使用 sf 将空间坐标集转换为 R 中的多边形 [英] Convert sets of spatial coordinates to polygons in R using sf

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问题描述

我列表中的每个元素都包含一组空间坐标，我想使用 sf 将其转换为多边形.每组坐标都按照我想连接点"的顺序排序，第一行和最后一行是相同的，以关闭多边形.每个列表元素都使用唯一标识符命名，我希望将其作为属性保留在 sf 输出中.

我在此处改编了与 sf 相关的答案中的代码:

(v0.2.0) 于 2018 年 10 月 5 日创建.

Each element of my list contains a set of spatial coordinates that I would like to convert to polygons using sf. Each set of coordinates is sorted in the order I would like to "connect the dots" and the first and last rows are identical, to close the polygons. Each list element is named with a unique identifier which I would like to retain as an attribute in the sf output.

I have adapted code from the sf-related answer here:

Convert sequence of longitude and latitude to polygon via sf in R

but my case differs in that I have multiple sets of coordinates (each of which should produce a separate polygon) whereas that question had a single set of coordinates (resulting in one polygon).

My specific question is how can I use sf to produce a sf polygon object containing multiple polygons in separate rows, each created with the coordinates in one of my list elements.

Thank you in advance for any suggestions or help.

Mark

My sample data, resulting from dput(), are at the end of this question and my code is:

points_df<-arrange(dat,SitePondGpsRep,DateTime_local) #sort on DateTime_local for proper sequence

points_df<-dplyr::select(points_df,SitePondGpsRep,Longitude,Latitude) #drop columns, for upcoming st_polygon call (requires numerics only)

points_ls<-split(points_df,points_df$SitePondGpsRep) #dataframe to list

points_ls<-lapply(points_ls, function(x) { x["SitePondGpsRep"] <- NULL; x }) #delete SitePondGpsRep column, it’s retained in list names

points_ls<-lapply(points_ls,function(x) {as.matrix(x)}) #convert to matrix for upcoming st_sf call

points_ls<-lapply(points_ls,function(x) {rbind(x,x[1,])}) #close poly, first and last point must be same

polys <- st_sf(st_sfc(st_polygon(points_ls)), crs = 4326) #create polys, but only one polygon is created when I expected three polygons

str(polys); glimpse(polys); plot(polys) #check output

Sample data:

dat <- structure(list(SitePondGpsRep = c("BURR-1-1-1", "BURR-1-1-1", 
"BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", 
"BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", 
"BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", 
"BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", 
"BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", 
"BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", 
"BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", 
"BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", 
"BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", 
"BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", 
"BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", 
"BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", 
"BURR-1-1-1", "BURR-1-1-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", 
"BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", 
"BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", 
"BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", 
"BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", 
"BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", 
"BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", 
"BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", 
"BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", 
"BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", 
"BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", 
"BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-4-1", "BURR-1-4-1", 
"BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", 
"BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", 
"BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", 
"BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", 
"BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", 
"BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", 
"BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", 
"BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", 
"BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", 
"BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", 
"BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1"
), DateTime_local = c("2018-05-30 10:49:04", "2018-05-30 10:49:05", 
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"2018-05-30 10:50:13", "2018-05-30 10:50:14", "2018-05-30 10:50:15", 
"2018-05-30 10:50:16"), Latitude = c(51.9851623569, 51.9851641171, 
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-105.0760967284, -105.0760972314)), class = "data.frame", row.names = c(NA, 
-177L))

解决方案

The easier way to approach this rather than using split and lapply is to use the ability of sf to work well with dplyr tools, especially grouped operations. We can:

  1. Create points for each vertex of the polygons with the coords argument of st_as_sf,
  2. group_by your id and summarise to combine the points into a MULTIPOINT for each polygon,
  3. Use st_cast to convert to POLYGON, drawing the lines between each vertex.

dat <- structure(list(SitePondGpsRep = c("BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-1-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-3-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1", "BURR-1-4-1"), DateTime_local = c("2018-05-30 10:49:04", "2018-05-30 10:49:05", "2018-05-30 10:49:06", "2018-05-30 10:49:07", "2018-05-30 10:49:08", "2018-05-30 10:49:09", "2018-05-30 10:49:10", "2018-05-30 10:49:27", "2018-05-30 10:49:28", "2018-05-30 10:49:29", "2018-05-30 10:49:30", "2018-05-30 10:49:31", "2018-05-30 10:49:32", "2018-05-30 10:49:33", "2018-05-30 10:49:34", "2018-05-30 10:49:35", "2018-05-30 10:49:36", "2018-05-30 10:49:37", "2018-05-30 10:49:38", "2018-05-30 10:49:39", "2018-05-30 10:49:40", "2018-05-30 10:49:41", "2018-05-30 10:49:42", "2018-05-30 10:49:43", "2018-05-30 10:49:44", "2018-05-30 10:49:45", "2018-05-30 10:49:46", "2018-05-30 10:49:47", "2018-05-30 10:49:48", "2018-05-30 10:49:49", "2018-05-30 10:49:50", "2018-05-30 10:49:51", "2018-05-30 10:49:52", "2018-05-30 10:49:54", "2018-05-30 10:49:55", "2018-05-30 10:49:56", "2018-05-30 10:49:57", "2018-05-30 10:49:58", "2018-05-30 10:50:01", "2018-05-30 10:50:02", "2018-05-30 10:50:03", "2018-05-30 10:50:04", "2018-05-30 10:50:05", "2018-05-30 10:50:06", "2018-05-30 10:50:07", "2018-05-30 10:50:09", "2018-05-30 10:50:10", "2018-05-30 10:50:11", "2018-05-30 10:50:12", "2018-05-30 10:50:13", "2018-05-30 10:50:14", "2018-05-30 10:50:15", "2018-05-30 10:50:16", "2018-05-30 10:50:17", "2018-05-30 10:50:18", "2018-05-30 10:50:20", "2018-05-30 10:50:24", "2018-05-30 10:50:27", "2018-05-30 10:50:36", "2018-05-30 10:50:41", "2018-05-30 10:50:42", "2018-05-30 10:50:43", "2018-05-30 10:50:44", "2018-05-30 10:50:45", "2018-05-30 10:49:05", "2018-05-30 10:49:06", "2018-05-30 10:49:07", "2018-05-30 10:49:08", "2018-05-30 10:49:09", "2018-05-30 10:49:10", "2018-05-30 10:49:11", "2018-05-30 10:49:12", "2018-05-30 10:49:13", "2018-05-30 10:49:19", "2018-05-30 10:49:31", "2018-05-30 10:49:32", "2018-05-30 10:49:33", "2018-05-30 10:49:34", "2018-05-30 10:49:35", "2018-05-30 10:49:36", "2018-05-30 10:49:37", "2018-05-30 10:49:38", "2018-05-30 10:49:39", "2018-05-30 10:49:40", "2018-05-30 10:49:41", "2018-05-30 10:49:42", "2018-05-30 10:49:43", "2018-05-30 10:49:44", "2018-05-30 10:49:45", "2018-05-30 10:49:46", "2018-05-30 10:49:47", "2018-05-30 10:49:48", "2018-05-30 10:49:49", "2018-05-30 10:49:50", "2018-05-30 10:49:51", "2018-05-30 10:49:52", "2018-05-30 10:49:53", "2018-05-30 10:49:54", "2018-05-30 10:49:55", "2018-05-30 10:49:56", "2018-05-30 10:49:57", "2018-05-30 10:49:58", "2018-05-30 10:49:59", "2018-05-30 10:50:00", "2018-05-30 10:50:01", "2018-05-30 10:50:02", "2018-05-30 10:50:03", "2018-05-30 10:50:04", "2018-05-30 10:50:05", "2018-05-30 10:50:06", "2018-05-30 10:50:07", "2018-05-30 10:50:10", "2018-05-30 10:50:11", "2018-05-30 10:50:12", "2018-05-30 10:50:13", "2018-05-30 10:50:14", "2018-05-30 10:50:15", "2018-05-30 10:50:16", "2018-05-30 10:50:37", "2018-05-30 10:50:44", "2018-05-30 10:49:05", "2018-05-30 10:49:06", "2018-05-30 10:49:07", "2018-05-30 10:49:08", "2018-05-30 10:49:09", "2018-05-30 10:49:10", "2018-05-30 10:49:11", "2018-05-30 10:49:12", "2018-05-30 10:49:19", "2018-05-30 10:49:21", "2018-05-30 10:49:22", "2018-05-30 10:49:26", "2018-05-30 10:49:27", "2018-05-30 10:49:30", "2018-05-30 10:49:31", "2018-05-30 10:49:32", "2018-05-30 10:49:33", "2018-05-30 10:49:34", "2018-05-30 10:49:35", "2018-05-30 10:49:36", "2018-05-30 10:49:37", "2018-05-30 10:49:38", "2018-05-30 10:49:39", "2018-05-30 10:49:40", "2018-05-30 10:49:41", "2018-05-30 10:49:42", "2018-05-30 10:49:43", "2018-05-30 10:49:44", "2018-05-30 10:49:45", "2018-05-30 10:49:46", "2018-05-30 10:49:47", "2018-05-30 10:49:48", "2018-05-30 10:49:49", "2018-05-30 10:49:50", "2018-05-30 10:49:51", "2018-05-30 10:49:52", "2018-05-30 10:49:54", "2018-05-30 10:49:57", "2018-05-30 10:49:58", "2018-05-30 10:49:59", "2018-05-30 10:50:00", "2018-05-30 10:50:01", "2018-05-30 10:50:02", "2018-05-30 10:50:03", "2018-05-30 10:50:04", "2018-05-30 10:50:05", "2018-05-30 10:50:06", "2018-05-30 10:50:07", "2018-05-30 10:50:08", "2018-05-30 10:50:09", "2018-05-30 10:50:10", "2018-05-30 10:50:11", "2018-05-30 10:50:12", "2018-05-30 10:50:13", "2018-05-30 10:50:14", "2018-05-30 10:50:15", "2018-05-30 10:50:16"), Latitude = c(51.9851623569, 51.9851641171, 51.9851674698, 51.9851741754, 51.9851825573, 51.9851923641, 51.9852027576, 51.9853603374, 51.985360086, 51.9853615109, 51.9853631873, 51.9853626005, 51.9853596669, 51.9853546377, 51.9853501953, 51.9853491057, 51.9853499439, 51.9853510335, 51.9853526261, 51.9853537157, 51.9853544701, 51.9853550568, 51.9853562303, 51.985358661, 51.9853618462, 51.985365618, 51.9853699766, 51.9853755087, 51.9853831362, 51.9853900932, 51.9853944518, 51.9853973016, 51.9854001515, 51.9854111318, 51.9854149874, 51.9854135625, 51.985412389, 51.9854097068, 51.9853739161, 51.9853589125, 51.9853450824, 51.9853315037, 51.9853169192, 51.9853025861, 51.9852880016, 51.985260509, 51.9852461759, 51.9852311723, 51.9852169231, 51.9852023385, 51.9851880893, 51.985174343, 51.9851596747, 51.9851456769, 51.9851331878, 51.9851182681, 51.9851253927, 51.9851476047, 51.9851814676, 51.9851861615, 51.9851861615, 51.9851835631, 51.9851810485, 51.985178953, 51.9851332717, 51.9851353671, 51.9851413183, 51.9851502031, 51.9851596747, 51.9851695653, 51.9851823058, 51.9851954654, 51.9852077868, 51.9852676336, 51.9853508659, 51.9853491057, 51.9853438251, 51.9853392988, 51.9853389636, 51.985339215, 51.9853398018, 51.9853422325, 51.9853455853, 51.9853481837, 51.9853506982, 51.9853537995, 51.9853565656, 51.9853595831, 51.9853626005, 51.9853671268, 51.9853722397, 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51.985419346, 51.985411467, 51.9853983074, 51.9853829686, 51.9853670429, 51.9853521232, 51.9853375386, 51.9853222836, 51.9853064418, 51.9852899294, 51.9852756802, 51.9852615986, 51.9852463435, 51.9852309208, 51.9852158334, 51.9852004945, 51.9851861615, 51.9851720799, 51.9851580821), Longitude = c(-105.0767748244, -105.0767996348, -105.0768228527, -105.0768438913, -105.0768627506, -105.0768831186, -105.0768996309, -105.0768738147, -105.0768491719, -105.0768251996, -105.0768006407, -105.0767758302, -105.0767515227, -105.0767283887, -105.0767055061, -105.0766806956, -105.0766552985, -105.0766305719, -105.0766069349, -105.0765823759, -105.0765584037, -105.0765349343, -105.0765112974, -105.076487828, -105.0764643587, -105.0764413923, -105.0764174201, -105.0763948727, -105.0763716549, -105.0763481855, -105.0763243809, -105.0763000734, -105.0762776099, -105.0762347784, -105.0762113091, -105.0761888456, -105.0761658791, -105.0761429127, -105.0761163421, -105.0761119835, -105.0761065353, 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-105.076188175, -105.0761637837, -105.0761408173, -105.0761313457, -105.0761326868, -105.076130759, -105.0761248916, -105.076120114, -105.0761155877, -105.0761135761, -105.0761135761, -105.0761101395, -105.0761072896, -105.0761061162, -105.0761060324, -105.0761067867, -105.0761083793, -105.0761087146, -105.0764583237, -105.0766065158, -105.0767834578, -105.0768087711, -105.0768324919, -105.0768561289, -105.0768786762, -105.0768972002, -105.0769114494, -105.0769231003, -105.0769605674, -105.0769288, -105.076918155, -105.0768983737, -105.0768753234, -105.076808352, -105.0767832901, -105.0767600723, -105.0767381117, -105.0767147262, -105.076689329, -105.0766637642, -105.0766387023, -105.0766154006, -105.0765913446, -105.0765661988, -105.0765423942, -105.0765190087, -105.0764952041, -105.0764719862, -105.076448014, -105.0764254667, -105.0764035899, -105.0763796177, -105.0763540529, -105.0763287395, -105.0763040129, -105.0762819685, -105.0762406457, -105.0761751831, -105.0761494506, -105.0761289988, -105.076121036, -105.0761198625, -105.0761174317, -105.0761130732, -105.076108044, -105.0761036016, -105.0761017576, -105.0761008356, -105.0760996621, -105.0760973152, -105.0760949682, -105.0760931242, -105.0760921184, -105.0760929566, -105.0760953873, -105.0760967284, -105.0760972314)), class = "data.frame", row.names = c(NA, -177L))
library(tidyverse)
library(sf)
dat %>%
  st_as_sf(coords = c("Longitude", "Latitude"), crs = 4326) %>%
  group_by(SitePondGpsRep) %>%
  summarise(geometry = st_combine(geometry)) %>%
  st_cast("POLYGON") %>%
  plot()

Created on 2018-10-05 by the reprex package (v0.2.0).

这篇关于使用 sf 将空间坐标集转换为 R 中的多边形的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！

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