I have a series of points in an area whose 'footprint' shape is highly irregular:
I'd like to determine all of the coordinates within the footprint's vertices. The end goal is to determine which data points lay outside this footprint.
Does anyone have an efficient way to go about doing this??
My best idea to approaching this is to draw a polygon based on the green area's vertices and then use said polygon's coordinates to determine 'outlier' points' (though, I'm not sure how to do that yet -- one step at a time!).
However, when I try creating a convex hull, it obviously creates problems because of the irregular shape of my green space. [Anyone know of a way to create CONCAVE hulls?]
Alternatively, is there a way to draw polygons manually using a 'click the graph' type method?
...Again, if you have a better solution to my problem than using polygons, please by all means suggest that solution!
Alternatively, is there a way to draw polygons manually using a 'click
the graph' type method?
Here's one idea. First, some random points:
library(manipulate)
library(sp)
set.seed(1)
par(pch = 19, cex=.5)
x <- runif(1000)
y <- runif(1000)
Now, draw and capture the polygon:
coords <- data.frame()
manipulate({
plot(y~x)
res <- manipulatorMouseClick()
coords <<- rbind(coords, data.frame(x=res$userX, y=res$userY))
if (length(coords)) lines(coords)
})
And determine which points are inside/outside of it (see ?point.in.polygon):
res <- point.in.polygon(x, y, coords$x, coords$y)!=0
plot(y~x, col = res + 1L)
lines(coords)
Related
I have drawn an imaginary map for fun, and I thought it would be even more fun to make a DEM for the imaginary country using R, just to add to the visualization. :)
Basically, I now have a png with the outline of the country. Just only with a black border, the rest is left white.
My plan was to create a raster from it, so I could eventually add values to every pixel in the map. Since it's a imaginary map, it doesn't have a coordinate system. I just set the extent as it is as png:
drawing <- raster('drawing.png')
extent(drawing) <- c(0, 388, 0, 348)
Then I create a dataframe with the points of which eventually the DEM is calculated from. Basically, I just randomly select pixel locations within the country (name them x and y) and assign a random value as height. For this I use a simple excel sheet:
samples <- read_excel('samples.xlsx')
coordinates(samples) <- ~x+y
spatlocations <- SpatialPoints(samples)
vectlocations <- vect(samples)
Then, I thought, I could create a map using Inverse Distance Weighting (IDW), just to get a nice smooth gradient looking map. So it would possibly something like this:
result <- idw(unsampled, samples)
However, I fail to do this. I for example don't really know how to exactly work with the raster I created from the png, and the same for the sample points.
As you maybe can see, I am what you could consider a beginner :).
Is this possible at all? Is this methodology correct? How can I achieve such a map? Thanks in advance!
I need help! I have a list of points (mydata X, Y = latitude, longitude) which I want to set in relation towards a single Point (for example 287, 347). I just want to measure (and plot) the distance between the list of points towards this single point of reference. But I don't get it. I tried PCA, but there I can't set my specific point of reference.
Does anyone has an idea how to do this?
mydata:
X,Y
260,204
409,208
260,60
272,181
367,257
315,208
381,294
66,333
246,220
330,207
342,304
...
If all you want is the euclidean distance, why not just make your own function to do it?
distance <- function(x, y, home= c(0,0)) sqrt((x-home[1])^2+(y-home[2])^2)
plot(distance(400*runif(100), 400*runif(100), c(287, 347)))
Using the distance formula, this is a trivial calculation:
mydata$distance = sqrt((mydata$X - 287)^2 + (mydata$Y - 347)^2)
I'm not sure what sort of plot you would like of distance - perhaps a histogram?
hist(mydata$distance)
I solved it by compute the distance like Georg said and then by using the density function.
mydata <- read.table("blabla.csv", sep=",")
mydata$distance = sqrt((mydata$V1 -555)^2 + (mydata$V2 - 85)^2)
d <- density(mydata$distance)
plot(d)
I think this is something like a Kernel regression (KDE)?.
I want to create 50 concentric circles. I did it with python but now I want to do this in R. I have tried the symbols function but with no result. I want my circles to start from x,y coordinates and the radius of each circle to be 3times bigger than the previous.
step=1
for(i in seq(1,50,1)){
symbols (x, y, circles=50, col="grey")
step=step+3
}
From this I get one circle as a result.
I am new in programming so it is probably very simple. Should I use a specific package?
The beauty of R is that many things can be vectorized, including the imput to the 'symbols' function. Here's an example for you:
#vector of radii
#written in a way that's easily changable
n_circles <- 50
my_circles <- seq(1,by=1,length.out = n_circles)
#generate x and y
x <- rep(1,n_circles)
y <- rep(1, n_circles)
#plot
symbols(x,y,1:n_circles)
I am trying to find the orthogonal distance between a set of location coordinates and a set of lines (roads or rivers). The set of points are in the form of latitude/longitude pairs, and the lines are in a shapefile (.shp). Plotting them on a map is not a problem, using either maptools or PBSmapping. But my basic problem is to find the minimum distance one has to travel from a location to reach a road or a river. Is there any way to do this in R?
If I understand correctly, you can do this simply enough with gDistance in the rgeos package.
Read in the lines as SpatialLines/DataFrame and points as SpatialPoints/DataFrame and then loop over each point calculating the distance each time:
require(rgeos)
## untested code
shortest.dists <- numeric(nrow(sp.pts))
for (i in seq_len(nrow(sp.pts)) {
shortest.dists[i] <- gDistance(sp.pts[i,], sp.lns)
}
Here sp.pts is the Spatial points object, and sp.lns is the Spatial lines object.
You must loop so that you only compare a single coordinate in sp.pts with the entirety of all lines geometries in sp.lns, otherwise you get the distance from an aggregate value across all points.
Since your data are in latitude/longitude you should transform both the lines and points to a suitable projection since the gDistance function assumes Cartesian distance.
MORE DISCUSSION AND EXAMPLE (edit)
It would be neat to get the nearest point on the line/s rather than just the distance, but this opens another option which is whether you need the nearest coordinate along a line, or an actual intersection with a line segment that is closer than any existing vertex. If your vertices are dense enough that the difference doesn't matter, then use spDistsN1 in the sp package. You'd have to extract all the coordinates from every line in the set (not hard, but a bit ugly) and then loop over each point of interest calculating the distance to the line vertices - then you can find which is the shortest and select that coordinate from the set of vertices, so you can have the distance and the coordinate easily. There's no need to project either since the function can use ellipsoidal distances with longlat = TRUE argument.
library(maptools)
## simple global data set, which we coerce to Lines
data(wrld_simpl)
wrld_lines <- as(wrld_simpl, "SpatialLinesDataFrame")
## get every coordinate as a simple matrix (scary but quick)
wrld_coords <- do.call("rbind", lapply(wrld_lines#lines, function(x1) do.call("rbind", lapply(x1#Lines, function(x2) x2#coords[-nrow(x2#coords), ]))))
Check it out interactively, you'll have to modify this to save the coords or minimum distances. This will plot up the lines and wait for you to click anywhere in the plot, then it will draw a line from your click to the nearest vertex on a line.
## no out of bounds clicking . . .
par(mar = c(0, 0, 0, 0), xaxs = "i", yaxs = "i")
plot(wrld_lines, asp = "")
n <- 5
for (i in seq_len(n)) {
xy <- matrix(unlist(locator(1)), ncol = 2)
all.dists <- spDistsN1(wrld_coords, xy, longlat = TRUE)
min.index <- which.min(all.dists)
points(xy, pch = "X")
lines(rbind(xy, wrld_coords[min.index, , drop = FALSE]), col = "green", lwd = 2)
}
The geosphere package has the dist2line function that does this for lon/lat data. It can use Spatial* objects or matrices.
line <- rbind(c(-180,-20), c(-150,-10), c(-140,55), c(10, 0), c(-140,-60))
pnts <- rbind(c(-170,0), c(-75,0), c(-70,-10), c(-80,20), c(-100,-50),
c(-100,-60), c(-100,-40), c(-100,-20), c(-100,-10), c(-100,0))
d <- dist2Line(pnts, line)
d
Illustration of the results
plot( makeLine(line), type='l')
points(line)
points(pnts, col='blue', pch=20)
points(d[,2], d[,3], col='red', pch='x')
for (i in 1:nrow(d)) lines(gcIntermediate(pnts[i,], d[i,2:3], 10), lwd=2)
Looks like this can be done in the sf package using the st_distance function.
You pass your two sf objects to the function. Same issue as with the other solutions in that you need to iterate over your points so that the function calculates the distance between every point to every point on the roadways. Then take the minimum of the resulting vector for the shortest distance.
# Solution for one point
min(st_distance(roads_sf, points_sf[1, ]))
# Iterate over all points using sapply
sapply(1:nrow(points_sf), function(x) min(st_distance(roads_sf, points_sf[x, ])))
I would like to identify linear features, such as roads and rivers, on raster maps and convert them to a linear spatial object (SpatialLines class) using R.
The raster and sp packages can be used to convert features from rasters to polygon vector objects (SpatialPolygons class). rasterToPolygons() will extract cells of a certain value from a raster and return a polygon object. The product can be simplified using the dissolve=TRUE option, which calls routines in the rgeos package to do this.
This all works just fine, but I would prefer it to be a SpatialLines object. How can I do this?
Consider this example:
## Produce a sinuous linear feature on a raster as an example
library(raster)
r <- raster(nrow=400, ncol=400, xmn=0, ymn=0, xmx=400, ymx=400)
r[] <- NA
x <-seq(1, 100, by=0.01)
r[cellFromRowCol(r, round((sin(0.2*x) + cos(0.06*x)+2)*100), round(x*4))] <- 1
## Quick trick to make it three cells wide
r[edge(r, type="outer")] <- 1
## Plot
plot(r, legend=FALSE, axes=FALSE)
## Convert linear feature to a SpatialPolygons object
library(rgeos)
rPoly <- rasterToPolygons(r, fun=function(x) x==1, dissolve=TRUE)
plot(rPoly)
Would the best approach be to find a centre line through the polygon?
Or is there existing code available to do this?
EDIT: Thanks to #mdsumner for pointing out that this is called skeletonization.
Here's my effort. The plan is:
densify the lines
compute a delaunay triangulation
take the midpoints, and take those points that are in the polygon
build a distance-weighted minimum spanning tree
find its graph diameter path
The densifying code for starters:
densify <- function(xy,n=5){
## densify a 2-col matrix
cbind(dens(xy[,1],n=n),dens(xy[,2],n=n))
}
dens <- function(x,n=5){
## densify a vector
out = rep(NA,1+(length(x)-1)*(n+1))
ss = seq(1,length(out),by=(n+1))
out[ss]=x
for(s in 1:(length(x)-1)){
out[(1+ss[s]):(ss[s+1]-1)]=seq(x[s],x[s+1],len=(n+2))[-c(1,n+2)]
}
out
}
And now the main course:
simplecentre <- function(xyP,dense){
require(deldir)
require(splancs)
require(igraph)
require(rgeos)
### optionally add extra points
if(!missing(dense)){
xy = densify(xyP,dense)
} else {
xy = xyP
}
### compute triangulation
d=deldir(xy[,1],xy[,2])
### find midpoints of triangle sides
mids=cbind((d$delsgs[,'x1']+d$delsgs[,'x2'])/2,
(d$delsgs[,'y1']+d$delsgs[,'y2'])/2)
### get points that are inside the polygon
sr = SpatialPolygons(list(Polygons(list(Polygon(xyP)),ID=1)))
ins = over(SpatialPoints(mids),sr)
### select the points
pts = mids[!is.na(ins),]
dPoly = gDistance(as(sr,"SpatialLines"),SpatialPoints(pts),byid=TRUE)
pts = pts[dPoly > max(dPoly/1.5),]
### now build a minimum spanning tree weighted on the distance
G = graph.adjacency(as.matrix(dist(pts)),weighted=TRUE,mode="upper")
T = minimum.spanning.tree(G,weighted=TRUE)
### get a diameter
path = get.diameter(T)
if(length(path)!=vcount(T)){
stop("Path not linear - try increasing dens parameter")
}
### path should be the sequence of points in order
list(pts=pts[path+1,],tree=T)
}
Instead of the buffering of the earlier version I compute the distance from each midpoint to the line of the polygon, and only take points that are a) inside, and b) further from the edge than 1.5 of the distance of the inside point that is furthest from the edge.
Problems can arise if the polygon kinks back on itself, with long segments, and no densification. In this case the graph is a tree and the code reports it.
As a test, I digitized a line (s, SpatialLines object), buffered it (p), then computed the centreline and superimposed them:
s = capture()
p = gBuffer(s,width=0.2)
plot(p,col="#cdeaff")
plot(s,add=TRUE,lwd=3,col="red")
scp = simplecentre(onering(p))
lines(scp$pts,col="white")
The 'onering' function just gets the coordinates of one ring from a SpatialPolygons thing that should only be one ring:
onering=function(p){p#polygons[[1]]#Polygons[[1]]#coords}
Capture spatial lines features with the 'capture' function:
capture = function(){p=locator(type="l")
SpatialLines(list(Lines(list(Line(cbind(p$x,p$y))),ID=1)))}
Thanks to #klewis at gis.stackexchange.com for linking to this elegant algorithm for finding the centre line (in response to a related question I asked there).
The process requires finding the coordinates on the edge of a polygon describing the linear feature and performing a Voronoi tessellation of those points. The coordinates of the Voronoi tiles that fall within the polygon of the linear feature fall on the centre line. Turn these points into a line.
Voronoi tessellation is done really efficiently in R using the deldir package, and intersections of polygons and points with the rgeos package.
## Find points on boundary of rPoly (see question)
rPolyPts <- coordinates(as(as(rPoly, "SpatialLinesDataFrame"),
"SpatialPointsDataFrame"))
## Perform Voronoi tessellation of those points and extract coordinates of tiles
library(deldir)
rVoronoi <- tile.list(deldir(rPolyPts[, 1], rPolyPts[,2]))
rVoronoiPts <- SpatialPoints(do.call(rbind,
lapply(rVoronoi, function(x) cbind(x$x, x$y))))
## Find the points on the Voronoi tiles that fall inside
## the linear feature polygon
## N.B. That the width parameter may need to be adjusted if coordinate
## system is fractional (i.e. if longlat), but must be negative, and less
## than the dimension of a cell on the original raster.
library(rgeos)
rLinePts <- gIntersection(gBuffer(rPoly, width=-1), rVoronoiPts)
## Create SpatialLines object
rLine <- SpatialLines(list(Lines(Line(rLinePts), ID="1")))
The resulting SpatialLines object:
You can get the boundary of that polygon as SpatialLines by direct coercion:
rLines <- as(rPoly, "SpatialLinesDataFrame")
Summarizing the coordinates down to a single "centre line" would be possible, but nothing immediate that I know of. I think that process is generally called "skeletonization":
http://en.wikipedia.org/wiki/Topological_skeleton
I think ideal solution would be to build such negative buffer which dynamically reach the minimum width and doesn't break when value is too large; keeps continued object and eventually, draws a line if the value is reached. But unfortunately, this may be very compute demanding because this would be done probably in steps and checks if the value for particular point is enough to have a point (of our middle line). Possible it's ne need to have infinitive number of steps, or at least, some parametrized value.
I don't know how to implement this for now.