I generate a raster map in R with some shaded portion, then i plot my shape file on the raster file to show boundaries of the map. I can calculate the the overall shaded area with a code but I want to calculate the shaded region coming under the separate polygons when i plot shape file on raster. Please help me with the code.
I am using maxent in R to have an idea of suitable area of certain crop for whole country. when I generate map, it is a raster file and I can calculate suitable area for whole country with a code, but I want to calculate the area for provinces as well for which i plot province vise shape file on the raster map.
I want help with the area calculation for each shaded polygon when i plot shape file on raster
pred_me2 [pred_me2 <=0.33] <- NA
pred_me2 [pred_me2 >0.66] <- NA
cell_size<-area (pred_me2, na.rm=TRUE, weights=FALSE)
cell_size<-cell_size[!is.na (cell_size)]
suitable<-length (cell_size)*median(cell_size)
You can try with this:
cell_size <- xres(pred_me2)*yres(pred_me2)
area_NA<- sum(is.na(values(pred_me2))) * cell_size
area_non_NA <- sum(!is.na(values(pred_me2))) * cell_size
Related
I have a bunch of (x,y) coordinates that I obtained by thresholding a 3D plot. When I plot the coordinates I get this surface:
These points are densely packed (the area in the middle is not colour fill, it's all points) and have an irregular shape. How do I obtain the boundary of this surface?
I tried saving the points as a SpatialPolygon object but that included the points in the middle. I want to only extract the boundary somehow and save that as a polygon.
Can I save these points as SpatialPoints and somehow dissolve the points in the middle?
My overall aim is to combine multiple shape files (polygons of river sub-basins from within a large river basin) into one file and plot as a map. This new combined file will later combine with variable data e.g.(rainfall) and plot by aes().
My problem is:
ggplot()+geom_sf() plots the correct shapes of the polygons but doesn't have the correct co-ordinates on the axes - it doesn't use the values given in the geometry column on the axes.
My thoughts on what is wrong, but I'm not sure how to correct:
The shape file read in has geometry in 'long' 'lat' (crs= 4326) but the crs is saying the coordinates are in UTM Zone 48N WGS84 (crs=32648). If I try and force the crs to 4326 the coordinate values change as if the conversion formula is trying to correct them.
geom_sf and coord_sf are doing something that I don't understand!
.
library(sp)
library(raster)
library(ggplot2)
library(sf)
library(ggsf)
library(rgdal)
library(plyr)
library(dplyr)
library(purrr)
setwd("/Users/.../Sub_Basin_Outlines_withSdata/")
list.files('/Users/.../Sub_Basin_Outlines_withSdata/', pattern='\\.shp$')
Read in individual polygon shape files from folder. Combine with ID.
bangsai <- st_read("./without_S_data/", "Nam Bang Sai")
BasinID <- "BGS"
bangsai <- cbind(bangsai,BasinID)
ing <- st_read("./without_S_data/", "Nam Ing Outline")
BasinID <- "ING"
The two individual shape files import as simple features, see image of R code
Combine the individual sub-basin polygon shape files into one shapefile with multiple features.
all_sub_basins <- rbind(bangsai,ing)
The image shows the values of the coordinates of the polygons/features in all_sub_basins$geometry. They are long lat format yet the proj4sting suggests UTM?
Plot the all_sub_basins simple feature shapefile in ggplot
subbasins <- ggplot()+
geom_sf(data=all_sub_basins, colour="red", fill=NA)
subbasins
The result is a correctly plotted shape file with multiple features (there are more polygons in this image than read in above). However the axes are incorrect (nonsense values) and are not plotting the same values as in the geometry field.
If I add in coord_sf and confirm the crs:
subbasins <- ggplot() +
geom_sf(data=all_sub_basins, colour="red", fill=NA)
coord_sf(datum=st_crs(32648), xlim = c(94,110), ylim = c(9,34))
subbasins
Then I get the Correct axes values but not as coordinates with N and E. It seems as if the geometry isn't recognised as coordinates, just as forced numbers?
I don't mind if the coordinates are UTM Zone 48N or lat long. Could I fix it in any of these ways? If so, how do I achieve that?
Change the shape file crs without changing the values in the geometry column so geom_sf would know to plot the correct axes text.
Extract the geometry from the shape file into a two column .csv file with long and lat columns. Convert csv into a sf and create my own shape file with correct crs.
Last resort, leave the plot as it is and replace new axes text manually.
Any help is much appreciated!
I have a raster r, one polygon shapefile regions and a point shapefile cities. I need to plot all three into one map layout. In addition to this I need to label point file with names of cities (cities$city$Town.Name) and their temperature and precipitation value (assigned as cities$labels). So I have used the following code with packages 'raster' and 'rasterVis'.
p1<-levelplot(regions.r,par.settings=mytheme,scales=list(draw=FALSE),xlab="",ylab="",margin=F)+
layer(sp.polygons(regions))+
layer(sp.points(cities,pch=20,cex=1.5,col="black"))
p1+
layer(sp.text(coordinates(cities), txt = cities$city$Town.Name, pos = 3,col="black",font=list(face="bold"),cex=0.8))+
layer(sp.text(coordinates(cities),txt = cities$label,
pos = 1,cex=0.6,col="black"))#Add shapefile labels
This works fine when area has scattered cities distribution (see figure below).
However, if the cities are concentrated in one part I experience overlap of labels (see figure below). Is there a way to avoid the label overlap?
it's my first time using the spatstat package, so I would like some advice. I am attempting to plot coordinate data into a irregular polygon area (format .shp), to calculate spatial analysis like Ripley's K. How can I add an irregular polygon area as a plot? How can I merge the .ppp data from the coordinates into the polygon area?
I have used the following codes:
Converting the coordinate data to .ppp format
library(spatstat)
library(sp)
library(maptools)
tree.simu <- read.table("simulation.txt", h=T)
tree.simu.ppp <-ppp(x=tree.simu$X,y=tree.simu$Y,window=owin(c(min(tree.simu$X),max(tree.simu$X)),c(min(tree.simu$Y),max(tree.simu$Y))))
plot(tree.simu.ppp)
With this function I am considering the plot area as the max and min valeu of the coordinates. I would like to put the polygon boundary as the plot.
Ploting the irregular polygon area
area <- readShapePoly("Area/Fragment.shp")
plot(area)
plot(tree.simu.ppp, add=T)
or
points(tree.simu.ppp)
The package accept the last function but, when I try to plot both files together, seems like that the .shp file it is fill the whole area. I can't visualize the coordinates data.
Thank you, I really appreciate your help!
ps.: If you know any material with those question, please I would be happy to take a look
This is indeed due to inconsistent bounding boxes as conjectured in the comment by #jlhoward. Your points are in [273663.9, 275091.45] x [7718635, 7719267] while the polygon is contained in [-41.17483, -41.15588] x [-20.619647, -20.610134].
Assuming the coordinates were indeed consistent with the window the correct way way of getting it into a ppp object would be:
library(spatstat)
library(sp)
library(maptools)
area <- readShapePoly("Area/Fragment.shp")
area <- as(area, "owin")
tree.simu <- read.table("simulation.txt", h=T)
tree.simu.ppp <-ppp(x=tree.simu$X,y=tree.simu$Y,window=area)
However, you will get a warning about your points being rejected since they are outside the window, and the object will contain no points.
I've been running into all sorts of issues using ArcGIS ZonalStats and thought R could be a great way. Saying that I'm fairly new to R, but got a coding background.
The situation is that I have several rasters and a polygon shape file with many features of different sizes (though all features are bigger than a raster cell and the polygon features are aligned to the raster).
I've figured out how to get the mean value for each polygon feature using the raster library with extract:
#load packages required
require(rgdal)
require(sp)
require(raster)
require(maptools)
# ---Set the working directory-------
datdir <- "/test_data/"
#Read in a ESRI grid of water depth
ras <- readGDAL("test_data/raster/pl_sm_rp1000/w001001.adf")
#convert it to a format recognizable by the raster package
ras <- raster(ras)
#read in polygon shape file
proxNA <- readShapePoly("test_data/proxy/PL_proxy_WD_NA_test")
#plot raster and shp
plot(ras)
plot(proxNA)
#calc mean depth per polygon feature
#unweighted - only assigns grid to district if centroid is in that district
proxNA#data$RP1000 <- extract(ras, proxNA, fun = mean, na.rm = TRUE, weights = FALSE)
#check results
head(proxNA)
#plot depth values
spplot(proxNA[,'RP1000'])
The issue I have is that I also need an area based ratio between the area of the polygon and all non NA cells in the same polygon. I know what the cell size of the raster is and I can get the area for each polygon, but the missing link is the count of all non-NA cells in each feature. I managed to get the cell number of all the cells in the polygon proxNA#data$Cnumb1000 <- cellFromPolygon(ras, proxNA)and I'm sure there is a way to get the actual value of the raster cell, which then requires a loop to get the number of all non-NA cells combined with a count, etc.
BUT, I'm sure there is a much better and quicker way to do that! If any of you has an idea or can point me in the right direction, I would be very grateful!
I do not have access to your files, but based on what you described, this should work:
library(raster)
mask_layer=shapefile(paste0(shapedir,"AOI.shp"))
original_raster=raster(paste0(template_raster_dir,"temp_raster_DecDeg250.tif"))
nonNA_raster=!is.na(original_raster)
masked_img=mask(nonNA_raster,mask_layer) #based on centroid location of cells
nonNA_count=cellStats(masked_img, sum)