合并多边形并求和它们的值 [英] Merging polygons and summing their values

问题描述

我有一个包含许多重叠多边形的数据框，我想将它们组合成一个形状，其值等于给定给每个单独多边形的值的总和.

I have a dataframe with many overlapping polygons that I would like to combine into a single shape with the value that equates to the sum of the values given to each infividual polygon.

一些示例数据:

df <- data.frame(x = c(0.5, 1.5, 4.5, 5.5),
         y = c(1, 1, 1, 1),
         id = c('a', 'b', 'c', 'd'),
         score = c(1, 3, 2, 4))

s_df <- SpatialPointsDataFrame(df[, c('x', 'y')], df[, 3:4]) %>%
  as('sf') %>%
  st_buffer(dist = 1)

plot(s_df)

我可以通过使用 sf 包中的 st_union 函数来获得这些多边形的并集，我认为下一步是在该多边形和原始多边形之间进行空间连接.但是，我不知道该怎么做.

I can get the union of these polygons by using the st_union function in the sf package, and I think the next step would be to do a spatial join between that and the original polygons. However, I cant figure out how to do it.

st_union 提供一个多面对象作为其输出，但 st_intersects 不适用于该对象类，而且我似乎也无法从多面对象创建 SpatialPolygonsDataframe.

st_union gives a multipolygon object as its output, but st_intersects doesn't work with that object class, and I can't seem to make a SpatialPolygonsDataframe from a multipolygon object either.

这是一个如此简单的任务，我觉得一定有一些我忽略或遗漏的基本功能

It is such a simple task I feel like there must be some basic function that I have either overlooked or missed

任何帮助将不胜感激

推荐答案

你考虑过不起眼的 dplyr::summarise() 吗?

Have you considered the humble dplyr::summarise()?

它将合并您的空间对象的几何图形 - 如果您设置分组变量，则可以合并为单个几何图形或多个几何图形 - 并且它可以进行任何聚合，例如在这个玩具示例中计算总分.

It will merge geometry of your spatial object - either to a single geometry, or multiple ones if you set a grouping variable - and it can do any aggregation, such as calculating total of score in this toy example.

一个可能的分组变量是多边形与其自身的交集——它似乎在这个例子中有效，我希望它能概括

A possible grouping variable is intersection of the polygons with themselves - it seems to work in this example, I hope it will generalise

library(sf)
library(sp)
library(dplyr)

df <- data.frame(x = c(0.5, 1.5, 4.5, 5.5),
                 y = c(1, 1, 1, 1),
                 id = c('a', 'b', 'c', 'd'),
                 score = c(1, 3, 2, 4))

s_df <- SpatialPointsDataFrame(df[, c('x', 'y')], df[, 3:4]) %>%
  as('sf') %>%
  st_buffer(dist = 1)

plot(s_df)

result <- s_df %>% 
  group_by(group = paste(st_intersects(s_df, s_df, sparse = T))) %>% 
  summarise(score = sum(score))

plot(result["score"])

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