spatial-temporal-prediction-of-climate-change-imp.json

{
  "alias": "video/2794/spatial-temporal-prediction-of-climate-change-imp",
  "category": "SciPy 2014",
  "copyright_text": "https://www.youtube.com/t/terms",
  "description": "As the field of climate modeling continues to mature, we must anticipate\nthe practical implications of the climatic shifts predicted by these\nmodels. In this talk, I'll show how we apply the results of climate\nchange models to predict shifts in agricultural zones across the western\nUS. I will outline the use of the Geospatial Data Abstraction Library\n(`GDAL <http://www.gdal.org/>`__) and Scikit-Learn\n(`sklearn <http://scikit-learn.org/>`__) to perform supervised\nclassification, training the model using current climatic conditions and\npredicting the zones as spatially-explicit raster surfaces across a\nrange of future climate scenarios. Finally, I'll present a python module\n(`pyimpute <https://github.com/perrygeo/pyimpute>`__) which provides an\nAPI to optimize and streamline the process of spatial classification and\nregression problems.\n\nOutline\n^^^^^^^\n\nThis talk will consist of four parts:\n\n1. A brief overview of climate data and the concept of agro-ecological\n   zones\n2. The theory and intuition behind bioclimatic envelope modeling using\n   supervised classification\n3. Visualization and interpretation of our results\n4. Detailed demonstration of the pyimpute/GDAL/sklearn workflow\n\n   -  Loading spatial data into numpy arrays\n   -  Random stratified sampling\n   -  Training, assessing and selecting the sklearn classifier\n   -  Prediction of zones given future climate data as explanatory\n      variables\n   -  Quantifying and interpreting uncertainty\n   -  Writing results to spatial data formats\n   -  Discussion of performance and memory limitations\n   -  Visualizing and interacting with the results\n\n\n",
  "duration": null,
  "id": 2794,
  "language": "eng",
  "quality_notes": "",
  "recorded": "2014-07-13",
  "related_urls": [
    "http://scikit-learn.org/",
    "http://www.gdal.org/",
    "https://github.com/perrygeo/pyimpute"
  ],
  "slug": "spatial-temporal-prediction-of-climate-change-imp",
  "speakers": [
    "Matthew Perry"
  ],
  "summary": "In this talk, I019ll show how we apply climate change models to predict\nshifts in agricultural zones across the western US. I will outline the\nuse of the pyimpute, GDAL and scikit-klearn to perform supervised\nclassification; training a model using current climatic conditions to\npredict spatially-explicit zones under future climate scenarios.\n",
  "tags": [
    "Tech"
  ],
  "thumbnail_url": "https://i1.ytimg.com/vi/7mmbRNE9VsA/hqdefault.jpg",
  "title": "Spatial-Temporal Prediction of Climate Change Impacts using pyimpute, scikit learn and GDAL",
  "videos": [
    {
      "length": 0,
      "type": "youtube",
      "url": "https://www.youtube.com/watch?v=7mmbRNE9VsA"
    }
  ]
}