My research seeks to quantify and explain patterns of variability as they relate to specific soil properties (such as nutrients, physical structure, ect.) There are patterns in the data itself (distribution shapes such as normal or bimodal, skewness, and variance) and in the spatial distribution of those data values.
I wish to learn more about tools that can characterize these data distributions and spatial patterns (emphasis on spatial for this class). It is especially a challenge because soil variables often don’t follow well-known distribution such as Gaussian or Exponential. This leaves me wary about the use of certain mathematical tools that requires assumptions such as a normal distribution. The central limit theorem does not apply when we move beyond questions about the mean.
At this point I do not have a specific question in mind, and I should also mention I haven’t collected any data for this project yet (I’m just starting lab analysis this spring). I do not have a specific spatial question, rather I’d to learn about various classification and interpolation methods.
Some background info on soil for the curios
A blurb from the Soil Science Society of America on the importance of soil:
Soil provides ecosystem services critical for life; soil acts as a water filter and a growing medium; provides habitat for billions of organisms, contributing to biodiversity; and supplies most of the antibiotics used to fight diseases. Humans use soil as a holding facility for solid waste, filter for wastewater, and foundation for our cities and towns. Finally, soil is the basis of our nation’s agroecosystems which provide us with feed, fiber, food and fuel.
On the source of soil variability:
Soil is HIGHLY heterogeneous. It is a mix of weathered rock minerals, plant organic matter, liquid, and gas. It’s been forming for thousands of years. A multitude of environmental variables affect that formation at spatial scales from nanometer bacterial interaction to varying climate across landscapes. The real challenge is that variability increases as a function of spatial area under consideration. The variability of a 0.5m X 0.5m plot is different than that of a 5m x 5m and is different than a 50m x 50m plot and so on.
A graphical look at shifting soil scale and methods of characterizing variability
Hi Max,
Thanks for the great introduction to your research. For the purposes of this course, you should formulate a problem you want to address. It is important not skip this first stage of spatial analysis.
I spoke to Julia and if you don’t have a specific question yet, does a question come to mind related to the spatial relationship between organic carbon and vegetation?
For more information on the stages of spatial analysis, see:
http://www.spatialanalysisonline.com/HTML/?spatial_analysis_as_a_process.htm
Thanks, Kuuipo
Hi Max,
Your post made me think of a question that often comes up with the spatial statistics tools… most often with the Hot Spot Analysis (Getis-Ord Gi*) tool. Because the Hot Spot Analysis (HSA) tool reports results as z-scores, some people think that you can only use it when your data are normally distributed. In fact, this tool is asymptoticially normal… as long as you ensure each feature has at least a few neighbors, and none of the features have all or almost all other features as a neighbor, you can have confidence that your results are valid even if the underlying data are skewed 🙂 The citation for this is either Getis and Ord, 1992 or Getis and Ord, 1995 (the full citations are in the Learn More About How Hot Spot Analysis Works).
I’m reading posts bottom to top here, so you may have already discovered this 🙂
I hope this is helpful.
Best wishes!
Lauren