Potential Control Location Suitability (PCL) is an estimate of fire control likelihood or probability developed by relating observations of fire control and lack of control with landscape predictor variables thought to influence control likelihood (O’Connor et al. 2017). Observations of fire control success come from the perimeters of historical fires during the period 2002-2021 and observations of control failure come from the same fire interiors. PCL is modeled using the boosted regression tree method, which iteratively fits regression trees to random subsets of the data to minimize model deviance from the training data. Boosted regression is ideal for this application because the models can capture non-linear effects and variable interactions. Landscape predictor variables include road influence, barrier influence, cost distance, resistance to control, flat topography influence, valley topography influence, ridge topography influence, steep topography influence, Suppression Difficulty Index, modeled fire rate of spread, and prior burning extent and timing. PCL models were developed for 29 discrete ecoregions and then mosaicked into a single West-wide layer by averaging values among adjacent models in a 1-km width buffer zone along the ecoregion boundaries. See the full methods report for more details.