Michael Mahoney

Fine-resolution maps of forest aboveground biomass (AGB) effectively represent spatial patterns and can be flexibly aggregated to map subregions by computing spatial averages or totals of pixel-level predictions. However, generalized model-based uncertainty estimation for spatial aggregates requires computationally expensive processes like iterative bootstrapping and computing pixel covariances. Uncertainty estimation for map subregions is critical for enhancing practicality and eventual adoption of model-based data products, as this capability would empower users to produce estimates at scales most germane to management: individual forest stands and ownership parcels. In this study we produced estimates of standard error (SE) associated with spatial averages of AGB predictions for ownership parcels in New York State (NYS). This represents the first model-based uncertainty estimation study to include all four types of uncertainty (reference data, sample variability, residual variability, and auxiliary data), incorporate spatial autocorrelation of model residuals, and use methods compatible with algorithmic modeling. We found that uncertainty attributed to residual variance, largely resulting from spatial correlation of residuals, dominated all other sources for most parcels in the study. These results suggest that improvements to model accuracy will yield the greatest reductions to total uncertainty in regions like the northeastern and midwestern United States where forests are divided into smaller spatial units. Further, we demonstrated that log-log regression relating parcel characteristics (area, perimeter, AGB density, forest cover) to parcel-level SE can accurately estimate uncertainty for map subregions, thus providing a convenient means to empower map users. These findings support transparency in future regional-scale model-based forest carbon accounting and monitoring efforts.

The package waywiser maintained by Mike Mahoney provides ergonomic methods for assessing spatial models.Assessing predictive models of spatial data can be challenging,both because these models are typically built for extrapolating outside the original region represented by training data and due to potential spatially structured errors,with "hot spots" of higher than expected error clustered geographically due to spatial structure in the

The package waywiser maintained by Mike Mahoney provides ergonomic methods for assessing spatial models.Assessing predictive models of spatial data can be challenging,both because these models are typically built for extrapolating outside the original region represented by training data and due to potential spatially structured errors,with "hot spots" of higher than expected error clustered geographically due to spatial structure in the

I'm thrilled to share that waywiser, my R package focused on providing framework-agnostic (but tidymodels-friendly) methods for assessing models fit to spatial data 1 , has passed peer review and been accepted to rOpenSci.

I'm thrilled to share that waywiser, my R package focused on providing framework-agnostic (but tidymodels-friendly) methods for assessing models fit to spatial data 1 , has passed peer review and been accepted to rOpenSci.

Assessing predictive models can be challenging. Modelers must navigate a wide array of evaluation methodologies implemented with incompatible interfaces across multiple packages which may give different or even contradictory results, while ensuring that their chosen approach properly estimates the performance of their model when generalizing to new observations. Assessing models fit to spatial data can be particularly difficult, given that model errors may exhibit spatial autocorrelation, model predictions are often aggregated to multiple spatial scales by end users, and models are often tasked with generalizing into spatial regions outside the boundaries of their initial training data. The waywiser package for the R language attempts to make assessing spatial models easier by providing an ergonomic toolkit for model evaluation tasks, with functions for multiple assessment methodologies sharing a unified interface. Functions from waywiser share standardized argument names and default values, making the user-facing interface simple and easy to learn. These functions are additionally designed to be easy to integrate into a wide variety of modeling workflows, accepting standard classes as inputs and returning size- and type-stable outputs, ensuring that their results are of consistent and predictable data types and dimensions. Additional features make it particularly easy to use waywiser along packages and workflows in the tidymodels ecosystem.