Video recording and production done by Enthought.
As the field of climate modeling continues to mature, we must anticipate the practical implications of the climatic shifts predicted by these models. In this talk, I'll show how we apply the results of climate change models to predict shifts in agricultural zones across the western US. I will outline the use of the Geospatial Data Abstraction Library (GDAL) and Scikit-Learn (sklearn) to perform supervised classification, training the model using current climatic conditions and predicting the zones as spatially-explicit raster surfaces across a range of future climate scenarios. Finally, I'll present a python module (pyimpute) which provides an API to optimize and streamline the process of spatial classification and regression problems.
This talk will consist of four parts:
A brief overview of climate data and the concept of agro-ecological zones
The theory and intuition behind bioclimatic envelope modeling using supervised classification
Visualization and interpretation of our results
Detailed demonstration of the pyimpute/GDAL/sklearn workflow
Loading spatial data into numpy arrays
Random stratified sampling
Training, assessing and selecting the sklearn classifier
Prediction of zones given future climate data as explanatory variables
Quantifying and interpreting uncertainty
Writing results to spatial data formats
Discussion of performance and memory limitations
Visualizing and interacting with the results