What I´m looking for is a way to rasterize (or fasterize) geometries within each tile of a certain extent step by step and join the parts of the rasterized geometries to entire raster objects.
## create sample data
# create a frame
library(sf)
ob = st_sf(st_sfc(st_polygon(list(rbind(c(0,0), c(0,9), c(6,9), c(6,0), c(0,0)))))
# create tiles
library(GSIF)
tl <- getSpatialTiles(as(ob, 'Spatial'), block.x=3, overlap.percent=2)
plot(tl)
# create sample polygons
g <- st_sfc(st_point(c(1,2)), st_point(c(5,6)), st_point(c(2,4)), st_point(c(3,3)), st_point(c(3,4)), st_point(c(4,5)))
g.b <- st_buffer(g,0.6)
p <- st_sf(value = ceiling(10*runif(6)),
geometry = st_sfc(g.b))
plot(p, add=TRUE)
Cropping the polygons (or parts of polygons) that are within each tile works fine , i guess. My actual goal is to process the data tile by tile. It looks like the following loop does this and also joins the parts of the polygons to entire polygons. Well, the output (cr) is the same as the input (p)... I thought that this step might be necessary in order to rasterize the resulting (parts of) polygons in the next step. i didn´t expect that the polygons would be joined. I was trying to build a loop based on the intermediate result, therefore following lines are add:
# crop polygons (or parts) for each tile
result <- p
for(i in 1:length(tl)) {cr <- rbind(result, st_crop(p, tl[i]))}
cr <- cr[-7,]
library(scales)
plot(st_geometry(cr), col=alpha("white", 0.5), add=TRUE)
However, I struggle rasterizing the (intermediate) results.
Try this:
myF <- function(i){return(st_crop(p, tl[i]))}
cr <- do.call(rbind, lapply(1:length(tl), myF))
Related
I am trying to iterate thru the process of creating centroids of a list of SpatialPolygonsDataFrames (each with several polygon features within), with the resulting SpatialPoints preserving the attributes (data) of the parent polygons. I have tried the sp::over function, but it seems to be problematic because centroids do not necessarily overlap with parent polygons. Furthermore, I am new to coding/R and do not know how to achieve this in a for loop and/or using an apply function.
So specifically, I need to (1) find a different function that will link the SpatialPolygonsDataFrames with the associated SpatialPoints (centroids); and (2) iterate thru the entire process and merge the SpatialPolygonsDataFrames data with the appropriate SpatialPoints - I do not know how to match the index value of one list to the index value in another while running a loop.
Here is a reproducible example for a single SpatialPolygonsDataFrames object showing that using sp::over doesn't work because some centroids do not end up overlapping parent polygons:
library(maptools) ## For wrld_simpl
library(sp)
## Example SpatialPolygonsDataFrames (SPDF)
data(wrld_simpl) #polygon of world countries
spdf1 <- wrld_simpl[1:25,] #country subset 1
spdf2 <- wrld_simpl[26:36,] #subset 2
spdf3 <- wrld_simpl[36:50,] #subset 3
spdfl[[1]]#data
#plot, so you can see it
plot(spdf1)
plot(spdf2, add=TRUE, col=4)
plot(spdf3, add=TRUE, col=3)
#make list of SPDF objects
spdfl<-list()
spdfl[[1]]<-spdf1
spdfl[[2]]<-spdf2
spdfl[[2]]<-spdf3
#create polygon centroids for each polygon feature (country) within spdfl[[1]]
spdf1c <- gCentroid(spdfl[[1]], byid=TRUE)
plot(spdfl[[1]])
plot(spdf1c, add=TRUE)
#add attributes 'NAME' and 'AREA' to SpatialPoints (centroid) object from SPDF data
spdf.NAME <- over(spdf1c, spdfl[[1]][,"NAME"])
spdf.AREA <- over(spdf1c, spdfl[[1]][,"AREA"])
spdf1c$NAME <- spdf.NAME
spdf1c$AREA <- spdf.AREA
spdf1c#data
#`sp::over` output error = name and area for ATG, ASM, BHS, and SLB are missing
I find SF is a great package for working with spatial data in R. I have fixed a few typos and added the right for loop below.
library(maptools) ## For wrld_simpl
library(sp)
library(sf)
## Example SpatialPolygonsDataFrames (SPDF)
data(wrld_simpl) #polygon of world countries
spdf1 <- wrld_simpl[1:25,] #country subset 1
spdf2 <- wrld_simpl[26:36,] #subset 2
spdf3 <- wrld_simpl[36:50,] #subset 3
spdfl[[1]]#data
#plot, so you can see it
plot(spdf1)
plot(spdf2, add=TRUE, col=4)
plot(spdf3, add=TRUE, col=3)
#make list of SPDF objects
spdfl<-list()
spdfl[[1]]<-spdf1
spdfl[[2]]<-spdf2
spdfl[[3]]<-spdf3
# Empty List for Centroids
centres <-list()
# For Loop
for (i in 1:length(spdfl)) {
centres[[i]] <- spdfl[[i]] %>%
sf::st_as_sf() %>% # translate to SF object
sf::st_centroid() %>% # get centroids
as(.,'Spatial') # If you want to keep as SF objects remove this line
}
#Plot your Spatial Objects
plot(spdfl[[1]])
plot(centres[[1]], add=TRUE)
Here is a solution which uses SF and sp. SF is great as it keeps things as dataframes which can easy to deal with. More information here: https://r-spatial.github.io/sf/
I have raster of the following features:
library(raster)
library(rgeos)
test <- raster(nrow=225, ncols=478, xmn=-15.8, xmx=32, ymn=-9.4, ymx=13.1)
I want to mask in this raster the cells that are within a given distance of a point.
I create the spatial points as followed:
p2=readWKT("POINT(31.55 -1.05)")
Then I create a spatial polygon object by adding a 0.5 buffer:
p2_Buffered <- gBuffer(p2, width = 0.5)
mask(test, mask=p2_Buffered,inverse=T)
When I mask my raster given this spatial object, I have the following error message:
Error in .polygonsToRaster(x, y, field = field, fun = fun, background
= background, : number of items to replace is not a multiple of replacement length
I do not understand because this is script I have been running many many times with different point and different buffer width without any problem.
What is strange is that when I change the width of the buffer, it works fine:
p2_Buffered <- gBuffer(p2, width = 0.4)
mask(test, mask=p2_Buffered,inverse=T)
This is also true for a different focal point:
p2=readWKT("POINT(32.55 -1)")
p2_Buffered <- gBuffer(p2, width = 0.5)
mask(test, mask=p2_Buffered,inverse=T)
I would like to identify the specific problem I have for that point because this is a script I should run in a routine (I have been doing it without any problem so far).
Thanks a lot
This is indeed a bug with polygons that go over the edge of a raster. It has been fixed in version 2.3-40 (now on CRAN), so it should go away if you update the raster package.
Here is a workaround (removing the part of the polygon that goes over the edge).
library(raster)
library(rgeos)
r <- raster(nrow=225, ncols=478, xmn=-15.8, xmx=32, ymn=-9.4, ymx=13.1)
e <- as(extent(r), 'SpatialPolygons')
p <- readWKT("POINT(31.55 -1.05)")
pb <- gBuffer(p, width = 0.5)
pbe <- intersect(pb, e)
values(r)
x <- mask(r, mask=pbe, inverse=TRUE)
You usually need to set some values to the raster layer. For a mask layer its always best to set values to 1.
library(raster)
library(rgeos)
# make sample raster
test <- raster(nrow=225, ncols=478, xmn=-15.8, xmx=32, ymn=-9.4, ymx=13.1)
# set values of raster for mask
test <- setValues(test, 1)
# make point buffer
p2=readWKT("POINT(15 5)")
p2_Buffered <- gBuffer(p2, width = 1.5)
# name projection of buffer (assume its the same as raster)
projection(p2_Buffered) <- projection(test)
# visual check
plot(test); plot(p2_Buffered, add=T)
If you want to trim down your raster layer to the just the single polygon then try this workflow.
step1 <- crop(test, p2_Buffered) # crop to same extent
step2 <- rasterize(p2_Buffered, step1) # rasterize polygon
final <- step1*step2 # make your final product
plot(final)
If you just want to poke a hole in your raster layer then use the mask function
# rasterize your polygon
p2_Buffered <- rasterize(p2_Buffered, test, fun='sum')
# now mask it
my_mask <- mask(test, mask=p2_Buffered,inverse=T) # try changing the inverse argument
plot(my_mask)
I've inherited a geodatabase of polygons of lakes for which I am trying to create sampling grids on each lake. My current strategy is to export spatial data to CSV, use R to run a loop to create the grids on each lake, and then write to a new shapefile. However, here is my problem, when exporting to a CSV the WKT strings get messed up and put onto different lines. Okay, no problem, I moved on to exporting just the geometry to CSV so that I get X-Y values. When I simply plot the points they look perfect (using plot(y~x)), but the points are not in order. So, when I transform the data to a SpatialPolygon in the sp package in R using the following sequence:
XY-points -> Polygon -> Polygons -> SpatialPolygon
and then plot the SpatialPolygon I get this:
I know this is an artifact of incorrectly ordered points, because when I order the points by X and then by Y and run the same procedure here is what I get:
This is what the correct plotting is supposed to look like (X-Y data plotted with open circles):
Here is a short reproducible example of what I am trying to deal with:
library(sp)
# correct polygon
data <- data.frame(x=c(1:10, 10:1), y=c(5:1, 1:10, 10:6))
# plot(y~x, data=data)
correct.data.points <- rbind(data, data[1,]) # to close the ring for a polygon
correct.data.coords <- as.matrix(cbind(correct.data.points))
correct.data.poly <- Polygon(correct.data.coords, hole=F)
correct.data.poly <- Polygons(list(correct.data.poly), ID=0)
correct.data.poly.sp <- SpatialPolygons(list(correct.data.poly))
plot(correct.data.poly.sp)
# incorrect polygon
scr.data <- data[c(sample(1:20)),]
# plot(y~x, data=scr.data)
scr.data.points <- rbind(scr.data, scr.data[1,]) # to close the ring for a polygon
scr.data.coords <- as.matrix(cbind(scr.data.points))
scr.data.poly <- Polygon(scr.data.coords, hole=F)
scr.data.poly <- Polygons(list(scr.data.poly), ID=0)
scr.data.poly.sp <- SpatialPolygons(list(scr.data.poly))
plot(scr.data.poly.sp)
Any thoughts? Thanks for any help or insight anyone can provide. Also, for reference I am using QGIS 2.6.0 and the MMQGIS Python plugin to do the geometry exporting.
In R I performed an analysis on a set of X, Y coordinates producing a Voronoi diagram, and I then created a border. Here I intersect the diagram and the border, and attempt to get the area of the resulting polygons. The areas for the interior "hole" polys are correct, but the edge polygons appear to maintain their original exaggerated size. A link to my data is here:
https://drive.google.com/a/ruths.ai/file/d/0B8QG4cbDqH0UaGM2VkkxZHZkZTA/edit?usp=sharing
Code illustrating the problem is here:
# Read in shapefiles.
# Files are located at:
# https://drive.google.com/a/ruths.ai/file/d/0B8QG4cbDqH0UaGM2VkkxZHZkZTA/edit?usp=sharing
require(sp)
require(rgeos)
SPDF <- readShapeSpatial("SPDF.shp")
SpP <- readShapeSpatial("SpP.shp")
# Examine plots
plot(SPDF)
SPDF#polygons[[337]]#area # Too large; want it cut off
SPDF#polygons[[339]]#area # Hole poly; area correct
gArea(SPDF[339,]) # Provides same area
gArea(SPDF[337,]) # Still provide wrong answer for problem
# poly # 337
# Merge polys using gDifference
D <- gDifference(SpP, SPDF, byid = TRUE)
plot(D)
# Seems to work, but areas now have a couple of problems.
# I pick apart D using the plotOrder slot to separate
# polys that are holes versus those that are not, allowing
# me to get the correct area for "hole" polys, but the
# edge polygons are still not correct, maintaining their
# area estimates from the original SPDF data frame.
areas <- vector()
for (i in 337:339){ # 337 = exterior poly, 338 and 339 are holes
po <- D#polygons[[i]]#plotOrder
if (max(po) == 2) {
areas[i] <- D#polygons[[i]]#Polygons[[2]]#area
} else {
areas[i] <- D#polygons[[i]]#area
}
}
areas
# How does one get the right areas for the edges that should be cut
# off by the intersection?
I should have used gIntersection instead of gDifference. For some reason that I still do not understand, the plot after using gDifference seemed correct, which was the main reason for the confusion.
I am fairly new to R, but not to ArcView. I am plotting some two-mode data, and want to convert the plot to a shapefile. Specifically, I would like to convert the vertices and the edges, if possible, so that I can get the same plot to display in ArcView, along with the attributes.
I've installed the package "shapefiles", and I see the convert.to.shapefile command, but the help doesn't talk about how to assign XY coords to the vertices.
Thank you,
Tim
Ok, I'm making a couple of assumptions here, but I read the question as you're looking to assign spatial coordinates to a bipartite graph and export both the vertices and edges as point shapefiles and polylines for use in ArcGIS.
This solution is a little kludgey, but will make shapefiles with coordinate limits xmin, ymin and xmax, ymax of -0.5 and +0.5. It will be up to you to decide on the graph layout algorithm (e.g. Kamada-Kawai), and project the shapefiles in the desired coordinate system once the shapefiles are in ArcGIS as per #gsk3's suggestion. Additional attributes for the vertices and edges can be added where the points.data and edge.data data frames are created.
library(igraph)
library(shapefiles)
# Create dummy incidence matrix
inc <- matrix(sample(0:1, 15, repl=TRUE), 3, 5)
colnames(inc) <- c(1:5) # Person ID
rownames(inc) <- letters[1:3] # Event
# Create bipartite graph
g.bipartite <- graph.incidence(inc, mode="in", add.names=TRUE)
# Plot figure to get xy coordinates for vertices
tk <- tkplot(g.bipartite, canvas.width=500, canvas.height=500)
tkcoords <- tkplot.getcoords(1, norm=TRUE) # Get coordinates of nodes centered on 0 with +/-0.5 for max and min values
# Create point shapefile for nodes
n.points <- nrow(tkcoords)
points.attr <- data.frame(Id=1:n.points, X=tkcoords[,1], Y=tkcoords[,2])
points.data <- data.frame(Id=points.attr$Id, Name=paste("Vertex", 1:n.points, sep=""))
points.shp <- convert.to.shapefile(points.attr, points.data, "Id", 1)
write.shapefile(points.shp, "~/Desktop/points", arcgis=TRUE)
# Create polylines for edges in this example from incidence matrix
n.edges <- sum(inc) # number of edges based on incidence matrix
Id <- rep(1:n.edges,each=2) # Generate Id number for edges.
From.nodes <- g.bipartite[[4]]+1 # Get position of "From" vertices in incidence matrix
To.nodes <- g.bipartite[[3]]-max(From.nodes)+1 # Get position of "To" vertices in incidence matrix
# Generate index where position alternates between "From.node" to "To.node"
node.index <- matrix(t(matrix(c(From.nodes, To.nodes), ncol=2)))
edge.attr <- data.frame(Id, X=tkcoords[node.index, 1], Y=tkcoords[node.index, 2])
edge.data <- data.frame(Id=1:n.edges, Name=paste("Edge", 1:n.edges, sep=""))
edge.shp <- convert.to.shapefile(edge.attr, edge.data, "Id", 3)
write.shapefile(edge.shp, "~/Desktop/edges", arcgis=TRUE)
Hope this helps.
I'm going to take a stab at this based on a wild guess as to what your data looks like.
Basically you'll want to coerce the data into a data.frame with two columns containing the x and y coordinates (or lat/long, or whatever).
library(sp)
data(meuse.grid)
class(meuse.grid)
coordinates(meuse.grid) <- ~x+y
class(meuse.grid)
Once you have it as a SpatialPointsDataFrame, sp provides some decent functionality, including exporting shapefiles:
writePointsShape(meuse.grid,"/home/myfiles/wherever/myshape.shp")
Relevant help files examples are drawn from:
coordinates
SpatialPointsDataFrame
readShapePoints
At least a few years ago when I last used sp, it was great about projection and very bad about writing projection information to the shapefile. So it's best to leave the coordinates untransformed and manually tell Arc what projection it is. Or use writeOGR rather than writePointsShape.