = comarea606$centroids
  , add = TRUE
  , pch = 24
  , col = "#B4DDF2"
  , bg  = "#B4DDF2"
  , cex = 1.2
)
points( 
  x = rgeos.centroids[ , "X" ]
  , y = rgeos.centroids[ , "Y" ]
  , pch = 24
  , col = "#FB0D1B"
  , bg  = "#FB0D1B"
  , cex = 0.6
)
legend(
  x = "left"
  , legend = c( 
    "Centroids from `sf::st_coordinate()`"
    , "Centroids from `rgeos::gCentroid()`"
  )
  , col      = c( "#B4DDF2", "#FB0D1B" )
  , pt.bg    = c( "#B4DDF2", "#FB0D1B" )
  , pch      = 24
  , bty      = "n"
  , cex      = 0.9
  , text.col = "white"
)
mtext(
  adj    = 0.99
  , line = 1
  , side = 1
  , cex  = 0.9
  , text = "Source: Chicago Data Portal"
  , col  = "white"
)

# end of script #

Session Info 

R version 3.4.4 (2018-03-15)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.2

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods  
[7] base     

other attached packages:
[1] rgeos_0.3-26 sf_0.6-0    

loaded via a namespace (and not attached):
 [1] modeltools_0.2-21 kernlab_0.9-25    reshape2_1.4.3   
 [4] lattice_0.20-35   colorspace_1.3-2  htmltools_0.3.6  
 [7] stats4_3.4.4      viridisLite_0.3.0 yaml_2.1.18      
[10] utf8_1.1.3        rlang_0.2.0       R.oo_1.21.0      
[13] e1071_1.6-8       pillar_1.2.1      withr_2.1.2      
[16] DBI_0.8           prabclus_2.2-6    R.utils_2.6.0    
[19] sp_1.2-7          fpc_2.1-11        plyr_1.8.4       
[22] robustbase_0.92-8 stringr_1.3.0     munsell_0.4.3    
[25] gtable_0.2.0      raster_2.6-7      R.methodsS3_1.7.1
[28] devtools_1.13.5   mvtnorm_1.0-7     memoise_1.1.0    
[31] evaluate_0.10.1   knitr_1.20        flexmix_2.3-14   
[34] class_7.3-14      DEoptimR_1.0-8    trimcluster_0.1-2
[37] Rcpp_0.12.16      udunits2_0.13     scales_0.5.0     
[40] backports_1.1.2   diptest_0.75-7    classInt_0.1-24  
[43] squash_1.0.8      gridExtra_2.3     ggplot2_2.2.1    
[46] digest_0.6.15     stringi_1.1.6     grid_3.4.4       
[49] rprojroot_1.3-2   rgdal_1.2-18      cli_1.0.0        
[52] tools_3.4.4       magrittr_1.5      lazyeval_0.2.1   
[55] tibble_1.4.2      cluster_2.0.6     crayon_1.3.4     
[58] whisker_0.3-2     dendextend_1.7.0  MASS_7.3-49      
[61] assertthat_0.2.0  rmarkdown_1.9     rstudioapi_0.7   
[64] viridis_0.5.0     mclust_5.4        units_0.5-1      
[67] nnet_7.3-12       compiler_3.4.4

1 个答案:

答案 0 :(得分：3)

此问题简化为：all.equal()与identical()有何不同，我们会转到这些函数的文档：

  

all.equal（x，y）是一个用于比较R对象x和y测试'接近相等'的实用程序。

和identical()

  

测试两个物体完全相等的安全可靠的方法。在这种情况下它返回TRUE，在其他每种情况下都返回FALSE。

让我们更接近all.equal.numeric()，这是对这两个对象的调用，因为它们都返回"double"和typeof()。我们在tolerance中看到all.equal.numeric()参数，默认设置为sqrt(.Machine$double.eps)。 .Machine$double.eps是您的计算机可以添加到1的最小数字，并且能够将其与1区分开来。这不是确切的，但它正处于这个数量级。 all.equal.numeric()基本上检查向量中的所有值是否为near()另一个向量中的所有值。您可以查看源代码（主要是错误检查），以确切了解它是如何做到的。

为了说服自己，事实上，identical()，请尝试查看sf.centroids - rgeos.centroids的输出。 

head(sf.centroids - rgeos.centroids)
#               X             Y
# 1 -5.056506e-10  2.623175e-09
# 2 -2.961613e-09 -4.269602e-09
# 3  4.235119e-10  4.358100e-09
# 4 -7.688925e-10 -1.051717e-09
# 5  1.226582e-09  1.668568e-10
# 6 -2.957009e-09  4.875247e-10

这两个矩阵绝对是非常接近的（但没有一个值完全相同）。

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