A motivating example for this paper is a large human location information system that collects two types of information on mobile device locations: 1) large amounts of low-accuracy cell tower triangulation (CTT) calculated location data and 2) small amounts of high-accuracy assisted global positioning system (AGPS) pinpointed location data. Integrating the CTT and AGPS data and extracting more complete and accurate location information is important to achieve better estimation of the true spatial density. However, the problem is challenging because there is no direct link between the CTT and APGS data. In this paper, we propose a multiresolution spatial generalized linear mixed model to integrate low-accuracy and high-accuracy spatial count data given no direct link between two data sources. The relationship between the high-accuracy data and low-accuracy data is estimated at a low-resolution level, where the relationship between the two types can be better captured, and then the estimated relationship is propagated to the high-resolution level. Using the high-accuracy data, the location information of the low-accuracy data is flexibly adjusted via spatial random effects that are modeled using a Gaussian process. The proposed method is validated using simulated and real data examples.

本文的一个激动人心的例子是一个大型的人类位置信息系统，该系统收集有关移动设备位置的两种类型的信息：1）大量的低精度蜂窝塔三角测量（CTT）计算的位置数据，以及2）少量的高精度辅助全球定位系统（AGPS）精确定位的位置数据。整合CTT和AGPS数据并提取更完整和准确的位置信息对于实现对真实空间密度的更好估计非常重要。但是，该问题具有挑战性，因为CTT和APGS数据之间没有直接联系。在本文中，我们提出了一种多分辨率空间广义线性混合模型，该模型可在两个数据源之间没有直接链接的情况下集成低精度和高精度空间计数数据。在低分辨率级别估计高精度数据和低精度数据之间的关系，其中可以更好地捕获两种类型之间的关系，然后将估计的关系传播到高分辨率级别。使用高精度数据，可通过使用高斯过程建模的空间随机效应灵活地调整低精度数据的位置信息。所提出的方法通过仿真和真实数据实例得到验证。低精度数据的位置信息可通过使用高斯过程建模的空间随机效应灵活调整。所提出的方法通过仿真和真实数据实例得到验证。低精度数据的位置信息可通过使用高斯过程建模的空间随机效应灵活调整。所提出的方法通过仿真和真实数据实例得到验证。