sf 数据框中的几何或坐标的空间等价于 "floor"

Question

我有几个记录了温度和盐度的位置的浮标数据。它在空间数据框中：

下面显示的数据均来自同一个浮标。

> head(CB_noaa_TS_2018_2021)
Simple feature collection with 6 features and 3 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: -76.4432 ymin: 38.97071 xmax: -76.44319 ymax: 38.97074
CRS:           NA
# A tibble: 6 × 4
  date_time           Temperature Salinity             geometry
  <dttm>                    <dbl>    <dbl>              <POINT>
1 2018-07-01 01:00:00        26.9     7.28  (-76.4432 38.97073)
2 2018-07-01 02:00:00        26.8     7.29 (-76.44319 38.97074)
3 2018-07-01 03:00:00        26.8     7.31  (-76.4432 38.97074)
4 2018-07-01 04:00:00        26.9     7.37  (-76.4432 38.97073)
5 2018-07-01 05:00:00        27.4     7.34  (-76.4432 38.97071)
6 2018-07-01 09:00:00        26.7     7.32  (-76.4432 38.97074)

这个数据集中有几个相距很多公里的浮标；然而，由于位置随时间（米）的轻微变化，坐标（来自 GPS）会波动，使其看起来像数百个独立的位置。我想将这些精确位置分组为每个浮标的大致位置。

是否有空间等效于 Lubridate 的“floor_date”，我可以在其中舍入坐标或以其他方式降低坐标的精度？

Answer 1

我认为您正在寻找 st_centroid，它可用于计算一组点的空间平均值。你可以这样使用它：

library(tidyverse)

CB_noaa_TS_2018_2021 %>%
  mutate(geometry = st_centroid(st_combine(geometry)))
#> Simple feature collection with 6 features and 3 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: -76.4432 ymin: 38.97073 xmax: -76.4432 ymax: 38.97073
#> CRS:           NA
#>             date_time Temperature Salinity                  geometry
#> 1 2018-07-01 01:00:00        26.9     7.28 POINT (-76.4432 38.97073)
#> 2 2018-07-01 02:00:00        26.8     7.29 POINT (-76.4432 38.97073)
#> 3 2018-07-01 03:00:00        26.8     7.31 POINT (-76.4432 38.97073)
#> 4 2018-07-01 04:00:00        26.9     7.37 POINT (-76.4432 38.97073)
#> 5 2018-07-01 05:00:00        27.4     7.34 POINT (-76.4432 38.97073)
#> 6 2018-07-01 09:00:00        26.7     7.32 POINT (-76.4432 38.97073)

您可以通过绘制黑色的原始点和红色的平均点来直观地看到它的作用：

CB_noaa_TS_2018_2021 %>%
  pluck(4) %>%
  plot()

CB_noaa_TS_2018_2021 %>%
  mutate(geometry = st_centroid(st_combine(geometry))) %>%
  pluck(4) %>%
  plot(col = "red", add = TRUE)

数据的可复制版本

library(sf)

CB_noaa_TS_2018_2021 <- st_sf(structure(list(date_time = structure(
  c(1530406800, 1530410400, 
1530414000, 1530417600, 1530421200, 1530435600), class = c("POSIXct", 
"POSIXt"), tzone = ""), Temperature = c(26.9, 26.8, 26.8, 26.9, 
27.4, 26.7), Salinity = c(7.28, 7.29, 7.31, 7.37, 7.34, 7.32), 
    geometry = structure(list(structure(c(-76.4432, 38.97073), class = c("XY", 
    "POINT", "sfg")), structure(c(-76.44319, 38.97074), class = c("XY", 
    "POINT", "sfg")), structure(c(-76.4432, 38.97074), class = c("XY", 
    "POINT", "sfg")), structure(c(-76.4432, 38.97073), class = c("XY", 
    "POINT", "sfg")), structure(c(-76.4432, 38.97071), class = c("XY", 
    "POINT", "sfg")), structure(c(-76.4432, 38.97074), class = c("XY", 
    "POINT", "sfg"))), class = c("sfc_POINT", "sfc"), precision = 0,
    bbox = structure(c(xmin = -76.4432, ymin = 38.97071, xmax = -76.44319, 
    ymax = 38.97074), class = "bbox"), crs = structure(list(input = 
    NA_character_, wkt = NA_character_), class = "crs"), n_empty = 0L)), 
    row.names = c("1", "2", "3", "4", "5", "6"), class = "data.frame"))

CB_noaa_TS_2018_2021
#> Simple feature collection with 6 features and 3 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: -76.4432 ymin: 38.97071 xmax: -76.44319 ymax: 38.97074
#> CRS:           NA
#>             date_time Temperature Salinity                   geometry
#> 1 2018-07-01 01:00:00        26.9     7.28  POINT (-76.4432 38.97073)
#> 2 2018-07-01 02:00:00        26.8     7.29 POINT (-76.44319 38.97074)
#> 3 2018-07-01 03:00:00        26.8     7.31  POINT (-76.4432 38.97074)
#> 4 2018-07-01 04:00:00        26.9     7.37  POINT (-76.4432 38.97073)
#> 5 2018-07-01 05:00:00        27.4     7.34  POINT (-76.4432 38.97071)
#> 6 2018-07-01 09:00:00        26.7     7.32  POINT (-76.4432 38.97074)

^{由 reprex package (v2.0.1)}

于 2022-05-09 创建

sf 数据框中的几何或坐标的空间等价于 "floor"

Spatial equivalent of "floor" for geometries or coordinates in sf dataframe

r

spatial

sf