I have a shape file, uploaded at the following path:
https://drive.google.com/open?id=0B1ITb_7lHh1EUFVfVWc4ekRfSnc
I imported the data using the "read.shapefiles" function in "shapefiles" package:
landuse<- read.shapefile("landuse")
I now need to extract the lat/long centroids of all the shapes within the landuse object and add it to the landuse$dbf dataframe
I tried two things:
lu_df<-coordinates(landuse)
lu_df<-SpatialPoints(landuse)
Both gave me the following error:
Error in coordinates(as.data.frame(obj)) :
error in evaluating the argument 'obj' in selecting a method for function 'coordinates': Error in data.frame(record = 1L, content.length = 80L, shape.type = 5L, :
arguments imply differing number of rows: 1, 4, 7
I am not sure on how to proceed.
First, I recommend using the rgdal package for spatial data. The readOGR and writeOGR functions provide nice Shapefile handling (like input and output of projections etc.).
UPDATE:
Since this is not working for #Shaz (see error in comments), the maptoolsfunction readShapePoly()was utilized. Also see this post.
To your problem: The rgeos package provides a function gCentroid() which calculates the centroid of your polygons. The solution looks something like this:
setwd("/path/to/your/shapefile/")
library(maptools)
library(rgeos)
landuse <- readShapePoly("landuse")
centr <- gCentroid(landuse, byid = TRUE)
# create SpatialPointsDataFrame to export via writeOGR
# positive side effect: All data from landuse#data joined to centr#data
centr <- SpatialPointsDataFrame(centr, data= landuse#data)
writeOGR(centr, ".", "landuse_centroids", driver = "ESRI Shapefile")
Related
I have this, and I am trying to use gDistance to calculate the distance between each centroid and the city of Baghdad. I am trying to do it like this:
gDistance(districts_centroids, districts#data$ADM3NAME %in% c("Baghdad"), byid=TRUE)
Where district_centroids are Formal Class SpatialPoints, and the districts#data... is basically the city of Baghdad in the shp file.
I get an error saying the following:
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘is.projected’ for signature ‘"logical"’
In addition: Warning message:
In RGEOSDistanceFunc(spgeom1, spgeom2, byid, "rgeos_distance") :
Spatial object 1 is not projected; GEOS expects planar coordinates
I am completely new to R and I don't really know what's going on.
Any help would be appreciated
Thank you!
This is a great question. After taking a look at things, it was decided the best way to answer this question was using simple feature objects rather than spatial objects.
The map in question looks familiar. I used the IRQ_2_sf.rds map which is probably the same map used and shown above. There are other alternative ways to achieve the solution to this question. Jupyter Lab is the IDE used.
Using the Google API and the geocode function, the coordinates for Baghdad were retrieved.
baghdad <- geocode("Baghdad, Iraq", source = c("google") )
A tibble: 1 × 2
lon lat
<dbl> <dbl>
44.36607 33.31524
Then sf functions were used to create the sf column object.
baghdad.sfg <- st_point(c(lon, lat), dim = "XY")
baghdad.sfc <- st_sfc(baghdad.sfg, crs = 4326)
Then, using the sf map named iraq, the centroids were created.
Note: warning message - st_centroid does not give correct centroids for longitude/latitude data. For this answer, the centroids will be close
enough.
iraq.c <- st_centroid(iraq)
The distance from each centroid to Baghdad gets determined in kilometers.
head(dist <- data.frame(c(st_distance(iraq.c$geom[], baghdad.sfc)/1000)))
Units: [m]
[1] 28.63250 59.61553 215.43354 323.06418 259.14509 113.55356
And then create a date frame that includes the names, the centroid geometry and the distance values. Requires some cleaning and binding.
distance <- c(dist$c.st_distance.iraq.c.geom....baghdad.sfc..1000.)
x <- distance[]
d_Bdad_Km <- as.numeric(str_extract(x, "[0-9]*.[0-9]."))
iraq2 <- iraq[-c(5, 8,9,10,11,12,13)]
df_dist <- cbind(iraq2, d_Bdad_Km) # df as desired
And then the outputs gets plotted.
plot(iraq$geom)
plot(iraq.c$geom, add = TRUE, col = "red")
plot(baghdad.sfc, add = TRUE, pch = 19, col = "blue")
Please ask if there are any follow-up questions. The plot can be viewed at this link:
I need to extract Sentinal 2 data for NDVI for specific study sites. I used RGIS tools and followed the reference manual https://cran.r-project.org/web/packages/RGISTools/RGISTools.pdf . I was able to obtain and plot the time series for NDVI.
Now I have to do the same procedure for my study area. I have a shapefile for my area which is accessible here, https://gis.utah.gov/data/boundaries/zip-codes/. It looks like that the shapefile needs to be converted into raster or sf type object before using it. I used st_as_sf to convert shapefile to sf but I receive the following error in senSearch function,
Error in if (as.integer(json$feed$opensearch:totalResults) > 0) { :
argument is of length zero
It is my first time working with such data, any help is appreciated.
It looks like you try to search using the extent argument. This argument only accepts spatial objects projected as lonlat projections. You can use the region argument with any spatial obj (sp, sf, or raster).
Here you have an example with your region:
library(RGISTools)
library(rgdal)
shp<-readOGR("ZipCodes_shp/ZipCodes")
plot(shp)
senres<-senSearch(startDate = as.Date("2018210", "%Y%j"),
endDate = as.Date("2018215", "%Y%j"),
platform = "Sentinel-2",
region = shp,
product = "S2MSI1C",
username="user",
password="pass")
I am trying to get the intersection of two shapefiles (census tracts that fall within the boundaries of certain metropolitan areas). I am able to successfully get the intersecting features, however when I try to convert the output of sf_intersect to a SpatialPolygonsDataframe I get the error:
"Error in as_Spatial(from) : conversion from feature type
sfc_GEOMETRY to sp is not supported"
This is my code:
library(sf)
library(dplyr)
library(tigris)
library(sp)
#download shapefiles corresponding to metro areas
metro_shapefiles<-core_based_statistical_areas(cb = FALSE, year = 2016)
#convert to sf and filter
metro_shapefiles<-st_as_sf(metro_shapefiles)%>%filter(GEOID==31080 )
#Data for California
census_tracts_california<-tracts(state="CA",year=2016)
census_tracts_california<-st_as_sf(census_tracts_california)
#INTERSECT AND CONVERT BACK TO SP
census_tracts_intersected1<-st_intersection(census_tracts_california,
metro_shapefiles)
#back to spatial
census_tracts_intersected1<-as(census_tracts_intersected1,"Spatial")
The error message is telling you you can't convert an sfc_GEOMETRY to a Spatial object. There is no sp equivalent object.
In your intersection result you have a mixture of geometries (and hence, you're returned an sfc_GEOMETRY as your 'geometry'). You can see all the geometries here:
types <- vapply(sf::st_geometry(census_tracts_intersected1), function(x) {
class(x)[2]
}, "")
unique(types)
# [1] "POLYGON" "MULTILINESTRING" "MULTIPOLYGON"
If you want, you can extract each type of geometry, and convert those to SP individually:
lines <- census_tracts_intersected1[ grepl("*LINE", types), ]
polys <- census_tracts_intersected1[ grepl("*POLYGON", types), ]
spLines <- as(lines, "Spatial")
spPolys <- as(polys, "Spatial")
Additional Information
I mentioned in the comments you could use st_join. However, this may not give you the result you want. Within sf library there are the geometric binary predicates, such as ?st_intersects, and geometric operations such as ?st_intersection
The predicates return a sparse (default) or dense matrix telling you with which geometry of y each geometry of x intersects. If you use this within st_join, it will return the (original) geometries that intersect, rather than the sparse matrix.
Whereas the operations (e.g. st_intersection) will compute the intersection, and return new geometries.
Example use
The predicates (st_intersects) can be used inside st_join, and they will return the original geometries which 'intersect'
sf_join <- sf::st_join(census_tracts_california, metro_shapefiles, join = st_intersects)
In this case this gives a single type of object
types <- vapply(sf::st_geometry(sf_join), function(x) {
class(x)[2]
}, "")
unique(types)
# [1] "MULTIPOLYGON"
## so you can convert to a Spatial object
spPoly <- as(sf_join, "Spatial")
But you need to decide if the result of st_intersect is what you're after, or whether you need the new geometries given by st_intersection.
Further reading
information on each join is on the sf blog.
spatial predicates and examples of what the different operations do are on wikipedia (with some good illustrations)
Credit to user #lbussett for their description on the difference between st_intersect and st_intersection
Conversion to a Spatial object can't handle mixed lines and polygons. After the intersection you can extract just the polygons (and discard any lines) using:
st_collection_extract("POLYGON")
Your example doesn't seem to fail anymore, so I've created a new example that intersects two polygons, with a shared side. This results in an intersection output of a polygon and a line.
On the second attempt I've piped the intersection through the st_collection_extract() function prior to successful conversion to a Spatial object.
library(sf)
library(dplyr)
library(sp)
#Create some boxes
BoxA <- st_polygon(list(cbind(c(0,0,2,2,0),c(0,2,2,0,0))))
BoxB <- st_polygon(list(cbind(c(1,1,3,3,1),c(1,3,3,1,1))))
BoxC <- st_polygon(list(cbind(c(2,2,4,4,2),c(0,2,2,0,0))))
#Create a funny shaped union to help demonstrate the intersection issue
BoxAB <- st_union(BoxA,BoxB)
plot(BoxAB)
plot(BoxC,add=TRUE,border="blue")
Example polygons to intersect
#Intersect of BoxAB with BoxC results in a line and a polygon
BoxIntersects<-st_intersection(BoxAB,BoxC)
plot(BoxIntersects)
Intersection made up of a polygon and a line
#back to spatial fails
SpatialVersionOfIntersects<-as(BoxIntersects,"Spatial")
Error in .as_Spatial(from, cast, IDs) :
conversion from feature type sfc_GEOMETRY to sp is not supported
#Intersect again, but this time extract only the polygons
BoxIntersects<-st_intersection(BoxAB,BoxC) %>% st_collection_extract("POLYGON")
#back to spatial suceeds!
SpatialVersionOfIntersects<-as(BoxIntersects,"Spatial")
I would like to merge some regions in gadm data and then plot the map. So far I have the following:
#install.packages("sp",dependencies=TRUE)
#install.packages("RColorBrewer",dependencies=TRUE)
#install.packages("maptools",dependencies=TRUE)
library(sp)
library(maptools)
#library(RColorBrewer)
# get spatial data
con <- url("http://gadm.org/data/rda/CZE_adm2.RData")
print(load(con))
close(con)
IDs <- gadm$ID_2
IDs[IDs %in% c(11500:11521)] <- "11500"
gadm_new <- unionSpatialPolygons(gadm, IDs)
# plot map
spplot(gadm_new, "NAME_2", col.regions=col, main="Test",colorkey = FALSE, lwd=.4, col="white")
However this results in error:
Error in function (classes, fdef, mtable) :
unable to find an inherited method for function "spplot", for signature "SpatialPolygons"
Now I have no idea what can possibly fix this error.
I'm not sure about what you're trying to do here.
The error is due to the fact that spplot is used to draw spatial objects with attributes, ie with associated data. Your gadm object is of class SpatialPolygonsDataFrame, so it defines polygons and associated data that can be accessed via the slot gadm#data. When you use UnionSpatialPolygons, you only get a SpatialPolygons class object, which can be plotted with plot, but not with spplot :
IDs <- gadm$ID_2
IDs[IDs %in% c(11500:11521)] <- "11500"
gadm_new <- unionSpatialPolygons(gadm, IDs)
plot(gadm_new)
If you want to use spplot, you have to merge your associated data manually, the same way you merged your polygons, and then build back a SpatialPolygonsDataFrame. One way to do it is the following :
gadm_new <- gadm
## Change IDs
gadm_new$ID_2[gadm_new$ID_2 %in% c(11500:11521)] <- "11500"
## Merge Polygons
gadm_new.sp <- unionSpatialPolygons(gadm_new, gadm_new$ID_2)
## Merge data
gadm_new.data <- unique(gadm_new#data[,c("ID_2", "ENGTYPE_2")])
## Rownames of the associated data frame must be the same as polygons IDs
rownames(gadm_new.data) <- gadm_new.data$ID_2
## Build the new SpatialPolygonsDataFrame
gadm_new <- SpatialPolygonsDataFrame(gadm_new.sp, gadm_new.data)
Then you can use spplot to plot a map with an associated attribute :
spplot(gadm_new, "ENGTYPE_2", main="Test", lwd=.4, col="white")
Note that here I only used the ENGTYPE_2 variable of your data, not the NAME_2 variable, as I don't see the point to represent a variable where each value seems unique for each polygon.
I am wondering if this is possible to do this R .
I have one data as SpatialLinesDataFrame and another as spatialPolygonDataFrame. Is it possible to overlay these two data ?
When I try to overlay these I get the following error:
jd <- overlay(res,hello)
Error in function (classes, fdef, mtable) : unable to find an inherited method for function
‘overlay’ for signature ‘"SpatialLinesDataFrame", "SpatialPolygonsDataFrame"’
In the above code res is the SpatialLinesDataFrame and hello is SpatialPolygonDataFrame.
I have an shapefile and then I have data points with x,yand z
coordinates. I want to show the contour lines on the shapefile.
The procedure I used is using akima package to do the interpolation. The
code I used to interpolate is
fld <- interp(x,y,z)
Then I changed this to spatial object by using following code:
res <-ContourLines2SLDF(contourLines(fld))
The above command would store the contourlines as spatial data.
Then I read the shapefile and I plot both shapefile and res as follows:
p1 <-
spplot(hello,sp.layout=list(list("sp.lines",res)),col="blue",lwd=0,fill="grey",colorkey=F)
p1
"hello" is my shapefile and "res" is the object I created as shown above.
The problem is contour stored in "res" extends beyond the shapefile. So I
want to clip that contour with the shapefile and only display the contour
within the shapefile area.
So I am looking for a way to clip the contour layer with the polygon layer.
I have attached the image I got with my code.
In the image you can see the lines out of the shapefile. I also want to know
how can I display the contour levels on the map.
Thank you so much.
Jdbaba
I also want to know what does overlay does exactly. Does it intersect the area of both the data ?
Thank you.
It sounds like you're trying to clip your lines to the polygon extent. Use gIntersection from the rgeos package. Here's a reproducible example:
library(rgeos)
xx <- SpatialPoints(coords=matrix(data=c(0,0), nrow=1))
xx <- gBuffer(spgeom=xx, width=1)
yy <- SpatialLines(list(Lines(Line(matrix(c(-1,1,-1,1), nrow=2)), ID=1)))
zz <- gIntersection(yy, xx)
You can overlay the plot like so:
plot(xx)
plot(zz, add = TRUE, col = "blue")
Noah's answer has worked quite well for me. However, the output of his answer is a SpatialLines object, which cannot be saved as a shape file.
My two cents here is about how you can convert your SpatialLines object into a SpatialLinesDataFrame and save it as a shape file.
res.df <- fortify(res) # create data frame of res, your original SpatialLinesDataFrame
data <- data.frame(id = unique(res.df$id)) # get ids of road segments
rownames(data) <- data$id
# transform SpatialLines object into SpatialLinesDataFrame
zzSpatialLineDF <- SpatialLinesDataFrame(zz, data) # convert zz object keeping road ids
# 5 Save Shape File to your working directory
writeOGR(zzSpatialLineDF, dsn = '.', layer ='zzSpatialLineDF', driver = 'ESRI Shapefile')