I recently had some success with my new gigapan robot. Got what I deemed the perfect camera to go with it (Canon Powershot SX110IS 9MP with 10x optical zoom) and dragged it out into the field. I can't embed the images in the blog and am working on getting them on my website. For now you can see them at the following links:
We (I have some partners in crime now) have recently been exploring the application of generalization routines in Arc to one of my excessively detailed published geologic maps. As part of a larger mapping effort (ND2MP: The Nevada Digital Dirt Mapping Project) I am walking the fine line between the rationality of automated generalization and the impracticality of manually generalizing detailed mapping that I have already completed.
A lot of basic concepts of cartography in general and geologic mapping in particular come to the fore when you start visualizing blotch maps (i.e. those based on polygons) at different scales. Some interesting complexities involving the analog to digital map world also arise...those issues will eventually be aired on the ND2MP blog. For now, I will show some of the results of automated generalization routines in Arc.
The detailed map in question is NBMG Map 156, a map of Ivanpah Valley, Nevada that was compiled at ~1:12k but was released in Dead Tree Edition at 1:50k so it would fit on the plotter/tree killer.
After perusing various options, we decided that the 'aggregate' generalization tool was the closest to what we wanted...but not exactly what we wanted. This tool melds polys/blotches together on the basis of only a couple of criteria: how close together two like polys can be before they meld into one, and how small the resulting polys (or holes) can be. Both of these concepts involve deciding on a minimum mappable unit (MMU) dimension (a post and discussion for another day fellow mappers).
The map below is an ungeneralized version of a part of the Ivanpah Valley map (in this case the Jean 7.5 Quad) shown at (roughly) 1:150k:
A generalized version wherein two groups of the most intricately mapped surficial units are aggregated is shown below at the same scale (the yellow and red ones):
At face value, the lower map is a bit more legible. In this instance we aggregated like-polys that were less than 40m apart and eliminated polys (in the same group) that were smaller than 5 ha (50,000 sq. meters). We are considering an MMU of 9 ha for a final compilation of Clark County surficial geology to print (yes...I said print) at 1:150k. Note that the centroids of the eliminated polys will be retained as a point data set in case it actually matters that they are gone.
The generalization routine shown above essentially eliminated numerous reaches of narrow, active desert washes. We are interested in retaining these for various reasons, but maybe only as lines. If anyone has a suggestion for how to extract the lines from the eliminated wash reaches as part of the generalization process (or has a suggestion for a better generalization routine) please speak up!
Here are the maps side by side for better comparison:
I finally made it out into the field to try out the gigapan robotic camera mount. Bottom line....sweet, man. This thing is a cinch to use and a kick to watch the first few times. It went so well that I started a new project that will be intimately linked with my too many other projects.
I have been swamped with many things ungeological at the office and could only make it to a local venue for the experiment...a cutbank along the Truckee River bike trail that I map in my mind each time I ride by it. Was hoping for a bigger splash with my first try, but settled on something simple.
After some basic setup procedures (maybe 5 minutes worth), I watched as my old sony digital camera was forced to take a systematic series of 33 pictures. Note that this is a small number and I could have taken 10s more with a higher resolution lens or a more expansive subject. Explore the gigapan site and you will get an idea of the possibilities.
Using the Gigapan stitcher software, I went from the image above to:
The result is a flawlessly stitched image (yes. I added the goofy deckled edge).
Take a minute to visit the hosted image at Gigapan.org to get a better feel why I think this is a great tool for geology. There you can zoom in and pan around and really check stuff out. The alluvial stratigraphy at this site is pretty straightforward, but you can imagine the insightful fun you could have with a particularly complicated exposure, right? Eventually, I will find out if the white bed is a tephra and get some radiocarbon dates on the organic muck horizons. Once I do that, I will tag the online image with the data.
A database of these types of (geotagged and geoannotated) images would be of great value. I need to ask some questions of others much smarter than I as to how I can add annotations and lines that can be turned on and off, etc.
Want to see some absolutely fabulous examples of what can be shown with gigapixel photography? Sure you do. Then check out the brilliant work of Greg Downing and others at xRez:
The images of Yosemite are amazing. Also look for the images of the Eastern Sierra front and the Alabama Hills. Rumor has it that the Grand Canyon is in the offing. I and a group of like-minded digital geoheads are trying to get Greg to show the xRez stuff at the GSA annual meeting in Portland this year. Stay tuned.
Note also that Dr. Ron Schott has many geologically interesting gigapans that are easily found on the gigapan site by searching on 'geology'. For my AZ pals, he has a lot from your turf...why not check them out and provide some insights you may have?
I have received a lot of input lately...thanks to those who care. I am still enamored with my recent talk title involving the phrase: '...the death knell, yes the death knell, for exclusively paper geologic maps'. But it may have incited some confusion and ire. Please note the intentional insertion of the adjective exclusively. That is a key term here....look it up on Wikipedia (you know, that online resource you dissavow but use all the time).
Maps that are only available in paper form, i.e., Dead Tree Editions (gotta love that one, no?) are of considerably less utility than those that have a viable digital counterpart that can be viewed, analyzed, and widely distributed. Sure, exclusively paper maps are functional, portable, archivable in traditional ways, and fun to hold, but they have a pretty limited application in the 21st century. I stand by that assertion.
That being said, let me enumerate some points:
1. I, yes I, use paper maps in the field. I do not like carrying a computer around at all. Have tried it, don't like it. Hence my enthusiastic endorsement of new digital pen technology that allows for real ink to be applied to real paper only to later be uploaded into a digital form.
The challenge to the modern cartographer is to create aesthetically acceptable analog / dead tree derivatives of digital maps when needed (which, admittedly, is often).
2. I, yes I, love to put paper maps on the wall of my office and garage.
3. I, yes I, have a degree in Geography and Cartography that dates to the days of the freaking Leroy lettering set and very old school ink pen technology.
4. I, yes I, appreciate that some digital maps are inadequately documented in the domain of metadata, but I would like to stress that I have many paper maps that don't come with any metadata or metadata-like data.
I could go on, but you are already tired of me. But wait! I have recently found a post on the OpenGeoData blog (a blog about a digital enterprise that could not be carried out with dead trees) that illustrates some truly novel applications for printed maps. I strongly recommend the links below: