Geographic Information Systems Asked on November 17, 2020
When joining attributes by location with the over function in R from the sp package, only the values for one of the polygons that intersects is retained. Is there a way in R that allows to take attributes of more than located feature? That is, to get ALL qualitative values the polygons that intersect/overlap. With an attribute column for the value of the first located feature, another for the second located feature, etc.
I have also asked in a separate question about Joining attributes by location for more than one located feature using QGIS
Sample code to perform simple spatial joins
#set up sample data
install.packages('tmap')#use this library to upload sample spatial polygons
library(tmap)
data(Europe) # countries
data(metro) # cities
#transform sample data of points into polygons with same crs/projection for this example (trivial example because point.in.poly suited to this problem but just for the sake of an example
metro.pts=spTransform(metro, crs(Europe))
metro.poly=gBuffer(metro.pts, width=50000, byid=TRUE)
#load libraries for spatial join
library(rgdal)
library(sp)
#spatial join of the two polygon shapefiles
#"find the cities in each country"
joined_one=cbind(over(Europe, metro.poly), as.data.frame(Europe))
#but this only joins one of the cities in each country even when more than one city is present
#if we check for spain, we only have Barcelona and not Madrid even though it is part of the city file
na.omit(joined_first[joined_first$iso_a3=="ESP",])
metro.poly[metro.poly$name=="Madrid"|metro.poly$name=="Barcelona",]
#using the `fn` option (function) we can calculate arithmetic for quantitative variables as to get values for more than one polygon that intersects
joined_qn=cbind(over(Europe, metro.poly[,5:8], fn=mean), as.data.frame(Europe))
I recommend you consider transitioning from sp to the sf package (this handy guide will help you along the way).
Here's an approach that uses sf::st_join() to achieve the spatial join you're looking for:
library(tmap)
library(sf)
library(tidyverse)
data(Europe)
data(metro)
europe_sf <- st_as_sf(Europe)
metro_pts_sf <- metro %>%
st_as_sf() %>%
st_transform(st_crs(europe_sf))
metro_poly_sf <- st_buffer(metro_pts_sf, dist = 5e4)
joined_sf <- st_join(europe_sf, metro_poly_sf)
Let's check the two Spanish cities you mention in the question:
joined_sf %>%
filter(name.x %in% "Spain") %>%
select(matches("name"), everything())
## Simple feature collection with 2 features and 28 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 2766833 ymin: 1584304 xmax: 3833320 ymax: 2463100
## epsg (SRID): NA
## proj4string: +proj=laea +lat_0=52 +lon_0=10 +x_0=4321000 +y_0=3210000 +ellps=GRS80 +units=m +no_defs
## name.x name.y name_long iso_a3.x sovereignt continent part
## 1 Spain Barcelona Barcelona ESP Spain Europe Southern Europe
## 2 Spain Madrid Madrid ESP Spain Europe Southern Europe
## EU_Schengen area pop_est pop_est_dens gdp_md_est gdp_cap_est
## 1 EU Schengen 498800 40525002 81.24499 1403000 34620.6
## 2 EU Schengen 498800 40525002 81.24499 1403000 34620.6
## economy income_grp life_exp well_being
## 1 2. Developed region: nonG7 1. High income: OECD 81.4 6.188263
## 2 2. Developed region: nonG7 1. High income: OECD 81.4 6.188263
## HPI iso_a3.y pop1950 pop1960 pop1970 pop1980 pop1990 pop2000
## 1 44.06279 ESP 1809390 2467926 3482047 3836761 4100808 4355425
## 2 44.06279 ESP 1699752 2391543 3520861 4253190 4413870 5014411
## pop2010 pop2020 pop2030 geometry
## 1 4933548 5478238 5685152 MULTIPOLYGON (((3830463 187...
## 2 5787392 6476334 6707421 MULTIPOLYGON (((3830463 187...
Correct answer by Tiernan on November 17, 2020
You can use raster::union
Example data:
library(tmap)
library(rgeos)
library(rgdal)
library(raster)
data(Europe)
# simplifying a bit
Eu <- Europe[Europe$iso_a3 %in% c('ESP', 'PRT', 'FRA'),1:2]
data(metro)
metro.pts <- spTransform(metro[, 'name'], crs(Europe))
metro.poly <- gBuffer(metro.pts, width=50000, byid=TRUE)
Original solution
u <- union(Eu, metro.poly)
or
i <- intersect(Eu, metro.poly)
**Update, now I understand the question better **
You can returnList = TRUE in over:
v <- over(Eu, metro.poly, returnList=TRUE)
You could now do:
a <- lapply(1:length(v), function(i) cbind(iso_a3=Eu$iso_a3[i], v[[i]]))
b <- do.call(rbind, a)
m <- merge(Eu, b, by="iso_a3", all.x=TRUE, duplicateGeoms=TRUE)
In merge, the argument duplicateGeoms=TRUE is necessary because otherwise merge cannot handle having more than one (i.e. duplicate) value to assign to each feature.
To get the following:
data.frame(m)
# iso_a3 name.x name.y
#1 ESP Spain Barcelona
#2 ESP Spain Madrid
#3 FRA France Lille
#4 FRA France Lyon
#5 FRA France Marseille
#6 FRA France Paris
#7 PRT Portugal Lisbon
#8 PRT Portugal Porto
I think that this is the same as what Tiernan showed in perhaps more elegant sf code, but I do not think that this is what you asked for.
For what you want, we need to create a data.frame from a list with varying number of elements. There might be a better way, but here is one:
m <- max(sapply(v, nrow))
iso <- as.character(Eu$iso_a3)
x <- sapply(1:length(v),
function(i) c(iso[i], c(v[[i]][,1], rep(NA, m))[1:m])
)
x <- data.frame(t(x), stringsAsFactors=FALSE)
colnames(x) <- c("iso_a3", paste0("metro_", 1:m))
Merge with the original polygons:
r <- merge(Eu, x, by="iso_a3", all.x=TRUE)
data.frame(r)
# iso_a3 name metro_1 metro_2 metro_3 metro_4
#1 ESP Spain Barcelona Madrid <NA> <NA>
#2 FRA France Lille Lyon Marseille Paris
#3 PRT Portugal Lisbon Porto <NA> <NA>
Answered by Robert Hijmans on November 17, 2020
Get help from others!
Recent Answers
Recent Questions
© 2024 TransWikia.com. All rights reserved. Sites we Love: PCI Database, UKBizDB, Menu Kuliner, Sharing RPP