I am now doing exploratory analysis and the objective is to plot a quadrat map, determine if there exists complete spatial randomness (visually and using chi-square test), etc. However, I am having trouble plotting a quadrat map. I previously asked this question that led me to reproject my data.
Here is my code:
library(rgdal) #Brings Spatial Data in R
library(spatstat) # Spatial Statistics
library(maptools)
library(raster)
# Load nyc zip code boundary polygon shapefile
s <- readOGR("/Users/my_name/Documents/fproject/zip","zip")
nyc <- as(s,"owin")
### OGR data source with driver: ESRI Shapefile
#Source: "/Users/my_name/Documents/project/zip", layer: "zip"
#with 263 features
# Load nyc arrests point feature shapefile
s <- readOGR("/Users/my_name/Documents/project/nycarrests/","geo1")
### OGR data source with driver: ESRI Shapefile
#Source: "/Users/my_name/Documents/project/nycarrests", layer: "geo1"
#with 103376 features
#It has 19 fields
#Converting the dataset into a point pattern
utm <- "+proj=utm +zone=32 +datum=WGS84"
x <- spTransform(s,utm)
arrests <- as(x,"ppp")
marks(arrests) <- NULL
Window(arrests) <- nyc
plot(arrests, main="2020", cols="dark green",pch=20)
### Quadrat density 2020
Q <- quadratcount(arrests, nx= 3, ny=3)
plot(arrests, pch=20, cols="grey70", main="2020") # Plot points
plot(Q, main="2020", add=TRUE) # Add quadrat grid
Here, my data points are constrained by NYC zip code boundaries, but they do not show up on my map. What can I do to fix this?
You are not providing much information. For example you could show (print) s (each time), or crs(s) (both with the raster package loaded)
Either way, it is almost certainly because the coordinate reference systems you are using are not the same. You change one to "UTM zone 21", but you do not change the others. They are probably different. Also, zone 21 is not appropriate for New York.
Related
I have a set of bioclimatic rasters that I'd like to crop and change to match my template raster.
This means changing projection, cellsize (the template raster has a bigger cellsize) extent and NA cells so that each non-NA cell on the template raster has matching cell on the bioclimatic rasters.
I don't want to change any cell values (except for the necessary changes in upscaling cell size).
Can anyone suggest a workflow in R? I looked through the "raster" package but was unable to figure out a workflow of which I was 100% sure
I think what you are after is the resample() function in the raster package. Here is a reproducible example of the workflow to do this task. This first code chunk is mostly to set up the data for reproducibility and to get the specific scenario you are asking about:
##Loading Necessary Packages##
library(raster)
library(tmap)
library(sf)
library(sp)
library(spData)
##Getting Example Datasets##
data("land")
data("us_states")
##Converting the Elevation layer in the "land" dataset to a rasterLayer##
Elev<-raster(xmx=180, xmn=-180, ymx=90, ymn=-90, t(land$elevation), crs=crs("+init=epsg:4326"))
##Looking at the dataset##
tmap_mode("plot")
tm_shape(Elev)+
tm_raster(palette = terrain.colors(10))+
tm_layout(legend.outside = TRUE)
##Getting necessary projections for spatial transformations##
WGS84<-CRS("+init=epsg:4326")#World Geographical System of 1984
ORLAMB<-CRS("+init=epsg:2992")#Oregon Statewide Lambert Projection
##Creating a fake raster for the US state of Oregon##
OR<-as_Spatial(us_states[us_states$NAME=="Oregon",])
OR_sp<-spTransform(OR, CRSobj=WGS84)
OR_lamb<-spTransform(OR, CRSobj=ORLAMB)
EXT<-extent(OR_lamb)
OR_blank<-raster(EXT, resolution=c(3000,3000), crs=ORLAMB)
values(OR_blank)<-runif(369238, 100, 200)
Now we have the specific scenario you are after we have a big world wide elevation data set projected in to WGS84, and a finer resolution fake raster for the US state of Oregon. The below portion is the main part of the workflow you are asking about
OR_ELEV_tmp<-crop(Elev, OR_sp) #Crop the worldwide elevation data to the state of ORegon
ORELEVLAMB<-projectRaster(OR_ELEV_tmp, crs=ORLAMB)#Project into Oregon Statewide Lambert
ELEV_rsmpld<-mask(resample(ORELEVLAMB, OR_blank),OR_lamb)#Resample to the resolution of our fake Oregon raster and mask to the shape of the state
##View the final output
tmap_mode("plot")
tm_shape(ELEV_rsmpld)+
tm_raster(palette = terrain.colors(10))+
tm_layout(legend.outside = TRUE)
1. The problem
I'm trying to extract the intersection of two polygons shapes in R. The first is the watershed polygon "ws_polygon_2", and the second is the Voronoi polygons of 5 rain gauges which was constructed from the Excel sheet "DATA.xlsx", both available here: link.
The code is the following:
#[1] Montagem da tabela de coordenadas dos postos pluviométricos
library(sp)
library(readxl)
dados_precipitacao_1985 <- read_excel(path="C:/Users/.../DATA.xlsx")
coordinates(dados_precipitacao_1985) <- ~ x + y
proj4string(dados_precipitacao_1985) <- CRS("+proj=longlat +datum=WGS84")
d_prec <- spTransform(dados_precipitacao_1985, CRSobj = "+init=epsg:3857")
#[2] Coleta dos dados espaciais da bacia hidrográfica
library(rgdal)
bacia_Caio_Prado <- readOGR(dsn="C:/Users/...", layer="ws_polygon_2")
bacia_WGS <- spTransform(bacia_Caio_Prado, CRSobj = "+proj=longlat +datum=WGS84")
bacia_UTM <- spTransform(bacia_Caio_Prado, CRSobj = "+init=epsg:3857")
#[3] Poligonos de Thiessen - 1 INTERPOLAÇÃO
library(dismo)
library(rgeos)
library(raster)
library(mapview)
limits_voronoi_WGS <- c(-40.00,-38.90,-5.00,-4.50)
v_WGS <- voronoi(dados_precipitacao_1985, ext=limits_voronoi_WGS)
bc <- aggregate(bacia_WGS)
u_WGS_1 <- gIntersection(spgeom1 = v_WGS, spgeom2 = bc,byid=TRUE)
u_WGS_2 <- intersect(bc, v_WGS)
When I apply the intersect function, the variable returned u_WGS_2 is a spatial polygon data frame with only 4 features, instead of 5. The Voronoi object v_WGS has 5 features as well.
By other hand, when I apply the gIntesection function, I get 5 features. However, the u_WGS_1 object is a spatial polygon only and I loss the rainfall data.
I'd like to know if I am committing any mistake or if there is any way to get the 5 features aggregated with the rainfall data in a spatial polygon data frame through the intersect function.
My objective is to transform this spatial polygon data frame with the rainfall data for each Voronoi polygon in a raster through the rasterize function later to compare with other interpolating results and satellite data.
Look these results. The first one is when I get the SPDF (Spatial Polygon Data Frame) I want, but missing the 5º feature. The second is the one I get with all the features I want, but missing the rainfall data.
spplot(u_WGS_2, 'JAN')
plot(u_WGS_1)
2. What I've tried
I look into the ws_polygon_2 shape searching for any other unwanted polygon who would pollute the shape and guide to this results. The shape is composed by only one polygon feature, the correct watershed feature.
I tried to use the aggregate function, as above, and as I saw in this tutorial. But I got the same result.
I tried to create a SPDF with de u_WGS_1 and the d_precSpatial Point Data Frame object. Actually, I'm working on it. And if it is the correct answer to my trouble, please help me with some code.
Thank you!
This is not an issue when using st_intersection() from sf, which retains the data from both data sets. Mind that dismo::voronoi() is compatible with sp objects only, so the precipitation data needs to be available in that format, at least temporarily. If you do not feel comfortable with sf and prefer to continue working with Spatial* objects after the actual intersection, simply invoke the as() method upon the output sf object as shown below.
library(sf)
#[1] Montagem da tabela de coordenadas dos postos pluviométricos
dados_precipitacao_1985 <- readxl::read_excel(path="data/DATA.xlsx")
dados_precipitacao_1985 <- st_as_sf(dados_precipitacao_1985, coords = c("x", "y"), crs = 4326)
dados_precipitacao_1985_sp <- as(dados_precipitacao_1985, "Spatial")
#[2] Coleta dos dados espaciais da bacia hidrográfica
bacia_Caio_Prado <- st_read(dsn="data/SHAPE_CORRIGIDO", layer="ws_polygon_2")
#[3] Poligonos de Thiessen - 1 INTERPOLAÇÃO
limits_voronoi_WGS <- c(-40.00,-38.90,-5.00,-4.50)
v_WGS <- dismo::voronoi(dados_precipitacao_1985_sp, ext=limits_voronoi_WGS)
v_WGS_sf <- st_as_sf(v_WGS)
u_WGS_3 <- st_intersection(bacia_Caio_Prado, v_WGS_sf)
plot(u_WGS_3[, 6], key.pos = 1)
The missing polygon is removed because it is invalid
library(raster)
bacia <- shapefile("SHAPE_CORRIGIDO/ws_polygon_2.shp")
rgeos::gIsValid(bacia)
#[1] FALSE
#Warning message:
#In RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid") :
# Ring Self-intersection at or near point -39.070555560000003 -4.8419444399999998
The self-intersection is here:
zoom(bacia, ext=extent(-39.07828, -39.06074, -4.85128, -4.83396))
points(cbind( -39.070555560000003, -4.8419444399999998))
Invalid polygons are removed as they are assumed to have been produced by intersect. In this case, the invalid data was already there and should have been retained. I will see if I can fix that.
Rworldmap looks like exactly what I need for mapping climate data, but I'm having a problem lining up the base map with the climate data. What I am mapping is ocean temperature data from JAMSTEC for August, 2015 from here:
http://www.jamstec.go.jp/ARGO/argo_web/ancient/MapQ/Mapdataset_e.html
The dataset name is TS_201508_GLB.nc. The R script I'm using is below. The country outlines are fine, but the data is for the oceans only and the data does not show in the oceans it is offset somehow. Can you tell me how to align the data to the map?
I've read lots of articles but I cannot tell how to align the two. The data I have is longitude and latitude. South latitude is negative and west longitude is negative, I don't see how they could be confused. How is the map shown, is there some sort of special convention for the lat/longs?
Thanks for any help you can provide.
The code:
library(RNetCDF)
library(sp)
library(rworldmap)
library(rgeos)
library(RColorBrewer)
library (classInt)
library(grid)
library(spam)
library(maps)
library(maptools)
library(fields)
library(methods)
library(rgdal)
library(rworldxtra)
fname <- "G:/Climate_Change/Ocean_Warming/MOAA_GPV_Jamstec_Temperature/TS_201508_GLB.nc"
moaa <- open.nc(fname)
# moaa
print.nc(moaa)
file.inq.nc(moaa)
#TOI is the temperature array extracted from the NCDF file
TOI = var.get.nc(moaa,"TOI",start=c(1,1,1),count=c(360,132,25))
TOI[1,1,1]
Long = var.get.nc(moaa,"LONGITUDE")
Lat = var.get.nc(moaa, "LATITUDE")
Pres = var.get.nc(moaa,"PRES")
# create grid
offset=c(-179.5,-60.50)
cellsize = c(abs(Long[1]-Long[2]),abs(Lat[1]-Lat[2]))
cells.dim = c(dim(Long), dim(Lat))
# create gt
gt <- GridTopology(cellcentre.offset=offset,cellsize=cellsize,cells.dim=cells.dim)
# create map window
mapDevice()
# Create a color pallette
colourPalette=c('blue','lightblue','white',brewer.pal(9,'YlOrRd'))
# Values at 2000 decibar for August 2015
ncMatrix <- TOI[,,25]
# Gridvalues
gridVals <-data.frame(att=as.vector(ncMatrix))
# create a spatialGridDataFrame
sGDF <-SpatialGridDataFrame(gt,data=gridVals)
# Vector to classify data
catMethod=seq(from=0,to=4,by=.33)
# plotting the map and getting params for legend
mapParams <- mapGriddedData( sGDF, nameColumnToPlot='att',catMethod=catMethod,colourPalette=colourPalette,addLegend=FALSE)
I finally figured it out. rworldmap wants the data organized from the upper left of the map(Northwest corner), that is Long = -180, Lat=90. The NetCDF data starts at Long=0 and Lat=-90(the middle of the map and south edge). So we have to reverse the values in the North-South direction:
#
# Flip the Latitude values so south is last
ncMatrix2 <- ncMatrix[,dim(Lat):1]
Then switch the values for east longitude and west longitude:
#
#Longitude values need to be from -180 to 0 then 0 to 180
# So we divide into East and West, then recombine with rbind
East_Long_values <-ncMatrix2[1:180,]
West_Long_Values <-ncMatrix2[181:360,]
ncMatrix3 <- rbind(West_Long_Values,East_Long_values)
Then everything else works.
I have the longitude and latitude of 5449 trees in NYC, as well as a shapefile for 55 different Neighborhood Tabulation Areas (NTAs). Each NTA has a unique NTACode in the shapefile, and I need to append a third column to the long/lat table telling me which NTA (if any) each tree falls under.
I've made some progress already using other point-in-polygon threads on stackoverflow, especially this one that looks at multiple polygons, but I'm still getting errors when trying to use gContains and don't know how I could check/label each tree for different polygons (I'm guessing some sort of sapply or for loop?).
Below is my code. Data/shapefiles can be found here: http://bit.ly/1BMJubM
library(rgdal)
library(rgeos)
library(ggplot2)
#import data
setwd("< path here >")
xy <- read.csv("lonlat.csv")
#import shapefile
map <- readOGR(dsn="CPI_Zones-NTA", layer="CPI_Zones-NTA", p4s="+init=epsg:25832")
map <- spTransform(map, CRS("+proj=longlat +datum=WGS84"))
#generate the polygons, though this doesn't seem to be generating all of the NTAs
nPolys <- sapply(map#polygons, function(x)length(x#Polygons))
region <- map[which(nPolys==max(nPolys)),]
plot(region, col="lightgreen")
#setting the region and points
region.df <- fortify(region)
points <- data.frame(long=xy$INTPTLON10,
lat =xy$INTPTLAT10,
id =c(1:5449),
stringsAsFactors=F)
#drawing the points / polygon overlay; currently only the points are appearing
ggplot(region.df, aes(x=long,y=lat,group=group))+
geom_polygon(fill="lightgreen")+
geom_path(colour="grey50")+
geom_point(data=points,aes(x=long,y=lat,group=NULL, color=id), size=1)+
xlim(-74.25, -73.7)+
ylim(40.5, 40.92)+
coord_fixed()
#this should check whether each tree falls into **any** of the NTAs, but I need it to specifically return **which** NTA
sapply(1:5449,function(i)
list(id=points[i,]$id, gContains(region,SpatialPoints(points[i,1:2],proj4string=CRS(proj4string(region))))))
#this is something I tried earlier to see if writing a new column using the over() function could work, but I ended up with a column of NAs
pts = SpatialPoints(xy)
nyc <- readShapeSpatial("< path to shapefile here >")
xy$nrow=over(pts,SpatialPolygons(nyc#polygons), returnlist=TRUE)
The NTAs we're checking for are these ones (visualized in GIS): http://bit.ly/1A3jEcE
Try simply:
ShapeFile <- readShapeSpatial("Shapefile.shp")
points <- data.frame(long=xy$INTPTLON10,
lat =xy$INTPTLAT10,
stringsAsFactors=F)
dimnames(points)[[1]] <- seq(1, length(xy$INTPTLON10), 1)
points <- SpatialPoints(points)
df <- over(points, ShapeFile)
I omitted transformation of shapefile because this is not the main subject here.
I have a SpatialPolygonsDataFrame with 11589 objects of class "polygons". 10699 of those objects consists of exactly 1 polygon, however the rest of those objects consists of multiple polygons (2 to 22).
If an object of consists of multiple polygons, three scenarios are possible:
Sometimes, those additional polygons describe a "hole" in the geographic ara describe by the first polygon in the object of class "polygons".
Sometimes, those additional polygons describe additional geographic areas, i.e. the shape of the region is quite complex and described by putting together multiple parts.
Sometimes, it might be a mix of both, 1) and 2).
Stackoverflow helped me to plot such an spatial object properly (Plot spatial area defined by multiple polygons).
However, I am still not able to answer how to determine whether a point (defined by longitude/latitude) is in a polygon.
Below is my code. I tried to apply the function point.in.polygon in the sp package, but found no way how it could handle such an object which consists of multiple polygons/holes.
# Load packages
# ---------------------------------------------------------------------------
library(maptools)
library(rgdal)
library(rgeos)
library(ggplot2)
library(sp)
# Get data
# ---------------------------------------------------------------------------
# Download shape information from the internet
URL <- "http://www.geodatenzentrum.de/auftrag1/archiv/vektor/vg250_ebenen/2012/vg250_2012-01-01.utm32s.shape.ebenen.zip"
td <- tempdir()
setwd(td)
temp <- tempfile(fileext = ".zip")
download.file(URL, temp)
unzip(temp)
# Get shape file
shp <- file.path(tempdir(),"vg250_0101.utm32s.shape.ebenen/vg250_ebenen/vg250_gem.shp")
# Read in shape file
map <- readShapeSpatial(shp, proj4string = CRS("+init=epsg:25832"))
# Transform the geocoding from UTM to Longitude/Latitude
map <- spTransform(map, CRS("+proj=longlat +datum=WGS84"))
# Pick an geographic area which consists of multiple polygons
# ---------------------------------------------------------------------------
# Output a frequency table of areas with N polygons
nPolys <- sapply(map#polygons, function(x)length(x#Polygons))
# Get geographic area with the most polygons
polygon.with.max.polygons <- which(nPolys==max(nPolys))
# Get shape for the geographic area with the most polygons
Poly.coords <- map[which(nPolys==max(nPolys)),]
# Plot
# ---------------------------------------------------------------------------
# Plot region without Google maps (ggplot2)
plot(Poly.coords, col="lightgreen")
# Find if a point is in a polygon
# ---------------------------------------------------------------------------
# Define points
points_of_interest <- data.frame(long=c(10.5,10.51,10.15,10.4),
lat =c(51.85,51.72,51.81,51.7),
id =c("A","B","C","D"), stringsAsFactors=F)
# Plot points
points(points_of_interest$long, points_of_interest$lat, pch=19)
You can do this simply with gContains(...) in the rgeos package.
gContains(sp1,sp2)
returns a logical depending on whether sp2 is contained within sp1. The only nuance is that sp2 has to be a SpatialPoints object, and it has to have the same projection as sp1. To do that, you would do something like this:
point <- data.frame(lon=10.2, lat=51.7)
sp2 <- SpatialPoints(point,proj4string=CRS(proj4string(sp1)))
gContains(sp1,sp2)
Here is a working example based on the answer to your previous question.
library(rgdal) # for readOGR(...)
library(rgeos) # for gContains(...)
library(ggplot2)
setwd("< directory with all your files >")
map <- readOGR(dsn=".", layer="vg250_gem", p4s="+init=epsg:25832")
map <- spTransform(map, CRS("+proj=longlat +datum=WGS84"))
nPolys <- sapply(map#polygons, function(x)length(x#Polygons))
region <- map[which(nPolys==max(nPolys)),]
region.df <- fortify(region)
points <- data.frame(long=c(10.5,10.51,10.15,10.4),
lat =c(51.85,51.72,51.81,51.7),
id =c("A","B","C","D"), stringsAsFactors=F)
ggplot(region.df, aes(x=long,y=lat,group=group))+
geom_polygon(fill="lightgreen")+
geom_path(colour="grey50")+
geom_point(data=points,aes(x=long,y=lat,group=NULL, color=id), size=4)+
coord_fixed()
Here, point A is in the main polygon, point B is in a lake (hole), point C is on an island, and point D is completely outside the region. So this code checks all of the points using gContains(...)
sapply(1:4,function(i)
list(id=points[i,]$id,
gContains(region,SpatialPoints(points[i,1:2],proj4string=CRS(proj4string(region))))))
# [,1] [,2] [,3] [,4]
# id "A" "B" "C" "D"
# TRUE FALSE TRUE FALSE
Since you can use the "point in polygon" routine, and this apparently isn't already suitably designed to handle the multi-polygon case in R (which I find a bit odd actually), you are left with having to cycle through each of the multiple polygons. Now the trick is, if you are inside an odd number of polygons, you are inside the multi-polygon. If you are inside an even number of polygons, then you are actually outside of the shape.
Point in polygon testing that uses ray-crossings should ALREADY be able to handle this, just by making sure you pass in all the vertices to the original point.in.polygon test, but I am not sure which mechanism R is using, so I can only give you the even/odd advice above.
I also found this code, not sure if it will help:
require(sp)
require(rgdal)
require(maps)
# read in bear data, and turn it into a SpatialPointsDataFrame
bears <- read.csv("bear-sightings.csv")
coordinates(bears) <- c("longitude", "latitude")
# read in National Parks polygons
parks <- readOGR(".", "10m_us_parks_area")
# tell R that bear coordinates are in the same lat/lon reference system
# as the parks data -- BUT ONLY BECAUSE WE KNOW THIS IS THE CASE!
proj4string(bears) <- proj4string(parks)
# combine is.na() with over() to do the containment test; note that we
# need to "demote" parks to a SpatialPolygons object first
inside.park <- !is.na(over(bears, as(parks, "SpatialPolygons")))
# what fraction of sightings were inside a park?
mean(inside.park)
## [1] 0.1720648
# use 'over' again, this time with parks as a SpatialPolygonsDataFrame
# object, to determine which park (if any) contains each sighting, and
# store the park name as an attribute of the bears data
bears$park <- over(bears, parks)$Unit_Name
# draw a map big enough to encompass all points (but don't actually plot
# the points yet), then add in park boundaries superimposed upon a map
# of the United States
plot(coordinates(bears), type="n")
map("world", region="usa", add=TRUE)
plot(parks, border="green", add=TRUE)
legend("topright", cex=0.85,
c("Bear in park", "Bear not in park", "Park boundary"),
pch=c(16, 1, NA), lty=c(NA, NA, 1),
col=c("red", "grey", "green"), bty="n")
title(expression(paste(italic("Ursus arctos"),
" sightings with respect to national parks")))
# now plot bear points with separate colors inside and outside of parks
points(bears[!inside.park, ], pch=1, col="gray")
points(bears[inside.park, ], pch=16, col="red")
# write the augmented bears dataset to CSV
write.csv(bears, "bears-by-park.csv", row.names=FALSE)
# ...or create a shapefile from the points
writeOGR(bears, ".", "bears-by-park", driver="ESRI Shapefile")