rgeos::gBuffer缩水不失分
rgeos::gBuffer shrink without loss of points
我需要能够在不丢失点的情况下缩小 lat/lon 数据的多边形；更重要的是，我需要在正确的方向上有效地“消除”这些点。通常，gBuffer 工作正常，但不能保证点的数量和它们的相对间距。最终，每个点都有我需要保留的属性，并且 gBuffer 和多边形 growing/shrinking 的样条、平滑和其他“不错的效率”不允许我以足够的信心保留这些属性一对一映射。
示例：
library(rgeos) # gBuffer
dat <- structure(list(x = c(6, 5.98, 5.94, 5.86, 5.75, 5.62, 5.47, 5.31, 5.13, -4.87, -5.04, -5.22, -5.39, -5.55, -5.69, -5.81, -5.9, -5.96, -6, -6, -6, -5.96, -5.9, -5.81, -5.69, -5.55, -5.39, -5.22, -5.04, -3.04, -2.87, -2.69, -2.53, -2.38, -2.25, -2.14, -2.06, -2.02, -2, -2, -1.96, -1.9, -1.81, -1.69, -1.55, -1.39, -1.22, -1.04, -0.87, 1.13, 1.31, 1.47, 1.62, 1.75, 1.86, 1.94, 1.98, 2, 2, 2, 2.04, 2.1, 2.19, 2.31, 2.45, 2.61, 2.78, 2.96, 4.96, 5.13, 5.31, 5.47, 5.62, 5.75, 5.86, 5.94, 5.98, 6), y = c(5, 5.18, 5.35, 5.51, 5.66, 5.78, 5.88, 5.95, 5.99, 5.99, 6, 5.97, 5.92, 5.83, 5.72, 5.59, 5.43, 5.27, 5.09, -4.91, -5.09, -5.27, -5.43, -5.59, -5.72, -5.83, -5.92, -5.97, -6, -6, -5.99, -5.95, -5.88, -5.78, -5.66, -5.51, -5.35, -5.18, -5, -1.91, -1.73, -1.57, -1.41, -1.28, -1.17, -1.08, -1.03, -1, -1.01, -1.01, -1.05, -1.12, -1.22, -1.34, -1.49, -1.65, -1.82, -2, -4.91, -5.09, -5.27, -5.43, -5.59, -5.72, -5.83, -5.92, -5.97, -6, -6, -5.99, -5.95, -5.88, -5.78, -5.66, -5.51, -5.35, -5.18, 5)), row.names = c(NA, -78L), class = "data.frame")
# "shrink-wrap"
sp <- sp::SpatialPolygons(list(sp::Polygons(list(sp::Polygon( as.matrix(dat) )), "dat")))
sp2 <- gBuffer(sp, width = -0.5)
dat2 <- as.data.frame(sp2@polygons[[1]]@Polygons[[1]]@coords)
c(nrow(dat), nrow(dat2))
# [1] 78 97
我们立即看到点数的变化。我认识到大多数时候，这是 gBuffer 的理想特征，所以也许 rgeos 不是这种转变的最佳工具。
library(ggplot2) # just for vis here
ggplot(dat, aes(x, y)) +
geom_path() + geom_point() +
geom_path(data = dat2, color = "red") + geom_point(data = dat2, color = "red")
这张图片对我想要的整体造型有效果，但是增加了点数，也就是说我不能再依赖于原来点的1对1关系了。
一般来说，多边形是不对称的，很多多边形都有这样的裁剪，其中许多在特定方向“拉”点的方法会产生偏差或方向错误。
我在 gBuffer 中找不到任何选项，在 rgeos 中找不到其他函数可以保留点的数量和基本空间关系。我不需要“完美”收缩，如果它改变了事情，但它不应该显着偏离。
我不知道有什么方法可以使用包来完成您的要求，但我整理了一个我认为可能有用的小示例。这种方法确实假设以 (0, 0).
为中心
通用方法将您的笛卡尔点转换为极坐标，然后使用一些因子 REDUCTION_FACTOR，您可以缩放您的点与原点的距离。但是，对于定义多边形凹面部分的点，您会注意到变化很小。所以我所做的是添加一个因子，通过 REDUCTION_FACTOR。 CONVEX_SHIFT_CUTOFF 可以通过考虑给定几何体的点分布来设置。
我确实不得不稍微捏造一下倍数，但我认为如果你的几何不对称的规模不是太大，你可能会为你的数据集调整它。您可能还可以做一些事情，以类似的方式或 ifelse 条件来确定每个几何体的方向，以正确解释凹度的差异，但如果没有更多信息，很难说。
library(rgeos) # gBuffer
dat <- structure(list(x = c(6, 5.98, 5.94, 5.86, 5.75, 5.62, 5.47, 5.31, 5.13, -4.87, -5.04, -5.22, -5.39, -5.55, -5.69, -5.81, -5.9, -5.96, -6, -6, -6, -5.96, -5.9, -5.81, -5.69, -5.55, -5.39, -5.22, -5.04, -3.04, -2.87, -2.69, -2.53, -2.38, -2.25, -2.14, -2.06, -2.02, -2, -2, -1.96, -1.9, -1.81, -1.69, -1.55, -1.39, -1.22, -1.04, -0.87, 1.13, 1.31, 1.47, 1.62, 1.75, 1.86, 1.94, 1.98, 2, 2, 2, 2.04, 2.1, 2.19, 2.31, 2.45, 2.61, 2.78, 2.96, 4.96, 5.13, 5.31, 5.47, 5.62, 5.75, 5.86, 5.94, 5.98, 6), y = c(5, 5.18, 5.35, 5.51, 5.66, 5.78, 5.88, 5.95, 5.99, 5.99, 6, 5.97, 5.92, 5.83, 5.72, 5.59, 5.43, 5.27, 5.09, -4.91, -5.09, -5.27, -5.43, -5.59, -5.72, -5.83, -5.92, -5.97, -6, -6, -5.99, -5.95, -5.88, -5.78, -5.66, -5.51, -5.35, -5.18, -5, -1.91, -1.73, -1.57, -1.41, -1.28, -1.17, -1.08, -1.03, -1, -1.01, -1.01, -1.05, -1.12, -1.22, -1.34, -1.49, -1.65, -1.82, -2, -4.91, -5.09, -5.27, -5.43, -5.59, -5.72, -5.83, -5.92, -5.97, -6, -6, -5.99, -5.95, -5.88, -5.78, -5.66, -5.51, -5.35, -5.18, 5)), row.names = c(NA, -78L), class = "data.frame")
# "shrink-wrap"
sp <- sp::SpatialPolygons(list(sp::Polygons(list(sp::Polygon( as.matrix(dat) )), "dat")))
sp2 <- gBuffer(sp, width = -0.5)
dat2 <- as.data.frame(sp2@polygons[[1]]@Polygons[[1]]@coords)
REDUCTION_FACTOR <- 0.92
CONVEX_SHIFT_CUTOFF <- 0.55
x <- c()
y <- c()
max_x <- max(dat$x)
max_y <- max(dat$y)
for ( i in 1:length(dat$x)) {
x_p <- dat$x[i]
y_p <- dat$y[i]
r <- sqrt(x_p^2 + y_p^2)
theta <- atan2(y_p, x_p)
r <- REDUCTION_FACTOR * r
if(abs(x_p) < CONVEX_SHIFT_CUTOFF * max_x) {
x <- c(x, r*cos(theta)*(1 + 4*(1-REDUCTION_FACTOR)) )
} else {
x <- c(x, r*cos(theta))
}
if(abs(y_p) < CONVEX_SHIFT_CUTOFF*max_y ) {
y <- c(y, r*sin(theta)*(1 - 4*(1-REDUCTION_FACTOR) ))
} else {
y <- c(y, r*sin(theta))
}
}
dat3 <- data.frame(x = x, y = y)
library(ggplot2) # just for vis here
ggplot(dat, aes(x, y)) +
geom_path() + geom_point() +
geom_path(data = dat2, color = "red") + geom_point(data = dat2, color = "red") +
geom_path(data = dat3, color = "blue") + geom_point(data = dat3, color = "blue")
并且这种方法产生以下缩放结果。
如果您对当前缩小多边形的方式感到满意，这可能会奏效。它以此为基础获得从旧（大）点到新（小）多边形的 1:1 点映射。
library(rgeos) # gBuffer
library(sf)
library(tidyverse)
dat <- structure(list(x = c(6, 5.98, 5.94, 5.86, 5.75, 5.62, 5.47, 5.31, 5.13, -4.87, -5.04, -5.22, -5.39, -5.55, -5.69, -5.81, -5.9, -5.96, -6, -6, -6, -5.96, -5.9, -5.81, -5.69, -5.55, -5.39, -5.22, -5.04, -3.04, -2.87, -2.69, -2.53, -2.38, -2.25, -2.14, -2.06, -2.02, -2, -2, -1.96, -1.9, -1.81, -1.69, -1.55, -1.39, -1.22, -1.04, -0.87, 1.13, 1.31, 1.47, 1.62, 1.75, 1.86, 1.94, 1.98, 2, 2, 2, 2.04, 2.1, 2.19, 2.31, 2.45, 2.61, 2.78, 2.96, 4.96, 5.13, 5.31, 5.47, 5.62, 5.75, 5.86, 5.94, 5.98, 6), y = c(5, 5.18, 5.35, 5.51, 5.66, 5.78, 5.88, 5.95, 5.99, 5.99, 6, 5.97, 5.92, 5.83, 5.72, 5.59, 5.43, 5.27, 5.09, -4.91, -5.09, -5.27, -5.43, -5.59, -5.72, -5.83, -5.92, -5.97, -6, -6, -5.99, -5.95, -5.88, -5.78, -5.66, -5.51, -5.35, -5.18, -5, -1.91, -1.73, -1.57, -1.41, -1.28, -1.17, -1.08, -1.03, -1, -1.01, -1.01, -1.05, -1.12, -1.22, -1.34, -1.49, -1.65, -1.82, -2, -4.91, -5.09, -5.27, -5.43, -5.59, -5.72, -5.83, -5.92, -5.97, -6, -6, -5.99, -5.95, -5.88, -5.78, -5.66, -5.51, -5.35, -5.18, 5)), row.names = c(NA, -78L), class = "data.frame")
# "shrink-wrap"
sp <- sp::SpatialPolygons(list(sp::Polygons(list(sp::Polygon( as.matrix(dat) )), "dat")))
sp2 <- gBuffer(sp, width = -0.5)
dat2 <- as.data.frame(sp2@polygons[[1]]@Polygons[[1]]@coords)
## New methods begin here
# change objects to type `sf`
sp_sf <- st_as_sf(sp)
sp2_sf <- st_as_sf(sp2)
dat_sf <- dat %>% st_as_sf(coords = c('x', 'y'))
dat2_sf <- dat2 %>% st_as_sf(coords = c('x', 'y'))
# The plot so far, saved for building on further down
p <- ggplot() +
geom_sf(data = sp_sf, color = 'blue', fill = NA) +
geom_sf(data = dat_sf, color = 'blue') +
geom_sf(data = sp2_sf, color = 'red', fill = NA) +
geom_sf(data = dat2_sf, color = 'red')
# Using st_nearest_points original points to new small polygon
# results in (perpendicular?) lines from old points to new small polygon
near_lines <- st_nearest_points(dat_sf, sp2_sf)
#plotted together:
p + geom_sf(data = near_lines, color = 'black')
## Zooming in on a problem area
p + geom_sf(data = near_lines, color = 'black') +
coord_sf(xlim = c(-3, 0), ylim = c(-2,0))
# Get only 1:1 points for shrunken polygon
# a small buffer had to be added, as some points were not showing up
# you may need to adjust the buffer, depending on your data & projection
new_points <- st_intersection(st_buffer(near_lines, .001), sp2_sf)
# All together now:
p + geom_sf(data = near_lines, color = 'black') +
geom_sf(data = new_points, color = 'green', size = 4) +
coord_sf(xlim = c(-3, 0), ylim = c(-2,0))
由 reprex package (v0.3.0)
于 2020-12-20 创建
我需要能够在不丢失点的情况下缩小 lat/lon 数据的多边形；更重要的是，我需要在正确的方向上有效地“消除”这些点。通常，gBuffer 工作正常，但不能保证点的数量和它们的相对间距。最终，每个点都有我需要保留的属性，并且 gBuffer 和多边形 growing/shrinking 的样条、平滑和其他“不错的效率”不允许我以足够的信心保留这些属性一对一映射。
示例：
library(rgeos) # gBuffer
dat <- structure(list(x = c(6, 5.98, 5.94, 5.86, 5.75, 5.62, 5.47, 5.31, 5.13, -4.87, -5.04, -5.22, -5.39, -5.55, -5.69, -5.81, -5.9, -5.96, -6, -6, -6, -5.96, -5.9, -5.81, -5.69, -5.55, -5.39, -5.22, -5.04, -3.04, -2.87, -2.69, -2.53, -2.38, -2.25, -2.14, -2.06, -2.02, -2, -2, -1.96, -1.9, -1.81, -1.69, -1.55, -1.39, -1.22, -1.04, -0.87, 1.13, 1.31, 1.47, 1.62, 1.75, 1.86, 1.94, 1.98, 2, 2, 2, 2.04, 2.1, 2.19, 2.31, 2.45, 2.61, 2.78, 2.96, 4.96, 5.13, 5.31, 5.47, 5.62, 5.75, 5.86, 5.94, 5.98, 6), y = c(5, 5.18, 5.35, 5.51, 5.66, 5.78, 5.88, 5.95, 5.99, 5.99, 6, 5.97, 5.92, 5.83, 5.72, 5.59, 5.43, 5.27, 5.09, -4.91, -5.09, -5.27, -5.43, -5.59, -5.72, -5.83, -5.92, -5.97, -6, -6, -5.99, -5.95, -5.88, -5.78, -5.66, -5.51, -5.35, -5.18, -5, -1.91, -1.73, -1.57, -1.41, -1.28, -1.17, -1.08, -1.03, -1, -1.01, -1.01, -1.05, -1.12, -1.22, -1.34, -1.49, -1.65, -1.82, -2, -4.91, -5.09, -5.27, -5.43, -5.59, -5.72, -5.83, -5.92, -5.97, -6, -6, -5.99, -5.95, -5.88, -5.78, -5.66, -5.51, -5.35, -5.18, 5)), row.names = c(NA, -78L), class = "data.frame")
# "shrink-wrap"
sp <- sp::SpatialPolygons(list(sp::Polygons(list(sp::Polygon( as.matrix(dat) )), "dat")))
sp2 <- gBuffer(sp, width = -0.5)
dat2 <- as.data.frame(sp2@polygons[[1]]@Polygons[[1]]@coords)
c(nrow(dat), nrow(dat2))
# [1] 78 97
我们立即看到点数的变化。我认识到大多数时候，这是 gBuffer 的理想特征，所以也许 rgeos 不是这种转变的最佳工具。
library(ggplot2) # just for vis here
ggplot(dat, aes(x, y)) +
geom_path() + geom_point() +
geom_path(data = dat2, color = "red") + geom_point(data = dat2, color = "red")
这张图片对我想要的整体造型有效果，但是增加了点数，也就是说我不能再依赖于原来点的1对1关系了。
一般来说，多边形是不对称的，很多多边形都有这样的裁剪，其中许多在特定方向“拉”点的方法会产生偏差或方向错误。
我在 gBuffer 中找不到任何选项，在 rgeos 中找不到其他函数可以保留点的数量和基本空间关系。我不需要“完美”收缩，如果它改变了事情，但它不应该显着偏离。
我不知道有什么方法可以使用包来完成您的要求，但我整理了一个我认为可能有用的小示例。这种方法确实假设以 (0, 0).
通用方法将您的笛卡尔点转换为极坐标，然后使用一些因子 REDUCTION_FACTOR，您可以缩放您的点与原点的距离。但是，对于定义多边形凹面部分的点，您会注意到变化很小。所以我所做的是添加一个因子，通过 REDUCTION_FACTOR。 CONVEX_SHIFT_CUTOFF 可以通过考虑给定几何体的点分布来设置。
我确实不得不稍微捏造一下倍数，但我认为如果你的几何不对称的规模不是太大，你可能会为你的数据集调整它。您可能还可以做一些事情，以类似的方式或 ifelse 条件来确定每个几何体的方向，以正确解释凹度的差异，但如果没有更多信息，很难说。
library(rgeos) # gBuffer
dat <- structure(list(x = c(6, 5.98, 5.94, 5.86, 5.75, 5.62, 5.47, 5.31, 5.13, -4.87, -5.04, -5.22, -5.39, -5.55, -5.69, -5.81, -5.9, -5.96, -6, -6, -6, -5.96, -5.9, -5.81, -5.69, -5.55, -5.39, -5.22, -5.04, -3.04, -2.87, -2.69, -2.53, -2.38, -2.25, -2.14, -2.06, -2.02, -2, -2, -1.96, -1.9, -1.81, -1.69, -1.55, -1.39, -1.22, -1.04, -0.87, 1.13, 1.31, 1.47, 1.62, 1.75, 1.86, 1.94, 1.98, 2, 2, 2, 2.04, 2.1, 2.19, 2.31, 2.45, 2.61, 2.78, 2.96, 4.96, 5.13, 5.31, 5.47, 5.62, 5.75, 5.86, 5.94, 5.98, 6), y = c(5, 5.18, 5.35, 5.51, 5.66, 5.78, 5.88, 5.95, 5.99, 5.99, 6, 5.97, 5.92, 5.83, 5.72, 5.59, 5.43, 5.27, 5.09, -4.91, -5.09, -5.27, -5.43, -5.59, -5.72, -5.83, -5.92, -5.97, -6, -6, -5.99, -5.95, -5.88, -5.78, -5.66, -5.51, -5.35, -5.18, -5, -1.91, -1.73, -1.57, -1.41, -1.28, -1.17, -1.08, -1.03, -1, -1.01, -1.01, -1.05, -1.12, -1.22, -1.34, -1.49, -1.65, -1.82, -2, -4.91, -5.09, -5.27, -5.43, -5.59, -5.72, -5.83, -5.92, -5.97, -6, -6, -5.99, -5.95, -5.88, -5.78, -5.66, -5.51, -5.35, -5.18, 5)), row.names = c(NA, -78L), class = "data.frame")
# "shrink-wrap"
sp <- sp::SpatialPolygons(list(sp::Polygons(list(sp::Polygon( as.matrix(dat) )), "dat")))
sp2 <- gBuffer(sp, width = -0.5)
dat2 <- as.data.frame(sp2@polygons[[1]]@Polygons[[1]]@coords)
REDUCTION_FACTOR <- 0.92
CONVEX_SHIFT_CUTOFF <- 0.55
x <- c()
y <- c()
max_x <- max(dat$x)
max_y <- max(dat$y)
for ( i in 1:length(dat$x)) {
x_p <- dat$x[i]
y_p <- dat$y[i]
r <- sqrt(x_p^2 + y_p^2)
theta <- atan2(y_p, x_p)
r <- REDUCTION_FACTOR * r
if(abs(x_p) < CONVEX_SHIFT_CUTOFF * max_x) {
x <- c(x, r*cos(theta)*(1 + 4*(1-REDUCTION_FACTOR)) )
} else {
x <- c(x, r*cos(theta))
}
if(abs(y_p) < CONVEX_SHIFT_CUTOFF*max_y ) {
y <- c(y, r*sin(theta)*(1 - 4*(1-REDUCTION_FACTOR) ))
} else {
y <- c(y, r*sin(theta))
}
}
dat3 <- data.frame(x = x, y = y)
library(ggplot2) # just for vis here
ggplot(dat, aes(x, y)) +
geom_path() + geom_point() +
geom_path(data = dat2, color = "red") + geom_point(data = dat2, color = "red") +
geom_path(data = dat3, color = "blue") + geom_point(data = dat3, color = "blue")
并且这种方法产生以下缩放结果。
如果您对当前缩小多边形的方式感到满意，这可能会奏效。它以此为基础获得从旧（大）点到新（小）多边形的 1:1 点映射。
library(rgeos) # gBuffer
library(sf)
library(tidyverse)
dat <- structure(list(x = c(6, 5.98, 5.94, 5.86, 5.75, 5.62, 5.47, 5.31, 5.13, -4.87, -5.04, -5.22, -5.39, -5.55, -5.69, -5.81, -5.9, -5.96, -6, -6, -6, -5.96, -5.9, -5.81, -5.69, -5.55, -5.39, -5.22, -5.04, -3.04, -2.87, -2.69, -2.53, -2.38, -2.25, -2.14, -2.06, -2.02, -2, -2, -1.96, -1.9, -1.81, -1.69, -1.55, -1.39, -1.22, -1.04, -0.87, 1.13, 1.31, 1.47, 1.62, 1.75, 1.86, 1.94, 1.98, 2, 2, 2, 2.04, 2.1, 2.19, 2.31, 2.45, 2.61, 2.78, 2.96, 4.96, 5.13, 5.31, 5.47, 5.62, 5.75, 5.86, 5.94, 5.98, 6), y = c(5, 5.18, 5.35, 5.51, 5.66, 5.78, 5.88, 5.95, 5.99, 5.99, 6, 5.97, 5.92, 5.83, 5.72, 5.59, 5.43, 5.27, 5.09, -4.91, -5.09, -5.27, -5.43, -5.59, -5.72, -5.83, -5.92, -5.97, -6, -6, -5.99, -5.95, -5.88, -5.78, -5.66, -5.51, -5.35, -5.18, -5, -1.91, -1.73, -1.57, -1.41, -1.28, -1.17, -1.08, -1.03, -1, -1.01, -1.01, -1.05, -1.12, -1.22, -1.34, -1.49, -1.65, -1.82, -2, -4.91, -5.09, -5.27, -5.43, -5.59, -5.72, -5.83, -5.92, -5.97, -6, -6, -5.99, -5.95, -5.88, -5.78, -5.66, -5.51, -5.35, -5.18, 5)), row.names = c(NA, -78L), class = "data.frame")
# "shrink-wrap"
sp <- sp::SpatialPolygons(list(sp::Polygons(list(sp::Polygon( as.matrix(dat) )), "dat")))
sp2 <- gBuffer(sp, width = -0.5)
dat2 <- as.data.frame(sp2@polygons[[1]]@Polygons[[1]]@coords)
## New methods begin here
# change objects to type `sf`
sp_sf <- st_as_sf(sp)
sp2_sf <- st_as_sf(sp2)
dat_sf <- dat %>% st_as_sf(coords = c('x', 'y'))
dat2_sf <- dat2 %>% st_as_sf(coords = c('x', 'y'))
# The plot so far, saved for building on further down
p <- ggplot() +
geom_sf(data = sp_sf, color = 'blue', fill = NA) +
geom_sf(data = dat_sf, color = 'blue') +
geom_sf(data = sp2_sf, color = 'red', fill = NA) +
geom_sf(data = dat2_sf, color = 'red')
# Using st_nearest_points original points to new small polygon
# results in (perpendicular?) lines from old points to new small polygon
near_lines <- st_nearest_points(dat_sf, sp2_sf)
#plotted together:
p + geom_sf(data = near_lines, color = 'black')
## Zooming in on a problem area
p + geom_sf(data = near_lines, color = 'black') +
coord_sf(xlim = c(-3, 0), ylim = c(-2,0))
# Get only 1:1 points for shrunken polygon
# a small buffer had to be added, as some points were not showing up
# you may need to adjust the buffer, depending on your data & projection
new_points <- st_intersection(st_buffer(near_lines, .001), sp2_sf)
# All together now:
p + geom_sf(data = near_lines, color = 'black') +
geom_sf(data = new_points, color = 'green', size = 4) +
coord_sf(xlim = c(-3, 0), ylim = c(-2,0))
由 reprex package (v0.3.0)
于 2020-12-20 创建