From a statistical viewpoint, characteristics such as ignition time, location and duration are relevant components for wildfire modeling. The observed ignition sites and starting times constitute a space-time point pattern, and a natural framework to model this type of data is via point processes. In this work, we propose a marked Poisson process to model fire patterns in space-time, considering durations as marks. The collected data correspond to fires observed in the Valencian Community, Spain, between 2010 and 2015. The methodology relies on writing the intensity function of such a process, jointly for starting times, locations and durations, as a weighted Dirichlet process mixture model. A particular choice of the kernel that determines such mixture was made, compatible with data features. We conducted posterior inference on some characteristics of interest for understanding wildfire behavior, showing high flexibility to emulate data patterns.

从统计角度来看，诸如点火时间，位置和持续时间之类的特征是野火建模的相关组成部分。观察到的着火点和开始时间构成了一个时空的点模式，并且通过点过程来建模此类数据的自然框架。在这项工作中，我们提出了一个标记的泊松过程，以时空为标记，对时空中的火灾模式进行建模。收集到的数据与2010年至2015年在西班牙巴伦西亚自治区观察到的大火相对应。该方法依赖于编写此过程的强度函数，共同针对开始时间，位置和持续时间，作为加权Dirichlet过程混合物模型。做出了决定这种混合的特定内核选择，与数据功能兼容。