How to convert a single spatial point to a raster object.
I want to create a raster of 0.1 resolution within the domain xmin = 150 xmx=180,ymin=25,ymax=35 and put the values of a datapoint dt point in the raster.
Here is the data and code.
dt<-data.frame(lon=71.85,lat =31.12,val=3)
dt
ras_dom<-raster(xmn=150, xmx=180, ymn=25, ymx=35,
crs="+proj=longlat +datum=WGS84 +no_defs ",
resolution=c(0.1,0.1), vals=NA)
ras_dom
I want to put the value (or values) from dt in the ras_dom so that I have a raster with values for given grids and NA for others.
library(raster)
coordinates(dt) <- ~ lon + lat # Convert data frame to spatial object
result <- rasterize(dt, ras_dom, "val", update = TRUE) # put point in raster
result # The resulting raster with the point added
Because dt in the example is outside the raster, result does not contain it.
Related
I need help with converting a variable or column values in a spatial polygon into a raster image. I have spatial data of administrative units with income(mean) information for each unit. I want to convert this information into raster for further analysis.
I tried the code below but it didn't work.
r <- raster(ncol=5,nrow=15)
r.inc <- rasterize(DK,r,field=DK#data[,2],fun=mean)
Where SP is the spatial polygon and the mean income for each spatial unit stored in column 2 of the SpatialPolygonDataframe. Can anyone help with a function or code of how to rasterise the values in the column of interest? An example of the spatialpolygondataframe (created) and my attempt to rasterize the data are below
suppressPackageStartupMessages(library(tidyverse))
url = "https://api.dataforsyningen.dk/landsdele?format=geojson"
geofile = tempfile()
download.file(url, geofile)
DK <- rgdal::readOGR(geofile)
DK#data = subset(DK#data, select = c(navn))
DK#data$inc = runif(11, min=5000, max=80000)
require(raster)
r <- raster(ncol=5,nrow=15)
r.inc <- rasterize(DK,r,field=DK#data[,2],fun=mean)
plot(r.inc)
Thank you.
Acknowledgement: The code for creating the sample SPDF was sourced from Mikkel Freltoft Krogsholm (link below).
https://www.linkedin.com/pulse/easy-maps-denmark-r-mikkel-freltoft-krogsholm/?trk=read_related_article-card_title
Here's something that makes a raster.
library(tidyverse)
library(rgdal)
library(raster)
url <- "https://api.dataforsyningen.dk/landsdele?format=geojson"
geofile <- tempfile()
download.file(url, geofile)
DK <- rgdal::readOGR(geofile)
r_dk <- raster(DK, nrows = 100, ncols = 100) # Make a raster of the same size as the spatial polygon with many cells
DK$inc <- runif(nrow(DK), min=5000, max=80000) # Add some fake income data
rr <- rasterize(DK, r_dk, field='inc') # Rasterize the polygon into the raster - fun = 'mean' won't make any difference
plot(rr)
The original raster was the size of the whole Earth so I think Denmark was being averaged to nothing. I resolved this by making an empty raster based on the extent of the DK spatial polygons with 100x100 cells. I also simplified the code. Generally, if you find yourself using # with spatial data manipulation, it's a sign that there might be a simpler way. Because the resolution of the raster is much larger than the size of each DK region, taking the average doesn't make much difference.
I am trying to calculate average annual temperatures for grid cells of 11x11km (except if the cell is coastal, the size is smaller) using the CRU database. The CRS of both vector and raster are the same. However, 332 out of 1363 cells show NA values after the extraction. I want to fill in the NA values before using the dataset for further analysis. Any idea of how I could deal with these missing values? I have looked at several possible solutions on this forum (and others). Unfortunately, none of them don’t seem to apply to my case.
Below are the details of my workflow:
# load the temperature dataset
temp <- brick("/CRU/cru_ts4.02.1901.2017.tmp.dat.nc", varname="tmp")
# set CRS for temp
utm = "+proj=utm +zone=49 +datum=WGS84 +towgs84=0,0,0"
tempro = projectRaster(temp, crs = utm, method = "bilinear")
# load the grid cells (in polygons) & set its CRS
fish <- st_read("/CRU/fish11.shp")
fishpro <- st_transform(fish, "+proj=utm +zone=49 +datum=WGS84 +towgs84=0,0,0")
# extract the temperature dataset
tempgrid <- extract(tempro, fishpro, fun='mean', na.rm=TRUE, df=TRUE, weights = TRUE, small = TRUE,
method='bilinear')
write.csv(tempgrid, file="temp.csv")
whereas the map is:
temperature
I do not think there is a simple answer to your question. Apparently the polygons are not over land; but we cannot tell as we do not have your data. It could also be that the UTM zone chosen is not appropriate.
I can say that what you are doing is wrong. If you need to transform the data; you should transform the vector data, not the raster data (even if that should not affect the NA problem much, if at all).
library(raster)
temp <- brick("/CRU/cru_ts4.02.1901.2017.tmp.dat.nc", varname="tmp")
fish <- st_read("/CRU/fish11.shp")
fishpro <- st_transform(fish, "+proj=longlat +datum=WGS84")
tempgrid <- extract(temp, fishpro, fun='mean', na.rm=TRUE, df=TRUE, small = TRUE)
You could also make a map to see what is going on (and perhaps include that as an image in your quesiton.
x <- crop(temp[[1]], extent(fishpro)+1)
plot(x)
lines(fishpro)
I've got a map with certain number of points on it. I want to (1) calculate the number of points that fall within the raster layer, and (2) extract these points to a data frame.
This is what I've done:
# Packages
library(raster)
library(ggplot2)
library(maptools)
library(tidyverse)
library(dplyr)
library(sp)
# Transform tree ring kml to dataframe
zz<-getKMLcoordinates('treering.kml', ignoreAltitude=TRUE)
l<-as.data.frame(zz)
l<-t(l)
tree <-SpatialPointsDataFrame(l, l,
proj4string = CRS(" +proj=longlat +ellps=WGS84 +datum=WGS84
+no_defs +towgs84=0,0,0"))
# Get world map
data(wrld_simpl)
# Transform World to raster
r <- raster(wrld_simpl, res = 1)
wrld_r <- rasterize(wrld_simpl, r)
# Import permafrost layer to raster
dist1<-raster("PZI.flt")
# Set CRS
dist1 <- projectRaster(from = dist1, crs = CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs
+towgs84=0,0,0"))
# Change colours
micolor <- rev(rainbow(12, alpha = 0.35))
transp <- rainbow(12, alpha = 0)
micolor[1:3] <- transp[1]
# Plot all
plot(wrld_r, col = "lightgrey")
plot(dist1, add=TRUE, legend = F, col = micolor)
plot(tree, add=T, pch = 20, col='black', cex=0.2)
I want to calculate and extract black points located on the colorful parts of this map
First raster::projectRaster does not "set" the projection but, rather reprojects the raster given a transformation and resampling. Given the computational requirements of this it is much faster to reproject the point data using sp::spTransform. Once your data is in the same projection space, you can use raster::extract to extract the raster values. Values out side the raster or in nodata (NA) areas will be assigned NA values. You can drop these observations using a simple NA index with which.
It looks like your data may have a constant value outside of the permafrost. Once you identify what this value is (eg., 0) you can remove these points as well. Here is a worked example. First we add packages and create some example data that is similar to yours.
library(sp)
library(raster)
dist1 <- raster(nrow=20, ncol=20)
dist1[] <- sample(1:10, ncell(dist1), replace=TRUE)
dist1[200:400] <- 0
trees <- sampleRandom(dist1, 100, sp=TRUE)
plot(dist1)
plot(trees,pch=20,col="red",add=TRUE)
Now, we extract the raster values and look at the dimensions of the point object (please note that I do not have to use the sp=TRUE argument in the raster::extract function).
trees#data <- data.frame(trees#data, dist1 = extract(dist1, trees))
dim(trees)
Now we create a row index indicating which rows contain zeros, make sure that we have identified rows (using an if statement) and then remove them. Looking at the object dimensions again, we can see how many points were removed from the original point data.
( idx <- which(trees$dist1 %in% 0) )
if(length(idx) > 0) trees <- trees[-idx,]
dim(trees)
I have a grid of the map of Porto, I would like to be able to insert in the grid the points to discretize them. In this way to see how many points are in each cell depending on the time.
This is the code of the grid:
#latitudS and longitudS are coordinates x Y
x = longitudS
y = latitudS
df = data.frame(x, y)
# Converting to a Spatial Points Data Frame so you can keep the value that you will change within the Grid
points = SpatialPointsDataFrame(coords = df[, 1:2], df)
# load some spatial data. Administrative Boundary
porto <- getData('GADM', country = 'Portugal', level = 2)
porto$NAME_1
porto <- porto[porto$NAME_2 == "Porto",]
# check the CRS to know which map units are used
proj4string(porto)
# "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
# Create a grid of points within the bbox of the SpatialPolygonsDataFrame
# colorado with decimal degrees as map units
grid <- makegrid(porto, cellsize = 0.003) # cellsize in map units!
# grid is a data.frame. To change it to a spatial data set we have to
grid <- SpatialPoints(grid, proj4string = CRS(proj4string(porto)))
portoWithin <-
SpatialPixels(grid, proj4string = CRS(proj4string(porto)))
#### Converting the GRID to a Raster
ras_portoWithin <- raster(portoWithin)
## Assigning temporary values to the raster
values(ras_portoWithin) <- 0
## Using the Spatial point file created above to replace the values of the raster
r1 <- ras_portoWithin
plot(porto)
plot(portoWithin, add = T)
The idea is that for each cell the number of points will be output depending on the time in which the points have been recorded.
Also ask if there would be any way to transfer that grid to a leaflet, so you can see it on a map.
Thanks!
I am trying to create a script that will generate a 2d topographic or contour map for a given set of coordinates. My goal is something similar to what is produced by
contour(volcano)
but for any location set by the user. This has proved surprisingly challenging! I have tried:
library(elevatr)
library(tidyr)
# Generate a data frame of lat/long coordinates.
ex.df <- data.frame(x=seq(from=-73, to=-71, length.out=10),
y=seq(from=41, to=45, length.out=10))
# Specify projection.
prj_dd <- "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"
# Use elevatr package to get elevation data for each point.
df.sp <- get_elev_point(ex.df, prj = prj_dd, src = "epqs")
# Convert from spatial to regular data frame, remove extra column.
# Use tidyr to convert to lat x lon table with elevation as fill.
# Sorry for the terrible code, I know this is sloppy.
df <- as.data.frame(df.sp)
df$elev_units <- NULL
df.w <- df %>% spread(y, elevation)
df.w <- as.matrix(df.w)
This creates a matrix similar to the volcano dataset but filled with NAs except for the 10 lat/lon pairs with elevation data. contour can handle NAs, but the result of contour(df.w) has only a single tiny line on it. I'm not sure where to go from here. Do I simply need more points? Thanks in advance for any help--I'm pretty new to R and I think I've bitten off more than I can chew with this project.
Sorry for delay in responding. I suppose I need to check SO for elevatr questions!
I would use elevatr::get_elev_raster(), which returns a raster object which can be plotted directly with raster::contour().
Code example below grabs a smaller area and at a pretty coarse resolution. Resultant contour looks decent though.
library(elevatr)
library(raster)
# Generate a data frame of lat/long coordinates.
ex.df <- data.frame(x=seq(from=-73, to=-72.5, length.out=10),
y=seq(from=41, to=41.5, length.out=10))
# Specify projection.
prj_dd <- "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"
# Use elevatr package to get elevation data for each point.
elev <- get_elev_raster(ex.df, prj = prj_dd, z = 10, clip = "bbox")
raster::contour(elev)
If it is a requirement to use graphic::contour(), you'll need to convert the raster object to a matrix first with raster::as.matrix(elev). That flips the coords though and I haven't spent enough time to try and get that part figured out... Hopefully the raster solution works for you.