Map projection cheat sheet

Here's a cheat sheet for map projection settings for the mapproject function (mapproj package). I've repeated the same general procedure for each map: 1. create blank world map, 2. project colored polygons, 3. overlay country boundaries. Some specifications were needed for individual map projections (i.e. different orientation, lat/lon limits, additional parameters). Most projections work well as long as the limits and orientation are valid - in a few cases country boundaries ('square' and 'hex') were wrapped incorrectly. I was unable to get the two spheroid projections working due to the escape character in their names ('sp\_mercator', 'sp\_albers') (maybe someone has a suggestion here?).

In addition to the maps and mapproj packages, the sinkr package is also used for the calculation of global distance, value to color conversion, and polygon creation.


Script to reproduce (high resolution png and pdf versions):

Create polygons from a matrix

The following function matrix.poly allows for the addition of polygons to a plot based on a matrix and defined matrix positions. I have used this function on occasion to highlight specific matrix locations (e.g. in the above figure). You can do the same by overlaying another image (left in above plot) but with this function you will have all other polygon plotting possibilities (e.g. borders etc.).

Adding a transparent image layer to a plot

The following example shows how to add a transparent image-type layer to a plot. The add.alpha function (below) simply adds transparency to a vector of colors which is then introduced in the "col" argument of an image plot.

XYZ geographic data interpolation, part 3

This will be probably be a final posting on interpolation of xyz data as I believe I have come to some conclusions to my original issues. I show three methods of xyz interpolation:
1. The quick and dirty method of interpolating projected xyz points (bi-linear)
2. Interpolation using Cartesian coordinates (bi-linear)
3. Interpolation using spherical coordinates and geographic distances (thin plate spline)

XYZ geographic data interpolation, part 2

Having recently received a comment on a post regarding geographic xyz data interpolation, I decided to return to my original "xyz.map" function and open it up for easier interpretation. This should make the method easier to adapt and follow.

The above graph shows the distance to Mecca as interpolated from 1000 randomly generated lat/lon data using the "interp" function of the akima package. Several functions, found within this blog, are needed to reproduce the plot (pos2coord, earth.dist, new.lon.lat, color.palette, val2col, image.scale). One thing you will notice is the strip of uninterpolated area within the stereographic projection. This is a problem that I have yet to resolve and has to do with the fact that the interpolation is not considering the connection along the 180° longitude line. This will probably require some other type of interpolation based on geographic distances rather than Cartesian coordinates.


R code to produce the above graph...

Maximum Covariance Analysis (MCA)

Maximum Covariance Analysis (MCA) (Mode 1; scaled) of Sea Level Pressure (SLP) and Sea Surface Temperature (SST) monthly anomalies for the region between -180 °W to -70 °W and +30 °N to -30 °S.  MCA coefficients (scaled) are below. The mode represents 94% of the squared covariance fraction (SCF).

Maximum Correlation Analysis (MCA) is similar to Empirical Orthogonal Function Analysis (EOF) in that they both deal with the decomposition of a covariance matrix. In EOF, this is a covariance matrix based on a single spatio-temporal field, while MCA is based on the decomposition of a "cross-covariance" matrix derived from two fields.

Converting values to color levels

     Adding color to a plot is helpful in many situations for visualizing an additional dimension of the data. Again, I wrote the below function "val2col" in R after having coded this manually over and over in the past. It uses similar arguments as the image function in that one defines the colors to be used as well as optional break points or z-limits by which the data is binned into those colors. The typical situation where I use the function is with the mapping of climate data, yet the addition of color to an XY plot can often be easier on the eyes than adding an additional z-axis spatial dimension. In combination with the image.scale function, that I previously posted, the data can be quickly interpretted.
     As an example, gridded sea level pressure is plotted above as projected polygons using the packages maps and mapproj. Values were converted to colors with the val2col function and the image.scale function plotted a corresponding scale. For those interested in using netcdf files, the example also uses the ncdf package for reading the data files into R.

Adding a scale to an image plot

[NOTE: new version of the image.scale function can be found here: http://menugget.blogspot.de/2013/12/new-version-of-imagescale-function.html.]

Here's a function that allows you to add a color scale legend to an image plot (or probably any plot needing a z-level scale). I found myself having to program this over and over again, and just decided to make a plotting function for future use. While I really like the look of levelplot(), the modular aspect of image() makes it much more handy to combine with other plotting commands or overlays.
For example, as far as I can tell, the simple addition of the triangle symbol to mark the highest point in the above map of Maunga Whau volcano is not possible with levelplot.
After adding this symbol, the function below - image.scale() - was used to add the accompanying color scale to another area of the device.



The function...

Simulating CMYK mis-registration printing

I recently came across a poster advertising a children's production of Shakespeare's The Tempest where they purposely used an effect to mimic a mis-registration in CMYK printing. You have probably seen this before as a slight offset in one of the 4 colors (cyan, magenta, yellow, and black).
     The CMYK color model is "subtractive" in that you end up with a white color (when the paper color is white) when no colors are printed. The opposite is an "additive" color model, such as RGB (red, green, blue), which results in black when none of these three color channels are added. This is more typically used in imaging on lit screens (e.g. color creation in R using the rgb() function).
     I wanted to try simulating this type of mis-registration in R and came up with the following function. For images with white backgrounds, the "subtractive" shift will look best while an "additive" shift works best for black backgrounds. The results are essentially the same, but you can eliminate some color channel striping on the image borders by choosing one or the other.
     This is probably much easier to do in a photo editting program, but I had fun with it nonetheless. I used the excellent package biOps for some of its image reading and manipulation functions.

...the function

map.xyz(): interpolation of XYZ data and projection onto a map

     I am still struggling to get a grasp of R's mapping capabilities. Part of my frustration lies in the fact that I often work on areas near the poles, which complicates interpolation across the 180 degree line. For smaller areas, interpolation can be done using the interp() function in the package akima. I have taken the results from interp and projected the image onto a map. You will need the akima, maps, and mapproj packages and the functions new.lon.lat(), earth.dist(), and pos2coord().


As an example I have mapped the distance from Mecca, Saudi Arabia:

The function...