Uncertainty Estimates for Tax Parcels in WWE

Proof-of-concept bootstrap estimates of standard error for AGB predictions aggregated within 2019 NYS tax parcels in the Warren, Washington, Essex LiDAR coverage

Lucas Johnson
2022-02-09

Description

SE Computations

The SE computations (by way of variance) generally follow the approach described in the McRoberts “Pretty Pictures” 2011 paper.

  1. To produce a distribution of pixel-level predictions, values were sampled from the 95% conformal interval at each pixel. 100 samples were drawn to produce 100 predictions per pixel.

  2. These 100 pixel predictions were then aggregated within tax parcels to produce 100 aggregate predictions per tax parcel.

  3. For a given parcel, the bootstrapped parcel prediction was computed as the average of the 100 aggregate predictions (2 above).

  4. The bootstrap variance for the parcel was computed as the variance of the 100 aggregate predictions (using results from 2 and 3 above).

  5. Variance was converted to a standard error value (SE) by dividing the square root of the bootstrap variance for a given parcel estimate by the square root of the number of constituent pixels for that given parcel.

Data

The Warren, Washington, Essex LiDAR region was leveraged for this analysis. Note that this area is relatively homogenous with respect to landcover, so we might expect these error estimates to be optimistic relative to other regions.

The LINMOD ensemble predictions were used in this analysis, but were masked to only include LCMAP tree cover and wetland classes. Pixels falling in any other class were set to NA and excluded from the analysis.

2019 tax parcels were used as aggregation units. These aggregation units make sense from an application standpoint, though perhaps more arbitrary units of aggregation would be more suitable.

Results

Uncertainty by Parcel Size

Percent Error by Parcel Size

Standard Error by Parcel Size

Parcel Maps

Corrections

If you see mistakes or want to suggest changes, please create an issue on the source repository.

Citation

For attribution, please cite this work as

Johnson (2022, Feb. 9). CAFRI Labs: Uncertainty Estimates for Tax Parcels in WWE. Retrieved from https://cafri-labs.github.io/acceptable-growing-stock/posts/uncertainty-estimates-for-tax-parcels-in-wwe/

BibTeX citation

@misc{johnson2022uncertainty,
  author = {Johnson, Lucas},
  title = {CAFRI Labs: Uncertainty Estimates for Tax Parcels in WWE},
  url = {https://cafri-labs.github.io/acceptable-growing-stock/posts/uncertainty-estimates-for-tax-parcels-in-wwe/},
  year = {2022}
}