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https://github.com/jmlondon/naturalearth_3571
map tiles for the natural earth II dataset in the EPSG:3571 projection; suitable for Alaska/Bering Sea
https://github.com/jmlondon/naturalearth_3571
Last synced: 5 days ago
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map tiles for the natural earth II dataset in the EPSG:3571 projection; suitable for Alaska/Bering Sea
- Host: GitHub
- URL: https://github.com/jmlondon/naturalearth_3571
- Owner: jmlondon
- Created: 2018-06-15T00:58:52.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2018-06-15T01:36:16.000Z (over 6 years ago)
- Last Synced: 2024-11-06T00:46:34.990Z (about 2 months ago)
- Size: 5.86 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
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README
---
output: github_document
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
library(sf)
library(leaflet)
```
# naturalearth_3571_tiles
The goal of naturalearth_3571 is to document creation of tile maps for use within
the `leaflet` package that are projected in WGS 84 / North Pole LAEA Bering
Sea (EPSG:3571). The tiles are derived from the Natural Earth II dataset.
The starting raster is downloaded from the [Natural Earth website](https://www.naturalearthdata.com/downloads/10m-raster-data/10m-natural-earth-2/). We are going to download the 1:10m resolution file with shaded relief,
water, and drainages. And, we will download the largest file size version.
The initial command line calls to `gdal` are provided below. The initial call
to `gdal_translate` simply establishes the source projection and creates a copy
that we call `ne.tif`. The next `gdalwarp` is where we specify to just include
the northern hempisphere. I would like to trim this even further to just focus
on the North Pacific. However, any attempts to provide a smaller region have
failed to create a proper tif in the next step. The last `gdalwarp` call is
where we actually re-project into the target EPSG:3571. Note the specification
of `-r lanczos` so we get a high quality resampling (this takes longer) and
also the `-wo SOURCE_EXTRA=1000` which prevents the presence of tearing at the
180 line (although, there is still a bit present, so maybe this should be 1500?)
```{bash, eval=FALSE}
gdal_translate -a_srs "+proj=longlat +ellps=sphere" NE2_HR_LC_SR_W_DR.tif ne.tif
gdalwarp -te -180 0 180 90 ne.tif ne_crop.tif
gdalwarp -dstnodata 0 -ot Byte -wo SOURCE_EXTRA=1000 -t_srs "EPSG:3571" \
-r lanczos -of GTiff ne_crop.tif naturalearth_3571.tif
```
The next step is to call `gdal2tiles.py`. In my case, this is found within my
Anaconda managed python install. It is important to pass `--profile=raster` so
the projection is maintained. I specified `-z 3-8` because there's likely no need
to zoom out higher (or in closer) for this dataset.
```{bash, eval=FALSE}
~/anaconda3/bin/gdal2tiles.py --profile=raster -z 3-8 \
naturalearth_3571.tif naturalearth_3571
```
Our tiles are now found within the `naturalearth_3571` folder and we will set
this as part of our `tile_url` variable for `leaflet`
```{r tile-url}
tile_url <- "naturalearth_3571/{z}/{x}/{y}.png"
```
The `leafletCRS` function requires us to provide various parameters all of which
are listed in the `tilemapresource.xml` file.
```{r leaflet-crs}
crs <- leafletCRS(crsClass = "L.Proj.CRS", code = "EPSG:3571",
proj4def = "+proj=laea +lat_0=90 +lon_0=180 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs",
resolutions = c(9156.71827398535061,
4578.35913699267530,
2289.17956849633765,
1144.58978424816883,
572.29489212408441,
286.14744606204221),
origin = c(-9009964.76123128645122, -9010456.80197188444436),
bounds = list(c(-9009964.76123128645122, -9010456.80197188444436),
c(9010456.80197188444436, 9009964.76123128645122))
)
```
Here we create an example trackline that crosses the 180 longitude line
```{r trackline-demo}
track_line <- 'LINESTRING (-169.4444 62.57549, -169.8999 62.31869, -170.4738 62.16436, -170.6529 61.4344, -171.7103 60.5111, -173.5295 60.78438, -175.2325 61.55942, -176.0107 61.88525, -177.1819 61.43621, -177.3077 61.25797, -177.3493 61.98553, -176.4385 62.26207, -177.3228 62.73083, -179.2457 62.84639, 179.7168 62.67369, 179.8549 62.58532, 179.8978 62.60145, -179.7893 62.64932, 179.9897 62.37538, -179.6393 62.66016, -179.8876 62.60982, -179.68 62.75012, 179.8264 62.78815, -179.789 62.72092, 179.6381 62.48176)'
track_line <- sf::st_as_sfc(track_line) %>%
sf::st_set_crs(4326)
```
And, now our call to create the leaflet map
```{r leaflet-map}
leaflet(options = leafletOptions(crs = crs)) %>%
addTiles(urlTemplate = tile_url,
options = tileOptions(noWrap = TRUE,
continuousWorld = FALSE))
```
## Let's try and create tiles with `tiler`
`tiler` is a new package that should create tiles in the same manner as our use
of `gdal2tiles.py`.
```{r install-tiler}
devtools::install_github("leonawicz/tiler", ref = "projections")
```
```{r create-tiles, eval=FALSE}
library(tiler)
map <- "naturalearth_3571.tif"
tile_dir <- "tiles"
tile(map, tile_dir, "3-8", profile = "raster")
```