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R for Macroecology Spatial data continued. Projections  Cylindrical projections Lambert CEA.

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Presentation on theme: "R for Macroecology Spatial data continued. Projections  Cylindrical projections Lambert CEA."— Presentation transcript:

1 R for Macroecology Spatial data continued

2 Projections  Cylindrical projections Lambert CEA

3 Behrmann EA  Latitude of true scale = 30

4 Choosing a projection  What properties are important?  Angles (conformal)  Area (equal area)  Distance from a point (equidistant)  Directions should be strait lines (gnomonic)  Minimize distortion  Cylindrical, conic, azimuthal

5 Projecting points  project() function in the proj4 package is good  Can also use project() from rgdal  spTransform() (in rgdal) works for SpatialPoints, SpatialLines, SpatialPolygons...  Can also handle transformations from one datum to another

6 Projecting points > lat = rep(seq(-90,90,by = 5),(72+1)) > long = rep(seq(-180,180,by = 5),each = (36+1)) > xy = project(cbind(long,lat),"+proj=cea +datum=WGS84 +lat_ts=30") > par(mfrow = c(1,2)) > plot(long,lat) > plot(xy)

7 Projecting a grid > mat = raster("MAT.tif") > mat = aggregate(mat,10) > bea = projectExtent(mat,"+proj=cea +datum=WGS84 +lat_ts=30") > mat class : RasterLayer dimensions : 289, 288, (nrow, ncol, ncell) resolution : , (x, y) extent : 0, 12, , (xmin, xmax, ymin, ymax) projection : +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs +towgs84=0,0,0 values : in memory min value : max value : > bea class : RasterLayer dimensions : 289, 288, (nrow, ncol, ncell) resolution : , (x, y) extent : 0, , , (xmin, xmax, ymin, ymax) projection : +proj=cea +datum=WGS84 +lat_ts=30 +ellps=WGS84 +towgs84=0,0,0 values : none

8 Projecting a grid > bea = projectExtent(mat,"+proj=cea +datum=WGS84 +lat_ts=30") > res(bea) = xres(bea) > matBEA = projectRaster(mat,bea) > mat class : RasterLayer dimensions : 289, 288, (nrow, ncol, ncell) resolution : , (x, y) extent : 0, 12, , (xmin, xmax, ymin, ymax) projection : +proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs +towgs84=0,0,0 values : in memory min value : max value : > matBEA class : RasterLayer dimensions : 169, 288, (nrow, ncol, ncell) resolution : , (x, y) extent : 0, , , (xmin, xmax, ymin, ymax) projection : +proj=cea +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0 +lat_ts=30 values : in memory min value : max value :

9 How does it look?

10 What happened? x = xFromCell(bea,1:ncell(bea)) y = yFromCell(bea,1:ncell(bea)) plot(x,y,pch = ".") xyLL = project(cbind(x,y), "+proj=cea +datum=WGS84 +latts=30”,inverse = T) plot(xyLL,pch = ".")

11 What happened  Grid of points in lat-long (where each point corresponds with a BEA grid cell)  Sample original raster at those points (with interpolation) Identical spacing in x direction Different spacing in y direction

12 What are the units? > matBEA class : RasterLayer dimensions : 169, 288, (nrow, ncol, ncell) resolution : , (x, y) extent : 0, , , (xmin, xmax, ymin, ymax) projection : +proj=cea +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0 +lat_ts=30 values : in memory min value : max value : Meters, along the latitude of true scale (30N and 30S)

13 A break to try things out  Projections

14 Spatially structured data  Tobler’s first law of geography  “Everything is related to everything else, but near things are more related than distant things.”  Waldo Tobler  Nearby data points often have similar conditions  Understanding these patterns can provide insights into the data, and are critical for statistical tests

15 Visualizing spatial structure  The correlogram  Often based on Moran’s I, a measure of spatial correlation Positive autocorrelation Negative autocorrelation

16 Making Correlograms  First goal – get x, y and z vectors x = xFromCell(mat,1:ncell(mat)) y = yFromCell(mat,1:ncell(mat)) z = getValues(mat) assignColors = function(z) { z = (z-min(z,na.rm=T))/(max(z,na.rm=T)-min(z,na.rm=T)) color = rep(NA,length(z)) index = which(!is.na(z)) color[index] = rgb(z[index],0,(1-z[index])) return(color) } plot(x,y,col = assignColors(z),pch = 15, cex = 0.2)

17 Pairwise distance matrix  Making a correlogram starts with a pairwise distance matrix!  (N data points requires ~ N 2 calculations)  Big data sets need to be subsetted down > co = correlog(x,y,z,increment = 10,resamp = 0, latlon = T,na.rm=T) Error in outer(zscal, zscal) : allocMatrix: too many elements specified

18 Pairwise distance matrix  Making a correlogram starts with a pairwise distance matrix!  (N data points requires ~ N 2 calculations)  Big data sets need to be subsetted down  sample() can help us do this > co = correlog(x,y,z,increment = 100,resamp = 0, latlon = T,na.rm=T) Error in outer(zscal, zscal) : allocMatrix: too many elements specified > length(x) [1] > length(x)^2 [1]

19 Quick comments on sample()  sample() takes a vector and draws elements out of it > v = c("a","c","f","g","r") > sample(v,3) [1] "r" "f" "c" > sample(v,3,replace = T) [1] "c" "a" "a" > sample(v,6,replace = F) Error in sample(v, 6, replace = F) : cannot take a sample larger than the population when 'replace = FALSE‘ > sample(1:20,20) [1]

20 Sampling  What’s wrong with this?  co = correlog( x[sample(1:length(x),1000,replace = F)], y[sample(1:length(y),1000,replace = F)], z[sample(1:length(z),1000,replace = F)], increment = 10, resamp = 0, latlon = T,na.rm=T)

21 Sampling, the right way > index = sample(1:length(x),1000,replace = F) > co = correlog(x[index],y[index],z[index],increment = 100,resamp = 0, latlon = T,na.rm=T) > plot(co)

22 Autocorrelation significance  Assessed through random permutation tests  Reassign z values randomly to x and y coordinates  Calculate correlogram  Repeat many times  Does the observed correlogram differ from random? > index = sample(1:length(x),1000,replace = F) > co = correlog(x[index],y[index],z[index],increment = 100,resamp = 100, latlon = T,na.rm=T) > plot(co) Downside – this is slow!

23 Significant deviation from random

24 Why is spatial dependence important?  Classic statistical tests assume that data points are independent  Can bias parameter estimates  Leads to incorrect P value estimates  A couple of methods to deal with this (next week!)  Simultaneous autoregressive models  Spatial filters

25 Just for fun – 3d surfaces  R can render cool 3d surfaces  Demonstrate live  rgl.surface() (package rgl)


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