Writing

More NASA Observations Acquire Interest

Some additional followup from Robert Simmon regarding the previous post. I asked more about the “amateur Earth observers” and the intermediate data access. He writes:

The original idea was sparked from the success of amateur astronomers discovering comets. Of course amateur astronomy is mostly about making observations, but we (NASA) already have the observations: the question is what to do with them–which we really haven’t figured out. One approach is to make in-situ observations like aerosol optical thickness (haziness, essentially), weather measurements, cloud type, etc. and then correlate them with satellite data. Unfortunately, calibration issues make this data difficult to use scientifically. It is a good outreach tool, so we’re partnering with science museums, and the GLOBE program does this with schools.

We don’t really have a good sense yet of how to allow amateurs to make meaningful analyses: there’s a lot of background knowledge required to make sense of the data, and it’s important to understand the limitations of satellite data, even if the tools to extract and display it are available. There’s also the risk that quacks with and axe to grind will willfully abuse data to make a point, which is more significant for an issue like climate change than it is for the face on Mars, for example. That’s just a long way of saying that we don’t know yet, and we’d appreciate suggestions.

I’m more of a “face on Mars” guy myself. It’s unfortunate that the quacks even have to be considered, though not surprising from what I’ve seen online. Also worth checking out:

Are you familiar with Web Map Service (WMS)?
http://www.opengeospatial.org/standards/wms
It’s one of the ways we distribute & display our data, in addition to KML.

And one last followup:

Here’s another data source for NASA satellite data that’s a bit easier than the data gateway:
http://daac.gsfc.nasa.gov/techlab/giovanni/

and examples of classroom exercises using data, with some additional data sources folded in to each one:
http://serc.carleton.edu/eet/

The EET holds an “access data workshop” each year in late spring, you may be interested in attending next year.

And with regards to guidelines, Mark Baltzegar (of The Cyc Foundation) sent along this note:

Are you familiar with the ongoing work within the W3C’s Linking Open Data project? There is a vibrant community actively exposing and linking open data.
http://richard.cyganiak.de/2007/10/lod/
http://esw.w3.org/topic/SweoIG/TaskForces/CommunityProjects/LinkingOpenData

More to read and eat up your evening, at any rate.

Thursday, July 31, 2008 | acquire, data, feedbag, parse  
Book

Visualizing Data Book CoverVisualizing Data is my book about computational information design. It covers the path from raw data to how we understand it, detailing how to begin with a set of numbers and produce images or software that lets you view and interact with information. Unlike nearly all books in this field, it is a hands-on guide intended for people who want to learn how to actually build a data visualization.

The text was published by O’Reilly in December 2007 and can be found at Amazon and elsewhere. Amazon also has an edition for the Kindle, for people who aren’t into the dead tree thing. (Proceeds from Amazon links found on this page are used to pay my web hosting bill.)

Examples for the book can be found here.

The book covers ideas found in my Ph.D. dissertation, which is basis for Chapter 1. The next chapter is an extremely brief introduction to Processing, which is used for the examples. Next is (chapter 3) is a simple mapping project to place data points on a map of the United States. Of course, the idea is not that lots of people want to visualize data for each of 50 states. Instead, it’s a jumping off point for learning how to lay out data spatially.

The chapters that follow cover six more projects, such as salary vs. performance (Chapter 5), zipdecode (Chapter 6), followed by more advanced topics dealing with trees, treemaps, hierarchies, and recursion (Chapter 7), plus graphs and networks (Chapter 8).

This site is used for follow-up code and writing about related topics.

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