A geostatistical approach to linking geographically aggregated data from different sources
A geostatistical approach to linking geographically aggregated data from different sources
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DOI：
10.1198/106186007x179257
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发表时间：
2007-03-01
影响因子：
2.4
通讯作者：
Young, Linda J.
中科院分区：
文献类型：
--
作者：
Gotway, Carol A.;Young, Linda J.
数字空间数据的广泛提供和地理信息系统的能力使人们能够容易地综合各种来源的空间数据。通常情况下，数据是在不同的地理尺度上收集的，每个尺度可能与感兴趣的尺度不同。地理信息系统毫不费力地处理这些类型的问题，通过光栅和光栅操作的基础上比例分配和质心平滑技术。然而，这些技术不提供估计值的不确定性度量，并且缺乏纳入可用于改进估计值的重要协变量信息的能力。他们还经常忽略不同的空间支持（例如，数据的形状和方向）。另一方面，支助变化问题的统计解决办法相当具体，难以执行。在这篇文章中，我们提出了一个通用的地理统计框架，用于连接来自不同来源的地理数据。该框架包括空间数据的聚合和分解，以及涉及重叠地理单元的预测问题。它明确地纳入了数据的支持，可以调整协变量值测量不同的空间单位在不同的尺度上，提供了一个衡量的不确定性所产生的预测，并计算在GIS中是可行的。我们开发的新框架还包括一个新的方法，同时估计的平均值和协方差函数从汇总数据使用广义估计方程。
The widespread availability of digital spatial data and the capabilities of Geographic Information Systems (GIS) make it possible to easily synthesize spatial data from a variety of sources. More often than not, data have been collected at different geographic scales, and each of the scales may be different from the one of interest. Geographic information systems effortlessly handle these types of problems through raster and geoprocessing operations based on proportional allocation and centroid smoothing techniques. However, these techniques do not provide a measure of uncertainty in the estimates and lack the ability to incorporate important covariate information that may be used to improve the estimates. They also often ignore the different spatial supports (e.g., shape and orientation) of the data. On the other hand, statistical solutions to change-of-support problems are rather specific and difficult to implement. In this article, we present a general geostatistical framework for linking geographic data from different sources. This framework incorporates aggregation and disaggregation of spatial data, as well as prediction problems involving overlapping geographic units. It explicitly incorporates the supports of the data, can adjust for covariate values measured on different spatial units at different scales, provides a measure of uncertainty for the resulting predictions, and is computationally feasible within a GIS. The new framework we develop also includes a new approach for simultaneous estimation of mean and covariance functions from aggregated data using generalized estimating equations.