ify it to your liking. And skip the lat/lon shenanigans.
Cheers,
Tim
> -Original Message-
> From: R-sig-Geo On Behalf Of Howard,
> Tim G (DEC) via R-sig-Geo
> Sent: Thursday, June 8, 2023 8:53 AM
> To: Kevin Zembower ; r-sig-geo@r-project.org
> Subject: Re: [R-sig-Geo] Adding C
Kevin,
To get the basemap in plot mode you need to download the tiles using read_osm.
This works for me, I
Simplified a bit:
library(sf)
library(tmap)
library(tigris)
options(tigris_use_cache = TRUE)
library(tmaptools)
## Get an example census map:
rw_tract <- tracts(state = "MD",
Kevin,
the tmap​ package might be what you are looking for.
https://cran.r-project.org/web/packages/tmap/vignettes/tmap-getstarted.html
https://cran.r-project.org/web/packages/tmap/index.html
Cheers,
Tim
From: R-sig-Geo on behalf of Kevin Zembower
via R-sig-Geo
Sent: Monday, June 5, 2023
Dear all -
Classically, if I want to create a spatial model and make a prediction for an
area within the extent of my raster stack my approach would be to crop all
predictor rasters to the area of interest and run the predict on that cropped
set. The stars package opens up another option: to
Francisco,
That's right, instead of cropping by longitude and latitude, you'll need to
define the model output area by another spatial layer that represents the
coastline and the certain distance inland.
To get that (your targeted modeling area), you need a spatial representation of
the
All,
I need to create XML metadata for some of my outputs (mostly rasters) following
the US government standard (FGDC):
https://www.fgdc.gov/standards/projects/FGDC-standards-projects/metadata/base-metadata/index_html
In researching what my options are in R, I have found EML:
at 02:27 Howard, Tim G (DEC) via R-sig-Geo
<mailto:r-sig-geo@r-project.org> wrote:
Ok, fair enough that there's no magic involved. I've worked through the details
with the small example as follows. The result is only a couple of cells
different in each direction.
library(star
ect: Re: [R-sig-Geo] stars::RasterIO using extent info?
Message-ID: <9d037da7-dc9c-9886-d6fc-5864cf8b4...@uni-muenster.de>
Content-Type: text/plain; charset="utf-8"
On 11/13/18 4:10 PM, Howard, Tim G (DEC) via R-sig-Geo wrote:
> Dear list,
>
> I am exploring the different op
Dear list,
I am exploring the different options for reading parts of large imagery object
in stars, as discussed here:
https://r-spatial.github.io/stars/articles/proxy.html
My ultimate goal is to read into RAM only a clipped portion of a large raster
(well, actually a raster stack, but
