Using my rudimentary knowledge of Python, I was interested in exploring the use of rpy2 to eventually be able to bring together spatial data analysis done in Python, with some higher level tools in R – in this case the powerful graphics library ggplot2 to visualize the results.
My setup is Mac OS 10.7.3, Python 2.7, R 2.14. (R needs to be compiled with ‘–enable-R-shlib’, which the official CRAN binary for Mac is). Also required are Xcode and NumPy.
There is no binary for rpy2 for my configuration available, so I downloaded the source (2.2.3). Extract somewhere, change into the rpy2-2.2.3 directory and install with:
sudo python setup.py build install
The Python code below takes a csv file (output from a some prior geoprocessing done with ArcPy) and produces a graphic with a map and a scatterplot – see the comments for further details.
Even though several examples of great circle visualizations exist by now, I had not seen the code of one made with ggplot2. Both solutions offered, here using plot and here using lattice, basically loop through the great circle lines ordered from low to high number of flights and overplot the lines with fewer counts, which are plotted in a light color with those with higher counts, which are plotted in a dark color.
In ggplot we can simply use the alpha parameter for transparency in combination with scale_colour_gradient to obtain a similar effect.
After last year’s experience with taking iPad to the field I am finally getting around to reporting back on the 4 week long field research experience with iPad this summer, when we were able to equip our whole field school team (the faculty, a graduate student TA, three undergraduate students and myself) with iPads. Goal for this year was to more extensively explore iPad as a team and to see where it might be useful as a research tool.