digging out the edge of the hill

Recently I had a possibility and time to look practically at the boundary questions, the ones I always like to philosophy about...  I got a simple task: to calculate the volume of a small heap (much smaller then hill) having an image with elevation values (DEM) and so I just needed to automatize and to simplify the steps that were already determined by others, mostly some routine work with ArcGIS (ESRI) tools, so just a bit of patience learning to geoprocess with python.

Digging the boundary  of a heap

But before starting the calculations I had to find out where is this heap and where it's not anymore. I had to draw clear boundaries in order to start some calculations at all. Apparently there was no tool for a heap determination founded where I work, till now all was done manually, by digitizing: zooming out and in, searching out that boundary where the heap starts or ends and going around.
The human brain is great, fast recognizing the pattern it searches for. Maybe recognizing just approximately, but nicely and unconsciously ignoring all other: small depressions, pikes or elevation model noise due to calculated heights of some bushes or some artificial constructions  or photogrametrical noise...
But the problem is that when having a huge images with a great resolution it takes hours and hours to load it and while zooming back and forward trying to draw a line in order to determine "exactly" where the heap starts or/and ends. Besides, when you start looking at a greater and greater resolution the picture of  all the image disappears and for our brain it becomes hard to process just various tones of colours in pixels in front. So, I needed to find how to automatize this first step in order I could work on the calculations. And just looking rather at a simple task or indeed at the DEM (it's prety nice!),  I fastly ran into a dark dark forest searching the way how to determine where this heap finishes or starts in order to be able to define it and build the script.

visual information cognition: spatial visualization in 3D with GPlates

Obtaining more data and developing methods of analysis, scientists in parallel are working on information visualization as it is the most perceivable and comprehensible way for humans anyone would tell. The visual cognition (visual analysis like perception, acquisition, memorization, of shape properties and spatial relations) is remarkably flexible and efficient (Ullman 1996).

Map creation with Matematica

I believe there is a number of research  in visual spatial cognition done to prove I've just stated above. One research in 3D vs 2D proves that 3D increases the spatial memory, what means one is able to remember at least longer. (Cockburn, 2004), although in general it is very hard to prove something about our brain using the same brain itself.

Indeed, I am not going to argue here if 3D is better then 2D or that visual information is perceived better than textual, as more research on this topic should be done from my side. But all I want to notice, that more and more softwares are developed for spatial information visualization.
If you have a look at the geoweb side, many applications recently launched has a graphical visualization. Statistics are no longer presented as a bunch of numbers, visual interface is developing,as example Gapminder, or new visualization package, google visualization api package in R software.
Map making has reached the peak already - everybody can do it from simply route creation on Google Maps or Google Earth, to table visualization using Google Fusion Tables, or such geoweb applications like GeoCommons, or we can jump to professional map creation and visualization. Already existing desktop applications that help to visualize data are often updated, besides new desktop softwares with an amazing visualization arise.

Let's have a look at such hard subject as plate-tectonics and it's visualization.