Geospatial information on geographical and human factors improved anthropogenic fire occurrence modeling in the Chinese boreal forest

We applied a classic logistic regression (LR) together with a geographically weighted logistic regression (GWLR) to determine the relationship between anthropogenic fire occurrence and potential driving factors in the Chinese boreal forest, and to test whether the explanatory power of the LR model could be increased by considering geospatial information of geographical and human factors using a GWLR model. Three tests, "all variables", "significant variables" and "cross-validation", were applied to compare model performance between the LR and GWLR models. Our results confirmed the importance of distance to railway, elevation, length of fire line and vegetation cover on fire occurrence in the Chinese boreal forest. In addition, GWLR model performs better than LR in terms of model prediction accuracy, model residual reduction and spatial parameter estimation by considering geospatial information of explanatory variables. This indicates that the global LR model is incapable of identifying underlying causal factors for wildfire modeling sufficiently. The GWLR model helped identify spatial variation between driving factors and fire occurrence, which can contribute better understanding of forest fire occurrence over large geographic areas and the forest fire management practices may be improved based on it.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author