Rich new marked point process data allow researchers to consider disparate problems such as the factors affecting the location and type of police use of force incidents, and the characteristics that impact the location and size of forest fires. We develop a two-stage log Gaussian Cox process that models these data in terms of both spatial (community-level) and nonspatial (individual or event-level) characteristics; both types of covariates are present in the examples we consider and are not easy to incorporate via existing methods. Via simulated and real data examples we find that our model is easy to interpret and flexible, accommodating multiple types of marks and multiple types of spatial covariates. In the first example we consider, our approach allows us to study the impact of community-level socioeconomic features such as unemployment as well as event-level features such as officer tenure on force used by police, illustrated through simulated examples. In our second example we consider factors that impact the locations and severity of forest fires from the Castilla-La Mancha region of Spain between 2004–2007.

丰富的新标记点过程数据使研究人员能够考虑不同的问题，例如影响警察使用武力事件的位置和类型的因素，以及影响森林火灾位置和规模的特征。我们开发了一个两阶段对数高斯 Cox 过程，根据空间（社区级别）和非空间（个人或事件级别）特征对这些数据进行建模；这两种类型的协变量都存在于我们考虑的示例中，并且不容易通过现有方法合并。通过模拟和真实数据示例，我们发现我们的模型易于解释且灵活，可容纳多种类型的标记和多种类型的空间协变量。在我们考虑的第一个例子中，我们的方法使我们能够研究社区层面的社会经济特征（如失业）以及事件层面的特征（如警官任期）对警察使用武力的影响，并通过模拟示例进行说明。在我们的第二个示例中，我们考虑了影响 2004 年至 2007 年期间西班牙卡斯蒂利亚-拉曼恰地区森林火灾发生地点和严重程度的因素。