8 September 10

8.1 Announcements

  • No new announcements today

8.2 Extreme precipitation in Kansas

  • During the next few lectures, I will demonstrate multiple ways that I would go about meeting the goals in assignment #2
  • My process
    • Determine the goals of the study
    • Exploratory data analysis
      • Live demonstration
    • The model building process
      • 1). Choose appropriate PDFs or PMFs for the data, process, and parameter models
      • 2). Choose appropriate mathematical models for the “parameters” or moments of the PDFs/PMFs from step 1.
      • 3). Choose an algorithm fit the statistical model to the data
      • 4). Make statistical inference (e.g., calculate derived quantities and summarize the posterior distribution)
    • Model checking, improvements, validation, and selection (Ch. 6)
  • What we will need to learn
    • How to use R as a geographic information system
    • How to use the hierarchical modeling framework to describe Kriging
      • Hierarchical Bayesian model vs. “empirical” hierarchical model
    • Specialized language used in spatial statistics (e.g., range, nugget, variogram)
    • New general tools from statistics
      • Gaussian process
      • Metropolis and Metropolis–Hastings algorithms
      • Gibbs sampler

8.3 Intro to GIS

  • Spatio-temporal data from a statistical and GIS perspective are quite different
    • Both disciplines, however, usually classify data based on the spatial support of the “data”
  • Data from the GIS perspective
    • Using R as a GIS, there are four main types of “data” that we will use.
      • Shapefiles
      • Raster
      • Points

8.3.2 Raster

8.4 Summary

  • There are entire courses on what we covered today
  • This is an area that is rapidly developing
    • New R packages to automate data downloads
    • New sources of data (e.g., UAS)
    • Best and most up-to-date resources are usually found be doing a Google search
  • Learning how to use R as a GIS can take some time and be frustrating at first