Tomislav Hengl of the University of Amsterdam has published new book. It's jam-packed with 291 pages on mapping and analyzing spatial data using free software including R, SAGA, GRASS, ILWIS and Google Earth, and freely-available map data. The book itself is also available for free, as an Open Access Publication. You can order the book in printed form for US$12.78, or download it for free as a PDF.
Surprisingly (given the title), this book isn't just about visual displays of spatial data. In fact, the first two chapters offer a nice overview of statistical analysis of spatial data (although with a greater focus on continuous-field models than point-process models). If you want a concise overview of regression-kriging, this is a great resource.
Chapter 3 addresses the various software tools you'll use to analyze the data and create the maps. Some care has been taken in considering how the software elements should be integrated, and Hengl recommends a "R on top" model, where R scripts drive the other tools.
This is a clever move: making use of the scripting capabilities of R means you can avoid much of the tedious manual back-and-forth activities that are usually associated with working with several software tools. Hengl offers some other reasons for working with R, too (p. 90):
- It is of high quality — It is a non-proprietary product of international collaboration between top statisticians.
- It helps you think critically — It stimulates critical thinking about problem-solving rather than a push the button mentality.
- It is an open source software — Source code is published, so you can see the exact algorithms being used; expert statisticians can make sure the code is correct.
- It allows automation — Repetitive procedures can easily be automated by user-written scripts or functions.
- It helps you document your work — By scripting in R, anybody is able to reproduce your work (processing metadata). You can record steps taken using history mechanism even without scripting, e.g. by using the savehistory command.
- It can handle and generate maps — R now also provides rich facilities for interpolation and statistical analysis of spatial data, including export to GIS packages and Google Earth.
Chapter 4 covers the various auxiliary data sources available, listing sources global environmental and socio-economic data, and sources of maps and satellite imagery like GADM, Google Earth and MODIS.
The remaining chapters are devoted to worked examples of spatial data analysis and mapping. By working through the examples, you can recreate charts like these (click to enlarge):
One minor complaint: most of the images in the book are in black-and-white (most likely to facilitate the printing process). But at least you have the R scripts and data for all exercises (these, plus updated maps, are available from the book's website), so at least you can re-run the examples in R to recreate them in color.
Anything new about stirling engines in Cal?
Looking for an update. Everything I find from google news is almost a year old. This is from the stirling engine deal with Southern Edison to build a 500MW plant outside of LA.
What geographical areas are best suited for a solar dish farm?
The southwest region of the United States is ideally suited for this. In fact, a solar farm 100 miles by 100 miles could satisfy 100% of the Americaâs annual electrical needs. Solar technology primarily addresses the peak power demands facing utility companies in the Southwest U.S. and other solar-rich areas.
The cost of living and job markets are better than the national average, but the best job strategy is not to go for averages, but look at your specific skills and experiences, figure out which careers that relates to, and then go to that geographical area:
technology - Silicon Valley
finance - New York
There are other factors to consider. How important are mountains? the ocean? good weather? I have met many midwesterners in Acapulco during the winter, and none ever told me
"I got to get back to Omaha. I just miss those snow covered plains."
4,000 Year Old Greenlander
WASHINGTON (Reuters) â Scientists have sequenced the DNA from four frozen hairs of a Greenlander who died 4,000 years ago in a study they say takes genetic technology into several new realms.
Surprisingly, the long-dead man appears to have originated in Siberia and is unrelated to modern Greenlanders, Morten Rasmussen of the University of Copenhagen and colleagues found.
"This provides evidence for a migration from Siberia into the New World some 5,500 years ago, independent of that giving rise to the modern Native Americans and Inuit," the researchers wrote in Thursday's issue of the journal Nature