I am having a lot of trouble in R calculating the area of home range of an animal. I thought once I produced a home range (if I've done it correctly) calculating the area would be easy, but no
I've pasted some of the code I've been trying. I wonder would anyone have any insight?
# Load package
library(adehabitat)
#Load file Frodo
dd <- read.csv(file.choose(), header = T)
# Plot the home range
xy <- dd[,c("X","Y")]
id <- dd[,"name"]
hr<- mcp(xy,id,percent=95)
plot(hr)
points(xy[xy$id=="frodo",])
#Great. Home range produced. Now calculate area
area <- mcp.area(xy, id,percent = 95),
# Result 2.287789e-09 Ha. Way to small. Maybe it doesnt like Lat / Long.
# Will try and convert coordinates into M or Km
# Load map project
library(mapproj)
x<-mapproject(t$X,t$Y,projection="mercator")
# Its converted it to something but its not M's or Km's.
# I'll try and run it anyway
xy <- x[,c("X","Y")]
# incorrect number of dimensions
# Ill try Project 4
library(proj4)
xy <- dd[,c("X","Y")]
tr <- ptransform(xy/180*pi, '+proj=latlong +ellps=sphere',
'+proj=merc +ellps=sphere')
View(tr)
# There seems to be a Z column filled with 0's.
# It that going to affect anything?
# Let's look at the data
plot(tr)
# Looks good, Lets try and create a home range
xy <- tr[,c("x","y")]
# 'incorrect number of dimensions'
No idea what the problem is. Don't know if I'm on the right track or doing something completely wrong
In order to calculate area you need your points in a projected coordinate systems (area in long/lat would just be units of degree). The type of projection you use is going to have a big effect on the resulting area. For instance the Mercator projection distorts area away from the Equator -- you might want to look into the best equal-area projection for your location. I am going to answer the programming part of your question, once you find the right projections to use you can substitute them in.
require(sp)
require(rgdal)
orig.points <- dd[,c("X","Y")]
# geographic coordinate system of your points
c1 <- CRS("+proj=latlong +ellps=sphere")
# define as SpatialPoints
p1 <- SpatialPoints(orig.points, proj4string=c1)
# define projected coordinate system of your choice, I am using the one you
# defined above, but see:
# http://www.remotesensing.org/geotiff/proj_list/mercator_1sp.html
# to make sure your definition of the mercator projection is appropriate
c2 <- CRS("+proj=merc ellps=sphere")
p2 <- spTransform(p1, c2) # project points
# convert to Polygon (this automatically computes the area as an attribute)
poly <- Polygon(p2)
poly#area #will print out the area
Related
I am trying to identify all points (postcodes in my case) that are located near to the coastline of the UK (i.e., a polygon). I am using R to process this.
I downloaded the geographical outline of United Kingdom from here as a shapefile. A list of all postcodes for the UK were accessed from the ONS here. Please note that the latter file is very large (211MB zipped).
To begin, I loaded in both files into R, and then convert them to the same coordinate reference system (OSGB1936; 27700). For the polygon of the UK, I convert this to lines that represent the boundary/coastline (note that while Northern Ireland shares a common boundary with Ireland, I will subset any postcodes erroneously matched as near the coastline by lat/long later). I then convert the points into spatial points.
# Load libraries
library(sf)
library(data.table)
# Load data
uk_shp <- read_sf("./GBR_adm/GBR_adm0.shp") # Load UK shapefile (ignore the download file says GBR, it is UK)
uk_shp <- st_transform(uk_shp, crs = 27700) # Convert to co-ordinate reference system (CRS) that allow buffers in correct units later (note: 4326 is World CRS)
uk_coast <- st_cast(uk_shp,"MULTILINESTRING") # Convert polygon to a line (i.e., coastline)
# Load in postcodes
pcd <- fread("./ONSPD_FEB_2022_UK/Data/ONSPD_FEB_2022_UK.csv") # Load all postcodes for Great Britain - this is a very large file so I also create a single
pcd <- pcd[, c(1:3, 43:44)] # Drop unnecessary information/columns to save memory
# Convert to spatial points data frame
pcd_sp <- pcd %>% # For object of postcodes
st_as_sf(coords = c("long", "lat")) %>% # Define as spatial object and identify which columns tell us the position of points
st_set_crs(27700) # Set CRS
I originally thought the most efficient approach to take would be to define what a coastal region is (here defined as within 5km of the coastline), create a buffer to represent that around the coastline, and then use a point-in-polygon function to select all points within the buffers. However, the code below had not finished running overnight which probably suggests that it was the incorrect approach and I an unsure why it is taking so long.
uk_coast <- st_buffer(uk_coast, 5000) # Create 5km buffer
pcd_coastal <- st_intersection(uk_buf, pcd_sp) # Point-in-polygon (i.e., keep only the postcodes that are located in the buffer region)
So I changed my approach to calculate the straight-line distance of each point to the nearest coastline. In running the code below, it gives incorrect distances. For example below, I select one postcode (AB12 4XP) which is located ~2.6km from the coastline, however the code below gives ~82km which is very wrong. I had tried st_nearest_feature() but could not get it to work (it may do, but was beyond my attempts).
test <- pcd_sp[pcd_sp$pcd == "AB124XP",] # Subset test postcode
dist <- st_distance(test, uk_coast, by_element = TRUE, which = "Euclidean") # Calculate distance
I am unsure how to proceed from here - I don't think it is the wrong CRS. It might be that the multilinestring conversion is causing problems. Does anyone have suggestions what to do?
sf has an st_is_within_distance function that can test if points are within a distance of a line. My test data is 10,000 random points in the bounding box of the UK shape, and the UK shape in OSGB grid coordinates.
> system.time({indist = st_is_within_distance(uk_coast, pts, dist=5000)})
user system elapsed
30.907 0.003 30.928
But this isn't building a spatial index. The docs say that it does build a spatial index if the coordinates are "geographic" and the flag for using spherical geometry is set. I don't understand why it can't build one for cartesian coordinates, but lets see how much faster it is...
Transform takes no time at all:
> ukLL = st_transform(uk_coast, 4326)
> ptsLL = st_transform(pts, 4326)
Then test...
system.time({indistLL = st_is_within_distance(ukLL, ptsLL, dist=5000)})
user system elapsed
1.405 0.000 1.404
Just over a second. Any difference between the two? Let's see:
> setdiff(indistLL[[1]], indist[[1]])
[1] 3123
> setdiff(indist[[1]], indistLL[[1]])
integer(0)
So point 3123 is in the set using lat-long, but not the set using OSGB. There's nothing in OSGB that isn't in the lat-long set.
Quick plot to show the selected points:
> plot(uk_coast$geometry)
> plot(pts$geometry[indistLL[[1]]], add=TRUE)
I'm having trouble calculating in R the area of an imported shapefile that has a multipart polygon (one feature containing two separate polygons). I noticed that ArcMap gave me a different value for the area of a shapefile than raster::area. To figure out which program was giving me the correct area, I broke the shapefile into single parts and recalculated the area of the two separate polygons:
library(raster)
> single_part <- shapefile("../Desktop/test/test_sp.shp")
> area(single_part)
[1] 575924.0 433409.8
> sum(area(single_part))
[1] 1009334
>
> multi_part <- shapefile("../Desktop/test/test_mp.shp")
> area(multi_part)
[1] 1018390
I realize now that I know about this problem, I should always break up polygon feature classes into single parts, but does anyone know how raster::area calculates the area of multipart polygons? I also tried using rgeos::gArea but got the same result. Is there a way to calculate the area of multipart polygons in R?
I'd love to know, because they're pretty common and I'm trying to switch from doing all my analyses in ArcMap to R.
In case it's helpful, here's an image of the shapefile:
multipart poly shapefile
EDIT ADDED 9/21/2018 -------------------------------------------------------
Here's a link to the shapefile test_mp.shp
From what I can tell, it seems like the problem stems from how R (vs. ArcMap) interprets the holes. See the difference between the ArcMap display and the R display. For some reason R is filling in those holes as part of the shapefile, which must be the reason that I'm getting different calculations for the area. Is there something wrong with the shapefile, or how I'm importing it?
Clearly your object named 'multi_part' has only one (multi?) polygon, as area returns a single value. I illustrate here how to investigate what you are after:
library(raster)
d <- getData('GADM', country='Isle of Man', level=0)
area(d)
[1] 579672897
Split into 4 polygons (islands)
dd <- disaggregate(d)
a <- area(dd)
a
[1] 19424.12 2705442.41 25629.79 576922400.90
sum(a)
[1] 579672897
The same area, and there is no reason why they would be different. Except perhaps if there is confusion with polygon holes. It is difficult to comment without your data.
You can write these objects to disk (see below) and see what ArcGIS gives you as area (but note that this example uses lon/lat coordinates, I am not sure if ArcGIS can compute areas on those).
shapefile(d, "man.shp")
Here is a case with and without a hole:
p1 <- rbind(c(-180,-20), c(-140,55), c(10, 0), c(-140,-60), c(-180,-20))
p2 <- rbind(c(-150,-20), c(-100,-10), c(-110,20), c(-150,-20))
# two (overlapping) polygons (no hole)
pol1 <- spPolygons(p1, p2, crs="+proj=utm +zone=1 +datum=WGS84")
# single polygon with hole
pol2 <- spPolygons(list(p1, p2), crs="+proj=utm +zone=1 +datum=WGS84")
a <- area(pol1) / 10e+9
b <- area(pol2) / 10e+9
a
#[1] 10925 800
sum(a)
#[1] 11725
a[1]-a[2]
#[1] 10125
b matches a[1] - a[2], as expected
b
#[1] 10125
I get exactly the same results with ArcGIS, using "calculate geometry" for a field in the attribute tables.
lat long
7.16 124.21
8.6 123.35
8.43 124.28
8.15 125.08
Consider these coordinates, these coordinates correspond to weather stations that measure rainfall data.
The intro to the gstat package in R uses the meuse dataset. At some point in this tutorial: https://rpubs.com/nabilabd/118172, the guys makes use of a "meuse.grid" in this line of code:
data("meuse.grid")
I do not have such a file and I do not know how to create it, can I create one using these coordinates? Or at least point me to material that discusses how to create a custom grid for a custom area (i.e not using administrative boundaries from GADM).
Probably wording this wrong, don't even know if this question makes sense to R savvy people. Still, would love to hear some direction, or at least tips. Thanks a lot!
Total noob at R and statistics.
EDIT: See the sample grid that the tutorial I posted looks like, that's the thing I want to make.
EDIT 2: Would this method be viable? https://rstudio-pubs-static.s3.amazonaws.com/46259_d328295794034414944deea60552a942.html
I am going to share my approach to create a grid for kriging. There are probably more efficient or elegant ways to achieve the same task, but I hope this will be a start to facilitate some discussions.
The original poster was thinking about 1 km for every 10 pixels, but that is probably too much. I am going to create a grid with cell size equals to 1 km * 1 km. In addition, the original poster did not specify an origin of the grid, so I will spend some time determining a good starting point. I also assume that the Spherical Mercator projection coordinate system is the appropriate choice for the projection. This is a common projection for Google Map or Open Street Maps.
1. Load Packages
I am going to use the following packages. sp, rgdal, and raster are packages provide many useful functions for spatial analysis. leaflet and mapview are packages for quick exploratory visualization of spatial data.
# Load packages
library(sp)
library(rgdal)
library(raster)
library(leaflet)
library(mapview)
2. Exploratory Visualization of the station locations
I created an interactive map to inspect the location of the four stations. Because the original poster provided the latitude and longitude of these four stations, I can create a SpatialPointsDataFrame with Latitude/Longitude projection. Notice the EPSG code for Latitude/Longitude projection is 4326. To learn more about EPSG code, please see this tutorial (https://www.nceas.ucsb.edu/~frazier/RSpatialGuides/OverviewCoordinateReferenceSystems.pdf).
# Create a data frame showing the **Latitude/Longitude**
station <- data.frame(lat = c(7.16, 8.6, 8.43, 8.15),
long = c(124.21, 123.35, 124.28, 125.08),
station = 1:4)
# Convert to SpatialPointsDataFrame
coordinates(station) <- ~long + lat
# Set the projection. They were latitude and longitude, so use WGS84 long-lat projection
proj4string(station) <- CRS("+init=epsg:4326")
# View the station location using the mapview function
mapview(station)
The mapview function will create an interactive map. We can use this map to determine what could be a suitable for the origin of the grid.
3. Determine the origin
After inspecting the map, I decided that the origin could be around longitude 123 and latitude 7. This origin will be on the lower left of the grid. Now I need to find the coordinate representing the same point under Spherical Mercator projection.
# Set the origin
ori <- SpatialPoints(cbind(123, 7), proj4string = CRS("+init=epsg:4326"))
# Convert the projection of ori
# Use EPSG: 3857 (Spherical Mercator)
ori_t <- spTransform(ori, CRSobj = CRS("+init=epsg:3857"))
I first created a SpatialPoints object based on the latitude and longitude of the origin. After that I used the spTransform to perform project transformation. The object ori_t now is the origin with Spherical Mercator projection. Notice that the EPSG code for Spherical Mercator is 3857.
To see the value of coordinates, we can use the coordinates function as follows.
coordinates(ori_t)
coords.x1 coords.x2
[1,] 13692297 781182.2
4. Determine the extent of the grid
Now I need to decide the extent of the grid that can cover all the four points and the desired area for kriging, which depends on the cell size and the number of cells. The following code sets up the extent based on the information. I have decided that the cell size is 1 km * 1 km, but I need to experiment on what would be a good cell number for both x- and y-direction.
# The origin has been rounded to the nearest 100
x_ori <- round(coordinates(ori_t)[1, 1]/100) * 100
y_ori <- round(coordinates(ori_t)[1, 2]/100) * 100
# Define how many cells for x and y axis
x_cell <- 250
y_cell <- 200
# Define the resolution to be 1000 meters
cell_size <- 1000
# Create the extent
ext <- extent(x_ori, x_ori + (x_cell * cell_size), y_ori, y_ori + (y_cell * cell_size))
Based on the extent I created, I can create a raster layer with number all equal to 0. Then I can use the mapview function again to see if the raster and the four stations matches well.
# Initialize a raster layer
ras <- raster(ext)
# Set the resolution to be
res(ras) <- c(cell_size, cell_size)
ras[] <- 0
# Project the raster
projection(ras) <- CRS("+init=epsg:3857")
# Create interactive map
mapview(station) + mapview(ras)
I repeated this process several times. Finally I decided that the number of cells is 250 and 200 for x- and y-direction, respectively.
5. Create spatial grid
Now I have created a raster layer with proper extent. I can first save this raster as a GeoTiff for future use.
# Save the raster layer
writeRaster(ras, filename = "ras.tif", format="GTiff")
Finally, to use the kriging functions from the package gstat, I need to convert the raster to SpatialPixels.
# Convert to spatial pixel
st_grid <- rasterToPoints(ras, spatial = TRUE)
gridded(st_grid) <- TRUE
st_grid <- as(st_grid, "SpatialPixels")
The st_grid is a SpatialPixels that can be used in kriging.
This is an iterative process to determine a suitable grid. Throughout the process, users can change the projection, origin, cell size, or cell number depends on the needs of their analysis.
#yzw and #Edzer bring up good points for creating a regular rectangular grid, but sometimes, there is the need to create an irregular grid over a defined polygon, usually for kriging.
This is a sparsely documented topic. One good answer can be found here. I expand on it with code below:
Consider the the built in meuse dataset. meuse.grid is an irregularly shaped grid. How do we make an grid like meuse.grid for our unique study area?
library(sp)
data(meuse.grid)
ggplot(data = meuse.grid) + geom_point(aes(x, y))
Imagine an irregularly shaped SpatialPolygon or SpatialPolygonsDataFrame, called spdf. You first build a regular rectangular grid over it, then subset the points in that regular grid by the irregularly-shaped polygon.
# First, make a rectangular grid over your `SpatialPolygonsDataFrame`
grd <- makegrid(spdf, n = 100)
colnames(grd) <- c("x", "y")
# Next, convert the grid to `SpatialPoints` and subset these points by the polygon.
grd_pts <- SpatialPoints(
coords = grd,
proj4string = CRS(proj4string(spdf))
)
# subset all points in `grd_pts` that fall within `spdf`
grd_pts_in <- grd_pts[spdf, ]
# Then, visualize your clipped grid which can be used for kriging
ggplot(as.data.frame(coordinates(grd_pts_in))) +
geom_point(aes(x, y))
If you have your study area as a polygon, imported as a SpatialPolygons, you could either use package raster to rasterize it, or use sp::spsample to sample it using sampling type regular.
If you don't have such a polygon, you can create points regularly spread over a rectangular long/lat area using expand.grid, using seq to generate a sequence of long and lat values.
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.