Spatial and Spatio-Temporal Log-Gaussian Cox Processes: Extending the Geostatistical Paradigm
Spatial and Spatio-Temporal Log-Gaussian Cox Processes: Extending the Geostatistical Paradigm
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DOI：
10.1214/13-sts441
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发表时间：
2013-11-01
影响因子：
5.7
通讯作者：
Taylor, Benjamin M.
中科院分区：
文献类型：
--
作者：
Diggle, Peter J.;Moraga, Paula;Taylor, Benjamin M.
本文首先描述了一类log-Gaussian Cox过程（LGCPs）作为空间和时空点过程数据的模型。我们讨论推理，特别关注基于似然推理的计算挑战。然后，我们通过描述四种应用来证明LGCP的实用性：估计空间点过程的强度面；多类型过程中的空间分离研究从空间离散数据构建疾病风险空间连续图；以及实时健康监测。我们认为，这类问题自然适合于地质统计学领域，传统上，地质统计学被定义为在有限数量的位置使用空间离散观测来研究空间连续过程。我们建议，地质统计学更有用的定义是根据它所解决的科学问题的类别，而不是特定的模型或数据格式。
In this paper we first describe the class of log-Gaussian Cox processes (LGCPs) as models for spatial and spatio-temporal point process data. We discuss inference, with a particular focus on the computational challenges of likelihood-based inference. We then demonstrate the usefulness of the LGCP by describing four applications: estimating the intensity surface of a spatial point process; investigating spatial segregation in a multi-type process; constructing spatially continuous maps of disease risk from spatially discrete data; and real-time health surveillance. We argue that problems of this kind fit naturally into the realm of geostatistics, which traditionally is defined as the study of spatially continuous processes using spatially discrete observations at a finite number of locations. We suggest that a more useful definition of geostatistics is by the class of scientific problems that it addresses, rather than by particular models or data formats.