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In the spatial point process context, kernel intensity estimation has beenmainly restricted to exploratory analysis due to its lack of consistency.Different methods have been analysed to overcome this problem, and theinclusion of covariates resulted to be one possible solution. In this paper wefocus on de\-fi\-ning a theoretical framework to derive a consistent kernelintensity estimator using covariates, as well as a consistent smooth bootstrapprocedure. We define two new data-driven bandwidth selectors specificallydesigned for our estimator: a rule-of-thumb and a plug-in bandwidth based onour consistent bootstrap method. A simulation study is accomplished tounderstand the performance of our proposals in finite samples. Finally, wedescribe an application to a real data set consisting of the wildfires inCanada during June 2015, using meteorological information as covariates.

Bootstrapping kernel intensity estimation for nonhomogeneous point processes depending on spatial covariates