velox

Velox is an R package for performing fast extraction and manipulation operations on geographic raster data. velox is fast because all raster computations are performed in C++ (using the excellent Rcpp API), and all data is held in memory. velox is intended to be used together with the raster package, to which it provides a straightforward interface.

Currently, the following operations are implemented in velox: + Focal value calculation (i.e. moving window filters) + Raster value extraction given polygons, lines, or points + Rasterization of polygons or lines + Raster aggregation + Cropping + Image patch flattening (similar to Matlab’s im2col) and reconstruction

The development of velox was funded in part by the Swiss National Science Foundation under COST action IS1107, SERI project C12.0087.

Update

As of version 0.1.0.9007 velox is interoperable with the Simple Feature package. Other new features include a redesigned C++ backend based on the Boost Geometry library and extract and rasterize functions for linestring geometries. Please note that these improvements entail new dependencies (see below).

Status

Travis-CI Build Status

Installation

Velox should work on all major operating systems (Linux, Mac OS, Windows).

Dependencies

Velox requires the rgdal, rgeos, and sf packages, which rely on the external GDAL (>= 2.0.0), GEOS (>= 3.3.0), PROJ.4 (>= 4.8.0), and UDUNITS libraries. On Debian/Ubuntu (>= 14.04), these dependencies can be installed by entering

sudo add-apt-repository -y ppa:ubuntugis/ubuntugis-unstable
sudo apt-get update
sudo apt-get install libgdal-dev libproj-dev libgeos-dev libudunits2-dev

in a terminal.

R Package

Once the system dependencies are available, you can either install velox from CRAN

install.packages("velox")

or you can install the development version using the install_github function from the devtools package:

library(devtools)
install_github("hunzikp/velox")

Please note that this page refers to the development version of the package.

Benchmarking

The following performance tests were peformed on a i7-6920HQ CPU @ 2.90GHz, using raster 2.5-8, PostgreSQL 9.5 and PostGIS 2.2. See here for the underlying R script.

Getting Started

Creating VeloxRaster Objects

VeloxRaster objects are created with the velox function:

library(velox)
library(raster)

## From GDAL readable raster file
vx1 <- velox("myraster.tif")

## From RasterLayer object
rl <- raster("myraster.tif")
vx2 <- velox(rl)

## From RasterStack object
rs <- stack("myraster.tif")
vx3 <- velox(rs)

## From matrix
mat <- matrix(1:100, 10, 10)
vx4 <- velox(mat, extent=c(0,1,0,1), res=c(0.1,0.1), crs="+proj=longlat +datum=WGS84 +no_defs")

## From list of matrices
mat.ls <- list(matrix(1:100, 10, 10), matrix(100:1, 10, 10))
vx5 <- velox(mat.ls, extent=c(0,1,0,1), res=c(0.1,0.1), crs="+proj=longlat +datum=WGS84 +no_defs")

## From list of VeloxRasters
vx.ls <- list(vx4, vx5)
vx6 <- velox(vx.ls)

Manipulating VeloxRaster Objects

VeloxRaster objects are ReferenceClass objects and thus mutable:

## Crop VeloxRaster
mat <- matrix(1:100, 10, 10)
vx <- velox(mat, extent=c(0,1,0,1), res=c(0.1,0.1), crs="+proj=longlat +datum=WGS84 +no_defs")
cropext <- c(0.3,0.7,0.3,0.7)
vx$crop(cropext)

> vx$extent
[1] 0.3 0.7 0.3 0.7

We can also aggregate a VeloxRaster…

## Aggregate VeloxRaster
mat <- matrix(1:100, 10, 10)
vx <- velox(mat, extent=c(0,1,0,1), res=c(0.1,0.1), crs="+proj=longlat +datum=WGS84 +no_defs")
vx$aggregate(factor=c(2,2), aggtype="sum")

… or calculate focal values (i.e. apply a moving window filter):

## Apply filter to VeloxRaster
mat <- matrix(1:100, 10, 10)
vx <- velox(mat, extent=c(0,1,0,1), res=c(0.1,0.1), crs="+proj=longlat +datum=WGS84 +no_defs")
vx$medianFocal(wrow=3, wcol=3, bands=1)

Extracting raster values given polygons

## Make VeloxRaster
mat <- matrix(1:100, 10, 10)
extent <- c(0,1,0,1)
vx <- velox(mat, extent=extent, res=c(0.1,0.1), crs="+proj=longlat +datum=WGS84 +no_defs")

## Make SpatialPolygonsDataFrame
library(sp)
library(rgeos)
set.seed(0)
coords <- cbind(runif(10, extent[1], extent[2]), runif(10, extent[3], extent[4]))
sp <- SpatialPoints(coords)
spol <- gBuffer(sp, width=0.2, byid=TRUE)
spdf <- SpatialPolygonsDataFrame(spol, data.frame(id=1:length(spol)), FALSE)

## Extract values and calculate mean
ex.mat <- vx$extract(spdf, fun=mean)

Rasterizing polygons

## Make VeloxRaster
mat <- matrix(0, 10, 10)
extent <- c(0,1,0,1)
vx <- velox(mat, extent=extent, res=c(0.1,0.1), crs="+proj=longlat +datum=WGS84 +no_defs")

## Make SpatialPolygonsDataFrame
library(sp)
library(rgeos)
set.seed(0)
coords <- cbind(runif(10, extent[1], extent[2]), runif(10, extent[3], extent[4]))
sp <- SpatialPoints(coords)
spol <- gBuffer(sp, width=0.05, byid=TRUE)
spdf <- SpatialPolygonsDataFrame(spol, data.frame(id=1:length(spol)), FALSE)

## Rasterize polygons using "id" column
vx$rasterize(spdf, field="id", band=1)

raster interface

## Make VeloxRaster with two bands
mat1 <- matrix(1, 10, 10)
mat2 <- matrix(2, 10, 10)
extent <- c(0,1,0,1)
vx <- velox(list(mat1, mat2), extent=extent, res=c(0.1,0.1), crs="+proj=longlat +datum=WGS84 +no_defs")

## Cast band 1 as RasterLayer
rl <- vx$as.RasterLayer(band=1)

## Cast both bands as RasterStack
rs <- vx$as.RasterStack()

## Back to VeloxRaster
vx2 <- velox(rs)

Help

Because most of velox’s functionality comes in the form of VeloxRaster methods, accessing the help pages is performed as follows:

## See all methods of VeloxRaster
?VeloxRaster

## See help for method 'extract'
?VeloxRaster_extract

## See help for method 'crop'
?VeloxRaster_crop

## etc...