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为什么我的空间连接使用sp和sf包返回不同的结果？

[英]Why is my spatial join returning different results using the sp versus the sf package?

 原文  2018-06-27 04:11:08  2   1    r/ spatial/ sp/ sf

问题描述

In the following reprex, I run a spatial join on some point and polygon data, but unexpectedly get different results when using the sp package from when I use the sf package. 在下面的reprex中，我对某些点和面数据运行了空间连接，但是使用sp包和使用sf包时意外地得到了不同的结果。 Why is this? 为什么是这样？

I am trying to count acled points within prio grid squares, but as shown below, my counts differ between packages even though running a st_covers join from sf , should to my knowledge be functionally the same as using the over method from sp . 我想指望acled内的点prio方格，而是如下图所示，我的数包之间的差异，即使运行st_covers从加入sf ，应该就我所知，在功能上是一样使用over的方法从sp 。 

library(sp) # packageVersion("sp") #> [1] ‘1.2.7’
library(sf) # packageVersion("sf") #> [1] ‘0.6.3’
library(rgdal)
library(maptools)
library(dplyr); library(tibble)

Here is the sample data I'm working with: 这是我正在使用的示例数据： 

# prio (polygon squares) and acled (points); in both sp and sf objects:

# prio sf polygons object
priosf <- structure(list(
  CELL_ID = c(180365, 176783, 150830, 145866, 140055), 
  gwno = c(615L, 616L, 432L, 626L, 475L), 
  POP = c(111983.7, 107369.7, 12169.35, 23005.76, 527012.1), 
  prio_country = c("Algeria", "Tunisia", "Mali", "South Sudan", "Nigeria"), 
  geometry = structure(list(structure(list(structure(c(2, 2, 2.5, 2.5, 2, 35, 35.5, 35.5, 35, 35), 
                                                     .Dim = c(5L, 2L))), class = c("XY", "POLYGON", "sfg")), 
                            structure(list(structure(c(11, 11, 11.5, 11.5, 11, 32.5, 33, 33, 32.5, 32.5), 
                                                     .Dim = c(5L, 2L))), class = c("XY", "POLYGON", "sfg")), 
                            structure(list(structure(c(-5.5, -5.5, -5, -5, -5.5, 14.5, 15, 15, 14.5, 14.5), 
                                                     .Dim = c(5L, 2L))), class = c("XY", "POLYGON", "sfg")), 
                            structure(list(structure(c(32.5, 32.5, 33, 33, 32.5, 11, 11.5, 11.5, 11, 11),
                                                     .Dim = c(5L, 2L))), class = c("XY", "POLYGON", "sfg")), 
                            structure(list(structure(c(7, 7, 7.5, 7.5, 7, 7, 7.5, 7.5, 7, 7), 
                                                     .Dim = c(5L, 2L))), class = c("XY", "POLYGON", "sfg"))), 
                       class = c("sfc_POLYGON", "sfc"), precision = 0, 
                       bbox = structure(c(-5.5, 7, 33, 35.5), 
                                        .Names = c("xmin", "ymin", "xmax", "ymax"), 
                                        class = "bbox"), 
                       crs = structure(list(epsg = 4326L, proj4string = "+proj=longlat +datum=WGS84 +no_defs"), 
                                       .Names = c("epsg", "proj4string"), class = "crs"), n_empty = 0L)), 
              .Names = c("CELL_ID", "gwno", "POP", "prio_country", "geometry"),
  row.names = c(NA, -5L), class = c("sf", "tbl_df", "tbl", "data.frame"),
  sf_column = "geometry", agr = structure(c(NA_integer_, NA_integer_, NA_integer_, NA_integer_), 
                                          class = "factor", .Label = c("constant", "aggregate", "identity"), 
                                          .Names = c("CELL_ID", "gwno", "POP", "prio_country")))

# prio sp polygons object
priosp <- as(priosf, 'Spatial')

# acled data
acled <- structure(list(
  EVENT_ID_CNTY = c("ALG3195", "ALG3316", "ALG4228", 
      "ALG4824", "MLI1050", "MLI1144", "MLI1423", "MLI1672", "NIG4606", 
      "NIG4951", "NIG6196", "NIG7661", "NIG9100", "SSD1216", "SSD1504", 
      "SSD3232", "SSD3234", "SSD3231", "SSD3239", "TUN1376", "TUN2597", 
      "TUN3217", "TUN3633"), 
  COUNTRY = c("Algeria", "Algeria", "Algeria", 
              "Algeria", "Mali", "Mali", "Mali", "Mali", "Nigeria", "Nigeria", 
              "Nigeria", "Nigeria", "Nigeria", "South Sudan", "South Sudan", 
              "South Sudan", "South Sudan", "South Sudan", "South Sudan", "Tunisia", 
              "Tunisia", "Tunisia", "Tunisia"), 
  LATITUDE = c(35.2122, 35.4343, 35.2122, 35.2122, 14.8252, 14.8252, 14.7414, 14.8252, 7.3028, 
               7.3028, 7.3028, 7.3028, 7.3588, 11.05, 11.05, 11.05, 11.05, 11.05, 11.05, 32.8487, 32.7149, 32.7149, 32.7149), 
  LONGITUDE = c(2.3189, 2.2166, 2.3189, 2.3189, -5.2547, -5.2547, -5.3282, -5.2547, 7.0382, 7.0382, 7.0382, 7.0382, 7.0994, 32.7, 32.7, 32.7, 32.7, 32.7, 32.7, 11.4309, 11.012, 11.012, 11.012)), 
  row.names = c(NA, -23L), 
  class = c("tbl_df", "tbl", "data.frame"), 
  .Names = c("EVENT_ID_CNTY", "COUNTRY", "LATITUDE", "LONGITUDE"))

# acled sf points object
acledsf <- st_as_sf(
  acled,
  coords = c('LATITUDE', 'LONGITUDE'),
  crs = 4326
)

# acled sp points object
coordinates(acled) <- ~LONGITUDE+LATITUDE
  proj4string(acled) <- proj4string(priosp)
acledsp <- acled; rm(acled)

sp package spatial join result. sp包空间连接结果。 I bound the polygons that intersect with every point, joined the result to the points, and then counted the number of CELL_IDs (polygons): 我绑定了与每个点相交的多边形，将结果连接到这些点，然后计算了CELL_ID（多边形）的数量： 

# sp spatial join:
addPolyDataToPts <- function (points, poly) {
  polysByPoint <- over(points, poly)
  points <- spCbind(points, polysByPoint)
}

acj <- addPolyDataToPts(acledsp, priosp)

(acled_count_sp <- acj@data %>% filter(!is.na(CELL_ID)) %>%
  group_by(CELL_ID, prio_country, POP) %>%
  summarize(acled_sp = n()) %>% arrange(CELL_ID) %>%
  rename(prio_country_sp = prio_country))
#> # A tibble: 5 x 4
#> # Groups:   CELL_ID, prio_country_sp [5]
#>   CELL_ID prio_country_sp     POP acled_sp
#>     <dbl> <chr>             <dbl>    <int>
#> 1 140055. Nigeria         527012.        5
#> 2 145866. South Sudan      23006.        6
#> 3 150830. Mali             12169.        4
#> 4 176783. Tunisia         107370.        4
#> 5 180365. Algeria         111984.        4

Analogous sf package spatial join result, where my count column acled_sf is different from the above acled_sp column for all but one polygon square. 类似的sf包空间连接结果，其中除一个多边形正方形外，我的计数列acled_sf与上述acled_sp列不同。 (140055; Nigeria): （140055；尼日利亚）： 

# sf spatial join:
(acled_count_sf <- 
  st_join(priosf, acledsf, join = st_covers) %>%
  group_by(CELL_ID, POP, prio_country) %>%
  summarize(acled_sf = n()) %>% ungroup %>% 
  arrange(CELL_ID) %>%
  rename(prio_country_sf = prio_country))
#> although coordinates are longitude/latitude, st_covers assumes that they are planar
#> Simple feature collection with 5 features and 4 fields
#> geometry type:  POLYGON
#> dimension:      XY
#> bbox:           xmin: -5.5 ymin: 7 xmax: 33 ymax: 35.5
#> epsg (SRID):    4326
#> proj4string:    +proj=longlat +datum=WGS84 +no_defs
#> # A tibble: 5 x 5
#>   CELL_ID     POP prio_country_sf acled_sf                        geometry
#>     <dbl>   <dbl> <chr>              <int>                   <POLYGON [°]>
#> 1 140055. 527012. Nigeria                5 ((7 7, 7 7.5, 7.5 7.5, 7.5 7, …
#> 2 145866.  23006. South Sudan            4 ((32.5 11, 32.5 11.5, 33 11.5,…
#> 3 150830.  12169. Mali                   1 ((-5.5 14.5, -5.5 15, -5 15, -…
#> 4 176783. 107370. Tunisia                6 ((11 32.5, 11 33, 11.5 33, 11.…
#> 5 180365. 111984. Algeria                1 ((2 35, 2 35.5, 2.5 35.5, 2.5 …

My running theory is that one method is binding values in an incorrect order but I'm not sure which. 我的理论是，一种方法以错误的顺序绑定值，但我不确定是哪种方法。 In my larger sample, I get similar values but bound to different polygons ie '2706' points get matched to Cell 1 for the sf join and to Cell 2 for the sp join. 在我的较大样本中，我得到了相似的值，但绑定到了不同的多边形，即“ 2706”点与sf连接的单元格1相匹配，而sp连接与单元格2的相匹配。

(And, in some cases some values are outright missing from the sf join) （而且，在某些情况下， sf连接中会完全缺少某些值）

Any insight into how or why my results differ in this way would be much appreciated. 任何对我的结果如何或为什么以这种方式有所不同的见解将不胜感激。

1 个解决方案

解决方案1
 5 已采纳  2018-06-29 05:32:44

So it took me plotting the data in mapview to figure out what was going on here, but at least in your given reprex, your issue is caused because you specified your longitude and latitude backwards when you created the acledsf object. 因此，我需要在mapview中绘制数据以弄清楚这里发生了什么，但是至少在给定的reprex中，您的问题是由于创建acledsf对象时向后指定了经度和纬度而引起的。 Created in the correct order and the join outputs match: 以正确的顺序创建，并且联接输出匹配： 

# acled sf points object
acledsf <- st_as_sf(
  acled,
  coords = c('LONGITUDE', 'LATITUDE'),  ###notice the correct order here
  crs = 4326
) 

# acled sp points object
coordinates(acled) <- c("LONGITUDE", "LATITUDE")
proj4string(acled) <- proj4string(priosp)
acledsp <- acled; rm(acled)


addPolyDataToPts <- function (points, poly) {
  polysByPoint <- over(points, poly)
  points <- spCbind(points, polysByPoint)
}

acj <- addPolyDataToPts(acledsp, priosp)

(acled_count_sp <- acj@data %>% filter(!is.na(CELL_ID)) %>%
    group_by(CELL_ID, prio_country, POP) %>%
    summarize(acled_sp = n()) %>% arrange(CELL_ID) %>%
    rename(prio_country_sp = prio_country))
#> # A tibble: 5 x 4
#> # Groups:   CELL_ID, prio_country_sp [5]
#>   CELL_ID prio_country_sp     POP acled_sp
#>     <dbl> <chr>             <dbl>    <int>
#> 1  140055 Nigeria         527012.        5
#> 2  145866 South Sudan      23006.        6
#> 3  150830 Mali             12169.        4
#> 4  176783 Tunisia         107370.        4
#> 5  180365 Algeria         111984.        4


### sf
(acled_count_sf <- 
    st_join(priosf, acledsf, join = st_covers) %>%
    group_by(CELL_ID, prio_country,  POP) %>%
    summarize(acled_sf = n()) %>% ungroup %>% 
    arrange(CELL_ID) %>%
    rename(prio_country_sf = prio_country))
#> although coordinates are longitude/latitude, st_covers assumes that they are planar
#> Simple feature collection with 5 features and 4 fields
#> geometry type:  POLYGON
#> dimension:      XY
#> bbox:           xmin: -5.5 ymin: 7 xmax: 33 ymax: 35.5
#> epsg (SRID):    4326
#> proj4string:    +proj=longlat +datum=WGS84 +no_defs
#> # A tibble: 5 x 5
#>   CELL_ID prio_country_sf     POP acled_sf                        geometry
#>     <dbl> <chr>             <dbl>    <int>                   <POLYGON [°]>
#> 1  140055 Nigeria         527012.        5 ((7 7, 7 7.5, 7.5 7.5, 7.5 7, …
#> 2  145866 South Sudan      23006.        6 ((32.5 11, 32.5 11.5, 33 11.5,…
#> 3  150830 Mali             12169.        4 ((-5.5 14.5, -5.5 15, -5 15, -…
#> 4  176783 Tunisia         107370.        4 ((11 32.5, 11 33, 11.5 33, 11.…
#> 5  180365 Algeria         111984.        4 ((2 35, 2 35.5, 2.5 35.5, 2.5 …

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