Combining Areal and Point Data in Geostatistical Interpolation: Applications to Soil Science and Medical Geography.
Combining Areal and Point Data in Geostatistical Interpolation: Applications to Soil Science and Medical Geography.
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DOI：
10.1007/s11004-010-9286-5
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发表时间：
2010-07-01
影响因子：
2.6
通讯作者：
Goovaerts, Pierre
中科院分区：
文献类型：
--
作者：
Goovaerts, Pierre
空间插值中的一个常见问题是在不同空间支持下测量的数据的组合。例如，可用于绘制疾病风险图的信息通常包括点数据（例如患者和对照者的居住地）和汇总数据（例如在普查跟踪一级记录的社会人口统计和经济属性）。同样，在实地离散地点进行的土壤测量通常辅以分区图（例如土壤或地质图），这些图将土壤属性的空间分布建模为具有恒定值的多边形（区域）的并列。本文提出了一种通用的克里金公式，允许通过使用克里金系统中的面积到面积、面积到点和点到点协方差来组合点和面数据。该程序使用两个数据集：（1）地质图和重金属浓度记录在表层土壤中的瑞士汝拉，（2）发病率晚期乳腺癌诊断的人口普查区和病人的住所在密歇根州的三个县的位置。在第二种情况下，克里金系统包括根据二项分布推导的误差方差项，以说明取决于这些区域中记录的病例总数的发病率可靠性的变化程度。除了在二项式克里金框架下，面积和点（AAP）克里金法确保预测的一致性，使每个映射单元内的插值平均值等于原始面积基准。在不同的人口规模和空间支持的情况下，二项式克立格，泊松克立格和指示克立格之间的关系进行了讨论。敏感性分析表明，较小的平滑和更高的预测精度的新程序，普通和传统的残差克里格的基础上，假设当地的平均值是恒定的每个映射单元。
A common issue in spatial interpolation is the combination of data measured over different spatial supports. For example, information available for mapping disease risk typically includes point data (e.g. patients' and controls' residence) and aggregated data (e.g. socio-demographic and economic attributes recorded at the census track level). Similarly, soil measurements at discrete locations in the field are often supplemented with choropleth maps (e.g. soil or geological maps) that model the spatial distribution of soil attributes as the juxtaposition of polygons (areas) with constant values. This paper presents a general formulation of kriging that allows the combination of both point and areal data through the use of area-to-area, area-to-point, and point-to-point covariances in the kriging system. The procedure is illustrated using two data sets: (1) geological map and heavy metal concentrations recorded in the topsoil of the Swiss Jura, and (2) incidence rates of late-stage breast cancer diagnosis per census tract and location of patient residences for three counties in Michigan. In the second case, the kriging system includes an error variance term derived according to the binomial distribution to account for varying degree of reliability of incidence rates depending on the total number of cases recorded in those tracts. Except under the binomial kriging framework, area-and-point (AAP) kriging ensures the coherence of the prediction so that the average of interpolated values within each mapping unit is equal to the original areal datum. The relationships between binomial kriging, Poisson kriging, and indicator kriging are discussed under different scenarios for the population size and spatial support. Sensitivity analysis demonstrates the smaller smoothing and greater prediction accuracy of the new procedure over ordinary and traditional residual kriging based on the assumption that the local mean is constant within each mapping unit.
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影响因子：
2.6
作者：
Goovaerts, P.
通讯作者：
Goovaerts, P.
DOI：
10.1198/106186007x179257
发表时间：
2007-03-01
影响因子：
2.4
作者：
Gotway, Carol A.;Young, Linda J.
通讯作者：
Young, Linda J.
影响因子：
8.9
作者：
ATTEIA, O;DUBOIS, JP;WEBSTER, R
通讯作者：
WEBSTER, R
影响因子：
2.9
作者：
Liu, Ten-Lin;Juang, Kai-Wei;Lee, Dar-Yuan
通讯作者：
Lee, Dar-Yuan
影响因子：
4.2
作者：
GOOVAERTS, P;JOURNEL, AG
通讯作者：
JOURNEL, AG