Assessing area-specific relative risks from large forest fire size in Canada

The evaluation of area-specific risks for large fires is of great policy relevance to fire management and prevention. When analyzing data for the burned areas of large fires in Canada, we found that there are dramatic patterns that cannot be adequately modelled by traditional hierarchical modelling assuming spatial autocorrelation. In this paper, we use the robust locally weighted scatterplot smoothing (LOESS) technique to remove spatial and temporal trends; and we account for periodical cycles by employing the relevant periodic functions as covariates in a hierarchical Gamma mixed effects model. Based on the results of this generalized multilevel analysis of large fire size, we provide an area-specific relative risks ranking system for Canada and confirm that lightning tends to cause more severe damage in terms of fire size than human factor. A diagnostic check on the modelling shows that large fires data are reasonably modelled using this combination of semiparametric and mixed effects modelling approaches.