如何在bbox中获取每个多边形的polys？
pp <- cbind(coordinates(polys),as.data.frame(polys))
仅给我lonlat但我想为每个多边形获得lat1lat2和lon1lon2。
polys=new("SpatialPolygonsDataFrame"
, data = structure(list(NAMRB_EN = structure(c(6L, 45L, 2L, 41L, 31L,
3L, 40L, 14L, 42L, 7L, 26L, 12L, 38L, 25L, 36L, 9L, 39L, 27L,
32L, 19L, 43L, 21L, 15L, 22L, 20L, 9L, 17L, 11L, 33L, 44L, 37L,
13L, 8L, 5L, 18L, 30L, 16L, 10L, 1L, 29L, 34L, 23L, 24L, 28L,
4L, 35L), .Label = c("Albany", "Arctic Ocean Seaboard", "Arnaud",
"Atlantic Ocean Seaboard", "Attawapiskat", "Back", "Baleine",
"Broadback", "Churchill", "Columbia", "Eastmain", "Feuilles",
"Fraser", "George", "Grande Baleine", "Harricanaw", "Hayes",
"Hudson Bay Seaboard", "Koksoak", "La Grande", "Little Mecatina",
"Mackenzie", "Mississippi", "Moose", "Naskaupi", "Nass", "Natashquan",
"Nelson", "Nottaway", "Pacific Ocean Seaboard", "Povungnituk",
"Romaine", "Rupert", "Saint John", "Saint Lawrence", "Seal",
"Severn", "Skeena", "St.-Augustin", "Stikine", "Taku", "Thelon",
"Wannock", "Winisk", "Yukon"), class = "factor"), NAODA_EN = structure(c(1L,
5L, 1L, 5L, 4L, 4L, 5L, 4L, 4L, 4L, 5L, 4L, 5L, 2L, 4L, 2L, 2L,
2L, 2L, 4L, 5L, 2L, 4L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 4L,
4L, 4L, 5L, 4L, 5L, 4L, 4L, 2L, 3L, 4L, 4L, 2L, 2L), .Label = c("Arctic Ocean",
"Atlantic Ocean", "Gulf of Mexico", "Hudson Bay", "Pacific Ocean"
), class = "factor"), NAMRB_FR = structure(c(4L, 45L, 19L, 41L,
31L, 2L, 40L, 12L, 42L, 5L, 27L, 10L, 38L, 26L, 36L, 7L, 39L,
28L, 32L, 16L, 43L, 18L, 13L, 23L, 17L, 7L, 15L, 9L, 33L, 44L,
37L, 11L, 6L, 3L, 22L, 21L, 14L, 8L, 1L, 30L, 34L, 24L, 25L,
29L, 20L, 35L), .Label = c("Albany", "Arnaud", "Attawapiskat",
"Back", "Baleine", "Broadback", "Churchill", "Columbia", "Eastmain",
"Feuilles", "Fraser", "George", "Grande Baleine", "Harricanaw",
"Hayes", "Koksoak", "La Grande", "Little Mecatina", "Littoral de l'océan Arctique",
"Littoral de l'océan Atlantique", "Littoral de l'océan Pacifique",
"Littoral de la Baie d'Hudson", "Mackenzie", "Mississippi", "Moose",
"Naskaupi", "Nass", "Natashquan", "Nelson", "Nottaway", "Povungnituk",
"Romaine", "Rupert", "Saint-Jean", "Saint-Laurent", "Seal", "Severn",
"Skeena", "St.-Augustin", "Stikine", "Taku", "Thelon", "Wannock",
"Winisk", "Yukon"), class = "factor"), NAODA_FR = structure(c(3L,
5L, 3L, 5L, 1L, 1L, 5L, 1L, 1L, 1L, 5L, 1L, 5L, 4L, 1L, 4L, 4L,
4L, 4L, 1L, 5L, 4L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 5L, 1L,
1L, 1L, 5L, 1L, 5L, 1L, 1L, 4L, 2L, 1L, 1L, 4L, 4L), .Label = c("Baie d'Hudson",
"Golfe de Mexique", "Océan Arctique", "Océan Atlantique", "Océan Pacifique"
), class = "factor")), .Names = c("NAMRB_EN", "NAODA_EN", "NAMRB_FR",
"NAODA_FR"), row.names = 0:45, class = "data.frame")
, polygons = list(<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>,
<S4 object of class structure("Polygons", package = "sp")>)
, plotOrder = c(3L, 24L, 35L, 46L, 44L, 2L, 42L, 38L, 45L, 36L, 26L, 9L, 32L,
1L, 20L, 39L, 27L, 31L, 43L, 25L, 16L, 7L, 30L, 40L, 6L, 15L,
34L, 13L, 12L, 41L, 28L, 8L, 23L, 29L, 5L, 10L, 37L, 11L, 14L,
33L, 4L, 22L, 18L, 19L, 17L, 21L)
, bbox = structure(c(-152.812332679775, 40.3769750107632, -52.6362915039062,
83.1106262207029), .Dim = c(2L, 2L), .Dimnames = list(c("x",
"y"), c("min", "max")))
, proj4string = new("CRS"
, projargs = "+proj=longlat +datum=NAD83 +no_defs +ellps=GRS80 +towgs84=0,0,0"
)
)
答案 0 :(得分：3)
空间多边形数据框有几个插槽。 ul, li a:hover {
font-size: 30px;
color: red;
}
是数据框，@data是多边形。您可以先试用@polygons来查看所获得的内容。如果它是多边形列表，则str(polys@polygons)具有lapply函数
sp::bbox答案 1 :(得分：3)
以前的答案很好，不幸的是，对于我来说，对于大型数据集（数百万个多边形）而言，它太慢了。
使用Rcpp可以加快速度（> 20次），以后的函数是从spatDataManagement包中提取的，你可以将它放在cpp文件中并在其上运行sourceCpp获取该功能或您可以安装spatDataManagement并直接使用该功能。此外，它很好地将所有内容放在具有足够列名称的data.frame中：
> head(GetBBoxes(phillyCensusTracts))
minX minY maxX maxY
1 -74.97678 40.07721 -74.95576 40.09781
2 -75.00539 40.09937 -74.96408 40.12390
3 -75.14641 39.92889 -75.13040 39.96294
4 -75.00942 40.05475 -74.99043 40.06916
5 -75.03647 40.05078 -75.02171 40.06731
6 -74.98463 40.07198 -74.97151 40.09381
代码：
#include <Rcpp.h>
#include <string>
using namespace Rcpp;
//' Get bounding box for each polygon/polyline
//' @description Gets the bounding box (range of x and y) for each item of a SpatialLines/Polygons (DataFrame or not) object.
//' It is equivalent to applying the \code{sp::bbox} function to all polygons with lapply but it is
//' simpler to use and much faster (even on a toy example of a few hundred polygons but designed for datasets with millions, then easily gets > 20x faster).
//' It is an important function to speed up other more complex comparisons between sp objects such as over().
//' @param x A SpatialPolygons[DataFrame] or SpatialLines[DataFrame]
//' @return A matrix of same number of columns than x and 4 columns : xmin, ymin, xmax, ymax
//' @export
//' @examples
//' data(phillyCensusTract)
//' ## simple use
//' system.time(bboxes <- GetBBoxes(phillyCensusTracts))
//' #=> 0.002 seconds
//' ## same using bbox
//' system.time(bboxesRef <- matrix(unlist(lapply(phillyCensusTracts@polygons,bbox)),ncol=4,byrow=TRUE))
//' #=> 0.021 seconds
// [[Rcpp::export]]
SEXP GetBBoxes( SEXP x ){
// determines object type and adapts the search of coordinates
S4 obj(x) ;
std::string nameList;
std::string nameSubList;
if(Rf_inherits(x, "SpatialLines") || Rf_inherits(x, "SpatialLinesDataFrame")){
nameList = "lines";
nameSubList = "Lines";
}else if(Rf_inherits(x, "SpatialPolygons") || Rf_inherits(x, "SpatialPolygonsDataFrame")){
nameList = "polygons";
nameSubList = "Polygons";
}else{
::Rf_error("In GetBBoxes, class must be Spatial[Polygons|Lines][DataFrame]");
}
List a = obj.slot(nameList);
// count items
int nPol = a.length();
NumericMatrix bboxes(nPol,4);
// get the range
for(int iPol = 0;iPol < nPol;iPol++){
S4 pol = a(iPol);
List b = pol.slot(nameSubList);
double minX = std::numeric_limits<double>::infinity();
double maxX = -std::numeric_limits<double>::infinity();
double minY = std::numeric_limits<double>::infinity();
double maxY = -std::numeric_limits<double>::infinity();
for(int iSP = 0; iSP < b.length(); iSP++){
S4 subPol = b(iSP);
NumericMatrix coords = subPol.slot("coords");
// X
NumericVector rangeX = range(coords(_,0));
if(rangeX(0)<minX) minX = rangeX(0);
if(rangeX(1)>maxX) maxX = rangeX(1);
// Y
NumericVector rangeY = range(coords(_,1));
if(rangeY(0)<minY) minY = rangeY(0);
if(rangeY(1)>maxY) maxY = rangeY(1);
}
bboxes(iPol,0) = minX;
bboxes(iPol,1) = minY;
bboxes(iPol,2) = maxX;
bboxes(iPol,3) = maxY;
}
Rcpp::DataFrame BBoxes = Rcpp::DataFrame::create(Rcpp::Named("minX")=bboxes(_,0),
Rcpp::Named("minY")=bboxes(_,1),
Rcpp::Named("maxX")=bboxes(_,2),
Rcpp::Named("maxY")=bboxes(_,3));
return BBoxes;
}
答案 2 :(得分：1)
返回plottable限定框（以SpatialPolygonsDataFrame的形式），而不是lapply(polys@polygons, bbox)返回的矩阵列表：
spatial_bboxes <- function(polygons) {
individual_bb <- function(polygon, projection) {
polygon <- sp::SpatialPolygons(list(polygon), proj4string = projection)
spatial_bbox <- as(raster::extent(polygon), "SpatialPolygons")
spatial_bbox <- spatial_bbox@polygons[[1]]
spatial_bbox@ID <- polygon@polygons[[1]]@ID
return(spatial_bbox)
}
polys <- lapply(polygons@polygons, individual_bb, polygons@proj4string)
spatial_polys <- sp::SpatialPolygons(polys, proj4string = polygons@proj4string)
spatial_polys_df <- sp::SpatialPolygonsDataFrame(spatial_polys, polygons@data)
return(spatial_polys_df)
}