rgeos :: gCentroid（）和sf :: st_centroid（）返回的值是否不同？ [英] Do the values returned by rgeos::gCentroid() and sf::st_centroid() differ?
问题描述
问题
执行
可复制示例
＃加载必要的软件包
库（sf）
库（rgeos）
＃加载数据
comarea606<-
read_sf（
dsn = https://data.cityofchicago.org/api/geospatial/cauq-8yn6?method=export&format=GeoJSON
，图层= OGRGeoJSON
）
＃查找每个多边形的质心
comarea606 $ centroids<-
st_centroid（x = comarea606 $ geometry）％>％
st_geometry（）
＃警告消息：
＃在st_centroid.sfc（x = comarea606 $ geometry）中：
＃st_centroid没有给出经度/纬度数据的正确质心
＃确保st_centroid（ ）方法d
＃包含与gCentroid（）
sf.centroids<-
st_coordinates（x = comarea606 $ centroids）
rgeos.centroids<-$相同的值b $ b gCentroid（
spgeom = Methods :: as（
object = comarea606
，Class = Spatial
）
，byid = TRUE
）@coords
＃确保姓氏相同
姓氏（rgeos.centroids）<-
姓氏（sf.centroids）
＃相等性测试
相同（
x = sf.centroids
，y = rgeos.centroids
）＃[1]否
全部.equal（
target = sf.centroids
，current = rgeos.centroids
）＃[1]是
＃查看前六个结果
头（sf。质心）
＃XY
＃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）
＃XY
＃1 -87.61 868 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
＃标识不相同的数字
sf.centroids [， X]％in％rgeos.centroids [， X]
＃[1]否否否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 FALSE FALSE $ 31 ] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
＃[41] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
＃[51] FALSE FALSE FALSE FALSE FALSE FALSE FALSE b＃[61]否否否否否否否否否否否否
＃[71]否否否错误假否否
sf.centroids [， Y]％in％rgeos.centroids [ ， Y]
＃[1]否否否否否否否否否否否否否否
＃[11]否否否否否否FALSE FALSE FALSE FALSE
＃[21] 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
＃[51] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
＃[61] FALSE FALSE FALSE FALSE
＃[61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE 71] FALSE FALSE FALSE FALSE FALSE FALSE
＃查看结果
par（
mar = c（2，0，3，0）
，bg = black
）
plot（
x = comarea606 $ geometry
，main =`sfv.`rgeos`：\nComparing Centroid Coordinates
，col .main = white
，col = black
，border = white
）
图（
x = comarea606 $ centroids
，加=真
，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
）
图例（
x = left
，图例= c（
来自sf :: st_coordinate（）`的中心
，来自`rgeos :: gCentroid（）`的中心
）
，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 =来源：Chicago Data Portal
，col = white
）
＃end脚本＃
会话信息
R版本3.4.4（2018-03-15）
平台：x86_64-apple-darwin15.6.0（64位）
运行于：macOS High Sierra 10.13.2
矩阵产品：默认
BLAS：/ System / Library / Fr ameworks / Accelerate.framework / Versions / A / Frameworks / vecLib.framework / Versions / A / libBLAS.dylib
LAPACK：/Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
语言环境：
[1] en_US.UTF-8 / en_US.UTF-8 / en_US.UTF-8 / C / en_US.UTF-8 / en_US.UTF-8
附加基本软件包：
[1]统计图形grDevices utils数据集方法
[7]基本
其他附加软件包：
[1] rgeos_0 .3-26 sf_0.6-0
通过名称空间（未附加）加载：
[1] modeltools_0.2-21 kernlab_0.9-25 reshape2_1.4.3
[4]点阵_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支柱_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 str ingr_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 ]评价_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标度_0.5.0
[40] backports_1.1.2 diptest_0.75-7 classInt_0.1-24
[43]壁球_1.0.8 gridExtra_2.3 ggplot2_2.2.1
[46]摘要_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]断言_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编译器_3.4.4
此问题简化为： all.equal（）与有何不同？ same（），我们去查看这些函数的文档：
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（）
一种安全可靠的方法来测试两个对象是否完全相等。在这种情况下返回TRUE，在其他情况下返回FALSE。
让我们更仔细地查看 all。 equal.numeric（），这是在这两个对象上调用的，因为它们都返回 double 和 typeof （）。我们看到 all.equal.numeric（）中有一个 tolerance 参数，该参数设置为 sqrt（.Machine $ double.eps），默认情况下。 .Machine $ double.eps 是您的计算机可以添加到 1 并能够区分的最小数字 1 。这不是确切的，但是大约是一个数量级。 all.equal.numeric（）本质上是检查向量中的所有值是否都是 near（）所有另一个向量中的值。您可以查看源代码（主要是错误检查），以确切地了解其操作方式。
为了使自己确信它们实际上不是 identical（），请尝试查看 sf.centroids-rgeos.centroids 的输出。
head（sf.centroids-rgeos.centroids）
＃XY
＃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
这两个矩阵最明确地几乎是相同的（但没有一个值是完全相同的）。
Question
Do the values returned by rgeos::gCentroid() and sf::st_centroid() differ? If so, how?
Context
After reading the relevant commands exported by rgeos section within the r-spatial/sf wiki, I was thrilled to see that I only needed the sf package - and no longer needed to import the rgeos package - to calculate the centroid of a given geometry.
However, the use of sf::st_centroid() gave me this warning, which is addressed here:
Warning message:
In st_centroid.sfc(x = comarea606$geometry) :
st_centroid does not give correct centroids for longitude/latitude data
That warning led me to test for equality between the two centroid retrieval methods, just to make sure that the coordinates are the same regardless of the method.
While my use of identical() and %in% resulted in non-identical matches, all.equal() and a map plotting the centroids from each method appear to be saying these two methods are nearly identical.
Is there any reason why one method would return a different set of values than the other?
Reproducible Example
# load neccessary packages
library( sf )
library( rgeos )
# load data
comarea606 <-
read_sf(
dsn = "https://data.cityofchicago.org/api/geospatial/cauq-8yn6?method=export&format=GeoJSON"
, layer = "OGRGeoJSON"
)
# find the centroid of each polygon
comarea606$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
This question simplifies to: how is all.equal() different from identical(), we go to the documentation for those functions:
all.equal(x, y) is a utility to compare R objects x and y testing ‘near equality’.
and for identical()
The safe and reliable way to test two objects for being exactly equal. It returns TRUE in this case, FALSE in every other case.
Lets look a little closer at all.equal.numeric(), which is what is called on these two objects, since both return "double" with typeof() . We see there is a tolerance argument in all.equal.numeric(), which is set to sqrt(.Machine$double.eps), by default. .Machine$double.eps is the smallest number that your machine can add to 1 and be able to distinguish it from 1. It's not exact, but it's on that order of magnitude. all.equal.numeric() essentially checks to see if all the values in a vector are near() all the values in another vector. You can look at the source code (which is mostly error checking) to see exactly how it does this.
To convince yourself that they are not, in fact, identical(), try looking at the output of sf.centroids - rgeos.centroids.
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
These two matrices are, most definitely, very nearly the same (but none of the values are exactly the same).
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