For a given region, we have a dataset composed of car theft locations along with a linked dataset of recovery locations which, due to partial recovery, is a relatively small subset of the set of theft locations. For an investigator seeking to understand the behavior of car thefts and recoveries in the region, several questions are addressed. Viewing the set of theft locations as a point pattern, can we propose useful models to explain the pattern? What types of predictive models can be built to learn about recovery location given theft location? Can the dependence between the point pattern of theft locations and the point pattern of recovery locations be formalized? Can the flow between theft sites and recovery sites be captured?

Origin–destination modeling offers a natural framework for such problems. However, here the data is not for areal units but rather is a pair of dependent point patterns, with the recovery point pattern only partially observed. We offer modeling approaches for investigating the questions above and apply the approaches to two datasets. One is small from the state of Neza in Mexico with areal covariate information regarding population features and crime type. The second, a much larger one, is from Belo Horizonte in Brazil but lacks potential predictors.

对于给定的区域，我们有一个由汽车盗窃地点组成的数据集，以及一个链接的恢复地点数据集，由于部分恢复，该数据集是盗窃地点集的一个相对较小的子集。对于寻求了解该地区盗车和盗窃行为的调查员，需要解决几个问题。将盗窃位置集视为点模式，我们是否可以提出有用的模型来解释这种模式？可以建立哪种类型的预测模型来了解给定盗窃地点的恢复地点？可以确定盗窃地点的点模式与恢复地点的点模式之间的依存关系吗？是否可以捕获盗窃站点和恢复站点之间的流量？

起点-目的地建模为此类问题提供了自然的框架。但是，这里的数据不是面积单位，而是一对从属点模式，恢复点模式仅被部分观察到。我们提供用于调查上述问题的建模方法，并将这些方法应用于两个数据集。一个是来自墨西哥内萨州的小数据，其中包含有关人口特征和犯罪类型的区域协变量信息。第二个是更大的一个，来自巴西的贝洛奥里藏特，但缺乏潜在的预测因素。