Ultrahigh dimensional variable selection for interpolation of geostatistical data: case studies in soil carbon modeling
Student: Benjamin Fitzpatrick
When: Tuesday, 7th March 2017 10:00 AM-11:00 AM
Where: GP-B Block, Level 1, Room 121
- Prof Kerrie Mengersen (Principal)
- Prof Peter Grace
- Prof David Lamb
- Kerrie Mengersen (Chair)
- Peter Grace
- James McGree
- David Lamb
When making inferences concerning the environment, ground truthed data will frequently be available as point referenced (geostatistical) observations accompanied by a rich ensemble of potentially relevant remotely sensed and in-situ observations. Modern soil mapping is one such example characterised by the need to interpolate geostatistical observations from soil cores and the availability of data on large numbers of environmental characteristics for consideration as covariates to aid this interpolation. In this talk I will outline my application of Least Absolute Shrinkage Selection Operator (LASSO) regularized multiple linear regression (MLR), Elastic Net (EN) regularized MLR and Random Forests to build models for predicting full cover maps of soil carbon when the number of potential covariates greatly exceeds the number of observations available (the p > n or ultrahigh dimensional scenario). The talk will commence with an introduction of the approach via the analysis of data from a single field site. The analysis of data from multiple sites where site membership is incorporated into the modelling and the analysis of data where groupings of observations by landscape functional type is incorporated into the modelling will also be presented. I will also present novel visualisations of the results of ultrahigh dimensional variable selection.
Fitzpatrick, B. R., Lamb, D. W., & Mengersen, K. (2016). Ultrahigh Dimensional Variable Selection for Interpolation of Point Referenced Spatial Data: A Digital Soil Mapping Case Study. PLoS ONE, 11(9): e0162489.
Fitzpatrick, B. R., Lamb, D. W., & Mengersen, K. (2016). Assessing Site Effects and Geographic Transferability when Interpolating Point Referenced Spatial Data: A Digital Soil Mapping Case Study.