Essential Steps for Setting up a Modeling Study
Setting up for modeling
•
Remember goals:
–
Desired model and uncertainty
•
Sample area selection
•
What resolution will we model at?
•
What are we modeling?
•
How will we validate the model?
•
Do we have enough sample data or too
much?
•
Do we have the predictor variables we
need?
Sample Area Selection
•
What is the area of interest?
•
How was the area sampled?
–
Optimal: rigorously sampled, randomly
spaced plots
–
Worse case: data integrated from multiple
sources with little documentation on
collection methods
•
How far can we extend the study area
realistically?
•
Best to pad the extent a bit
Sample Area Selection
•
CA – Douglas fir
Sample Area Selection
•
CA – Redwood (coastal)
Sample Area Selection
•
CA - Giant Sequoia
Resolution
•
Higher resolution:
–
Better detail
–
Can be misleading
–
Performance problems
•
Lower resolution:
–
Much faster processing
–
Can hide “niche environments”
•
Recommendation:
–
Use lowest predictor layer resolution for all
layers, or lower
–
Grid to lower resolution as needed
What are we modeling?
•
Need a relationship we can model
•
Measured variables:
–
Height, DBH, Canopy size
–
Weight, length, grain size, proportion
–
Income, longevity
•
Presence/Absence:
–
Logistic regression
•
Occurrences:
–
Point Density?
Sample Data
•
Too many points will overwhelm R
–
Sub-sample
–
Aggregate
•
Too few points will not represent the
phenomenon
–
Get more data?
–
Switch to a simulation
–
Give up?
Predictors
•
Look far and wide
•
Transform predictors as needed
•
Investigate what will predict the
phenomenon
–
Visit the site, talk to “experts”
–
Switch to a simulation?
–
Give up?
Ensure clarity on modeling goals and uncertainties. Select sample areas strategically based on interest and available data. Determine appropriate resolution for modeling. Define variables to model and validate the model effectively. Assess sample data adequacy and predictor variables availability. Explore the modeling process step by step for accurate results.
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Presentation Transcript
Setting up for modeling Remember goals: Desired model and uncertainty Sample area selection What resolution will we model at? What are we modeling? How will we validate the model? Do we have enough sample data or too much? Do we have the predictor variables we need? namNm15
Sample Area Selection What is the area of interest? How was the area sampled? Optimal: rigorously sampled, randomly spaced plots Worse case: data integrated from multiple sources with little documentation on collection methods How far can we extend the study area realistically? Best to pad the extent a bit namNm15
Sample Area Selection CA Douglas fir namNm15
Sample Area Selection CA Redwood (coastal) namNm15
Sample Area Selection CA - Giant Sequoia namNm15
Resolution Higher resolution: Better detail Can be misleading Performance problems Lower resolution: Much faster processing Can hide niche environments Recommendation: Use lowest predictor layer resolution for all layers, or lower Grid to lower resolution as needed namNm15
What are we modeling? Need a relationship we can model Measured variables: Height, DBH, Canopy size Weight, length, grain size, proportion Income, longevity Presence/Absence: Logistic regression Occurrences: Point Density? namNm15
Sample Data Too many points will overwhelm R Sub-sample Aggregate Too few points will not represent the phenomenon Get more data? Switch to a simulation Give up? namNm15
Predictors Look far and wide Transform predictors as needed Investigate what will predict the phenomenon Visit the site, talk to experts Switch to a simulation? Give up? namNm15
