TileManager: Tools for creating and detecting tiling schemes for geospatial datasets

Authors: Andrew Plowright License: GPL 3

This package provides tools for working with tiled geospatial datasets.

Introduction

Use the tileScheme function to create a set of tiles from a Raster or Extent object.

library(TileManager)
#> TileManager - Please note that version 0.4.0 is NOT backwards compatible

data(CHMdemo)

ts <- tileScheme(CHMdemo, tiledim = c(30,30), buffer = 5)

plot(CHMdemo)
plot(ts, add = T)

Use the removeEmpty argument to drop tiles with no Raster values.

ts <- tileScheme(CHMdemo, tiledim = c(20,20), buffer = 2.5, removeEmpty = TRUE)

plot(CHMdemo)
plot(ts, add = T)

Other handy features:

• The origin argument can be used to force the tile scheme to snap to a pair of coordinates.
• The bufferSpill argument controls whether or not the buffers extent beyond the input’s original extent.
• By default, tile dimensions are in map units. Using the cells argument, they can be defined by a number of Raster cells.

The ‘tileScheme’ class

A ‘tileScheme’ object is composed of the following slots:

str(ts, 2)
#> Formal class 'tileScheme' [package "TileManager"] with 6 slots
#>   ..@ tiles :List of 32
#>   ..@ buffs :List of 32
#>   ..@ nbuffs:List of 32
#>   ..@ crs   :Formal class 'CRS' [package "sp"] with 1 slot
#>   ..@ buffer: num 2.5
#>   ..@ data  :'data.frame':   32 obs. of  3 variables:

• Three lists of Polygons objects:
  • tiles: the actual extents of the tiles
  • buffs: the buffered tiles
  • nbuffs: the non-overlapping buffers (see section below)
• The crs slot, which stores the tile scheme’s coordinate reference system.
• A numeric buffer slot.
• A data.frame in the data slot, which stores the row, column and name of each tile.

head(ts@data)
#>      row col tileName
#> R1C1   1   1     R1C1
#> R1C3   1   3     R1C3
#> R1C4   1   4     R1C4
#> R2C1   2   1     R2C1
#> R2C2   2   2     R2C2
#> R2C3   2   3     R2C3

Non-overlapping buffers

Non-overlapping buffers (often abbreviated to nbuffs) are useful for re-assembling tiled data. Essentially, they preserve buffers only where they do not overlap onto neighboring tiles (i.e.: along the edge of the tile scheme). This allows you to recombine tiles without worrying about overlapping areas and without losing any information along the data edges.

In the example below:

• The solid blue is the tile
• The dashed red is the buffer
• The solid red is the non-overlapping buffer

Methods

Some useful methods are provided for subsetting the tile scheme, or for converting it into other formats.

Get buffers as a SpatialPolygonsDataFrame:

ts[["buffs"]]
#> class       : SpatialPolygonsDataFrame 
#> features    : 32 
#> extent      : 439872.8, 440015.2, 5526636, 5526753  (xmin, xmax, ymin, ymax)
#> crs         : +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0 
#> variables   : 3
#> names       : row, col, tileName 
#> min values  :   1,   1,     R1C1 
#> max values  :   6,   7,     R6C7

Subset a specific tile by name, number or row/col:

# By name
ts["R2C2"]
#> class     : tileScheme
#> extent    : 439895.25, 439915.25, 5526710.75, 5526730.75 (xmin, xmax, ymin, ymax)
#> CRS       : +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0
#> tiles     : 1
#> nrow/ncol : 1,1
#> buffer    : 2.5
#> variables : row, col, tileName

# By number
ts[7]
#> class     : tileScheme
#> extent    : 439935.25, 439955.25, 5526710.75, 5526730.75 (xmin, xmax, ymin, ymax)
#> CRS       : +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0
#> tiles     : 1
#> nrow/ncol : 1,1
#> buffer    : 2.5
#> variables : row, col, tileName

# By row/col
ts[2,3]
#> class     : tileScheme
#> extent    : 439915.25, 439935.25, 5526710.75, 5526730.75 (xmin, xmax, ymin, ymax)
#> CRS       : +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0
#> tiles     : 1
#> nrow/ncol : 1,1
#> buffer    : 2.5
#> variables : row, col, tileName

Subset entire rows or columns:

# One row
ts[4,]
#> class     : tileScheme
#> extent    : 439875.25, 439975.25, 5526670.75, 5526690.75 (xmin, xmax, ymin, ymax)
#> CRS       : +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0
#> tiles     : 5
#> nrow/ncol : 1,5
#> buffer    : 2.5
#> variables : row, col, tileName

# Multiple columns
ts[,2:3]
#> class     : tileScheme
#> extent    : 439895.25, 439935.25, 5526638, 5526750.75 (xmin, xmax, ymin, ymax)
#> CRS       : +proj=utm +zone=11 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0
#> tiles     : 11
#> nrow/ncol : 6,2
#> buffer    : 2.5
#> variables : row, col, tileName

Saving and loading

The tile scheme can be saved as a single SHP file. In this case, tiles, buffs and nbuffs will all be merged into a single polygons dataset. Buffer information is saved to an accompanying XML file.

# Create tile scheme
ts <- tileScheme(CHMdemo, tiledim = c(30,30), buffer = 5)

# Save tile scheme
tileSave(ts, "C:/myfiles/tilescheme.shp")

# Load tile scheme
ts <- tileLoad("C:/myfiles/tilescheme.shp")

Tile detection

If you have a received a series of tiled raster files, the tileDetector function can even be used to detect the tile size and buffer size of the data and generate the associated tileScheme.