Automating the Process

ModelBuilder

  • Visual programming language to automate geprocessing
  • Easily fix and rerun entire processes and tasks with updated data

Three Levels of Automation

  • Simple Automation
  • Advanced Automation
  • Intelligent Automation

Programming

  • Why program?
    • Can improve productivity by automating routine tasks and eliminating many sources of human error
    • Programs are reproducible
    • Programs can run 24/7
    • Programming can be fun
  • Easy idea: Tell computer what your want it to do
  • Difficult to execute: Hard to translate your thinking to code
  • Frustrations may abound, but remember the goal is to make things easier and it can be fun

The Modeling Process

10 Steps to Develop and Evaluate a Model

Model Simplicity is Key

Incorporating Geographic Information Into Spatial Models

  • Balance need to incorporate more data with complexity of model
  • Ability to extract useful information from a dataset
  • Ability to interpolate useful data from an incomplete dataset through various methods
  • Choices of scale
  • Need to process data to get to appropriate scale (spatial resolution, spatial scale/study area, temporal resolution) to incorporate into model

Uncertainty Propagation

  • Intrinsic Uncertainty - primary data
  • Inherited Uncertainty - secondary data
  • Operational Uncertainty - related to software and hardware in processing
  • Uncertainty in Use - perceptions and misinterpretations
  • Ways in which to measure uncertainty; ways to evaluate and control for some of these

Considerations for Model Development

  • Importance of transparency of how you have used a model; model parameters, data, calculations, and output
  • Transparency increases replicability and “trust” in models and model results
  • Documentation and being able to explain all aspects of a model - type, inputs, assumptions, uncertainty, error
  • iTERATIVE PROCESS
  • Balance between accessibility and expertise