knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
slippymath provides tools to assist your work with slippy map tile servers and the map tiles they provide. The main things it can help with are:
determining what zoom level to use
identifying the tiles you need to plot your data over
identifying where lonlat coordinates fall in tile space
determining the bounding boxes of tiles
* compositing tiles into a single spatially referenced raster.
Downloading tiles does not get a built-in helper. This is to make
as service agnostic as possible. There is an example of downloading from Mapbox
in the README. You may also enjoy the
ceramic package by Michael Sumner.
The main assumption
slippymath makes is that you can calculate a bounding box
for your spatial data. Most spatial tools have this capability - see
Given a bounding box in longitude and latitude (EPSG: 4326), you can execute a 'tile query' to see how many slippy map tiles your data would occupy at various zoom levels:
library(slippymath) uluru_bbox <- c(xmin = 131.02084, xmax = 131.0535, ymin = -25.35461, ymax = -25.33568) bbox_tile_query(uluru_bbox)
When choosing your zoom level consider: Tiles are 256 x 256 pixel squares which roughly range in size from 30 - 40kb. 100's of tiles will be 10's of megabytes. You may also want to check rate limits imposed by the server you intend to pull the tiles from.
slippymath can also choose a zoom level for your tiles, given a tile budget. See below.
slippymath calls the set of tiles you need to plot all your data over a "Tile
tg for short. You can get a tile grid given a bounding box and tile
zoom level OR a bounding box and tile budget (the zoom will be chosen to fit the
For example using the Uluru bounding box from above and zoom level 14:
bbox_to_tile_grid(uluru_bbox, zoom = 14)
And using the same bounding box with a budget of 15 tiles:
bbox_to_tile_grid(uluru_bbox, max_tiles = 15)
A tile grid is an object that contains a dataframe of tile coordinates (
$tiles) and a
Occasionally it may make sense to find the tile at a given zoom for a single
slippymath has functions to convert between longitude and latitude tile
lonlat_to_tilenum(131.02084, -25.35461, zoom = 14) tilenum_to_lonlat(14154, 9385, zoom = 14)
In this example, the exact lon lat we started with is not returned in the conversion from tile coordinate space. The lon lat returned is always the top left corner point of the tile.
Once you have tiles, you will likely want to know their spatial bounding boxes as this is essential for compositing them. If you use the built-in compositing function, this is taken care of for you.
Find the bounding boxes of a tile grid like so:
uluru_grid <- bbox_to_tile_grid(uluru_bbox, max_tiles = 15) tile_grid_bboxes(uluru_grid) ## for a single tile use tg_bb()
Slippy maps use a special mercator projection: EPSG: 3857. The tile bounding boxes are expressed in this projection.
A simple function to composite tiles to a single raster with the correct spatial
extent (again in the EPSG 3857 projection) is provided. It constructs individual
raster and subsequently calls
raster::merge which has issues with manual
gc which can slow things down with many tiles.
To composite, it is assumed you have a tile grid object, and a list of image files that correspond in order to the tiles in the tile grid. See the README example for a way to download tiles and get such a list.
raster_out <- compose_tile_grid(tile_grid, images)
Finally a convenience function is provided to convert the raster to a .png if needed:
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