I am trying to get accustomed to using the sf package in R.
Currently, I am trying to clip a shapefile of all zip codes in the US to the border of the state of Utah. I have downloaded a shapefile for the Utah border and a shapefile of all zip code tabulation areas in the US and matched their projection information.
I found that creating a clip from two data files using st_intersects() followed by st_intersection() works well. st_intersects() saves only the zip codes that intersect with the border of Utah, then st_intersection() should clip the zip codes that touch or intersect with the Utah border to the exact Utah border.
When I run the st_intersects() command on the zip code file, the result preserves the multipolygon geometries of all of the zip codes. When I run st_intersection(), it creates a new sf object that has a mix of geometries - points, polygons, and multipolygons. I want the resulting sf file to only have multipolygons. I was able to remove the point geometries using this code:
zips_utah <- zips_utah %>%
filter(st_geometry_type(.) != "POINT")
However, I still have an sf object with a mix of multipolygons and polygons. When I try to cast the polygons to multipolygons using st_cast(), I get this error:
only first part of geometrycollection is retainedError in st_cast.POINT(x[[1]], to, ...) :
cannot create MULTIPOLYGON from POINT
It seems that points still exist in the geometries of of my zip code shapefile, perhaps under the guise of "GEOMETRYCOLLECTION"? When I check the geometry type using unique(st_geometry_type(zips_utah)), this is the result I get:
[1] MULTIPOLYGON POLYGON
[3] GEOMETRYCOLLECTION
18 Levels: GEOMETRY POINT LINESTRING ... TRIANGLE
My understanding is that I cannot save the resulting clipped zip code sf object to a shapefile unless I first convert all the geometries to the same type. When I try to save the clipped zip code file using st_write(), I get the following error:
Error 1: Attempt to write non-polygon (POINT) geometry to POLYGON type shapefile.
I am wondering:
How do I preserve geometry types when running st_intersection?
Secondarily, does anyone have good resources on how the sf package handles geometries and mixed geometry types? What is the usefulness of this?
Related
I am trying to map a geojson file (A map of Alaska precincts, downloaded from the division of elections and then converted online to geojson) onto a choropleth map using folium, the problem is the coordinates are in 7-digit numbers like this:
[ -16624764.227, 8465801.1497 ]
I read on a similar post that this was most likely a US coordinate system like UTM or State Plane, and recommended using an API to reproject it. Is it also possible to access the coordinates directly such as with geopandas and divide them by 100000?
The data is most likely in a specific cartographic projection. You don't just want to divide by 100k - the data will likely have nonlinear transformations which have a different effect on the position depending on the location. See the GeoPandas docs on working with projections.
If the CRS of the data is correctly encoded, you can re-project the dataframe into lat/lons (e.g. WGS84, which has the EPSG Code 4326) using geopandas.GeoDataFrame.to_crs, e.g.:
df_latlon = df.to_crs("epsg:4326")
I have a polygon file and a point file (gpkg file)
Within the area of the polygon, there are several point files.
How can I extract only the points that exist in the area of the polygon?
The language used is assumed to be the R sf package.
The sample data is as follows.
https://drive.google.com/file/d/1vzyStvbSWYiqj7Yu-Te4sPHlZlxscAlE/view?usp=sharing
https://drive.google.com/file/d/1StzjcogZAS3gxy9owxKNZWOZFf1LoI1o/view?usp=sharing
I'm using the plotKML package to write a number of shapefiles to a KML file. The shapefiles only contain coordinates in two dimensions (longitude & latitude). The plotKML package has support for reading a third dimension (elevation or altitude) from the spatial object and writing this to the KML file.
My plan is to iterate through the shapefiles and create new SpatialPolygonsDataFrames with the missing third dimension (the altitude data is contained in the 'data' slot) but the Polygon() function specifically states that the coords argument must be a "2-column numeric matrix with coordinates".
How is it possible to create a Polygon with a three column coords matrix?
This is not supported by sp, by design. Package sf has support for three-dimensional coordinates in polygon nodes (POLYGON Z) but this is, afaict, not (yet) supported by plotKML.
I have a spatial dataset of shopping centers that I would like to create buffers around in R.
I think these packages will be useful:
require(maptools)
require(geosphere)
I was able to do so for a set of coordinates, but not for spatial data. The code looks like this:
coordinates(locs) <- c("Longitude", "Latitude") # set spatial coordinates
fivekm <- cbind(coordinates(locs), X=rowSums(distm (coordinates(locs)[,1:2], fun = distHaversine) / 1000 <= 5)) # number of points within 5 km
But I don't know what function/package to use for a set of polygons. Can someone please advise on the function (or code) and I will go from there?
Thanks!
In library rgeos, there is the gBuffer function that works with SpatialPoints or SpatialPolygons.
The width parameter allows to set the distance to which you want to buffer. However, be careful, this distance is in the scale of the coordinates system used. Thus, in degrees and not in meters with non-projected data. As suggested by #Ege Rubak, you will have to project your data with spTransform first (be sure to use the appropriate CRS according to your location).
As for now, rgeos library works with library sp, but not (yet?) with the recent sf.
I think the only option at the moment is to project your longitude and latitude points to a flat map and then do everything there. As far as I know there are no packages for doing polygonal geometry on the sphere yet (I'm working on one, but there is no ETA).
Projection used to be done with spTransform from the sp package, but now it may be more convenient to use the more modern simple features package sf which has the function st_transform. The vignette https://cran.r-project.org/web/packages/sf/vignettes/sf1.html has a section called "Coordinate reference systems and transformations" to help you with this part. The buffering is described in the section "Geometrical operations".
The two previous post have covered the details but I thought it might be helpful to provide a workflow. This is assuming you have you are using points of lat and long. What is your original spatial data format?
Convert your coordinates into a Spatial Points Dataframe SpatialPointsDataFrame and assign it a geographic CRS (proj4) that matches your coordinate data (probably WGS84)
Change the projection to a local projected CRS with preferred units
Apply buffer to spatial point data frame, the width will now be in more usable units
EDIT: I have reworded the title question slightly, and adjusted the text to respond to the comment by #DWin.
Combining geographic layers that are projected and not projected can be challenging. Often, it seems, some transformation is necessary, as geographic layers come from different products and publishers.
I am aware that R has several tools to perform geographic transformations. For example:
For objects of class Spatial* in the sp package, the spTransform() function in the rgdal package can be used; and,
For objects of class Raster* in the raster package, the projectRaster() function can be used.
Here is a specific task that I would like to accomplish in R: Transform to UTM grid Zone 15N (Datum: NAD83) a polygons layer describing lakes in a UTM grid Zone 15N (Datum: NAD27) projection (this is in an ESRI shapefile format).
The useful thing here is the epsg database included in rgdal.
epsgs = make_EPSG()
subset(epsgs,grepl("15N",epsgs$note))
[etc]
code
2703 26715 # NAD27 / UTM zone 15N [etc]
2851 26915 # NAD83 / UTM zone 15N [etc]
[etc]
Those codes are what you need in spTransform. If your lakes are in a shapefile with that NAD27 projection, then:
require(maptools)
lakes = readShapeSpatial("lakes.shp")
proj4string(lakes)=CRS("+init=epsg:26715")
should give you the lakes as supplied (note I dont think readShapeSpatial will read a .prj file with a shapefile set, so I've set it here explicitly)
Now to convert to NAD83 datum version of UTM zone 15N:
lakes83 = spTransform(lakes,CRS("+init=epsg:26915"))
Rasters are a bit trickier since they generally involve a warp so that you end up with a regular grid in your projected coordinate system - you can't just transform the coordinates of the corners...