Files
Abstract
Our study focuses on modeling wildfire damage in the State of Florida. The approach is
to evaluate wildfire risks in a spatio-temporal framework. A block bootstrapping
method has been proposed to construct a statistical model accounting for explanatory
variables while adjusting for spatial and temporal autocorrelation. Although the
bootstrap (Efron 1979) method can handle independent observations well, the strong
autocorrelation of wildfire risks brings about a major challenge. Motivated by
bootstrapping overlapped blocks methods in an autoregressive time series scenario
(Kunsch 1989) and block bootstrapping method of dependent data from a spatial map
(Hall 1985), we have developed a method to bootstrap overlapping spatio-temporal
blocks. By selecting an appropriate block size, the spatial-temporal correlation can be
eliminated. With our saptio-temporal block bootstrapping approach, impacts of
environmental factors on SPB outbreaks and implications of pine forest management
are assessed. Almost all the explanatory variables, including climate factors, forest
ecosystem and socio-economic conditions have been detected to have significant
impacts. Consequently, our method offers a way to forecast the future burning risks,
given the current influential information of a county.