Boundaries, links and clusters: a new paradigm in spatial analysis?
Boundaries, links and clusters: a new paradigm in spatial analysis?
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DOI：
10.1007/s10651-007-0066-4
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发表时间：
2008-12-01
影响因子：
3.8
通讯作者：
Goovaerts, Pierre
中科院分区：
文献类型：
--
作者：
Jacquez, Geoff M.;Kaufmann, Andy;Goovaerts, Pierre
本文开发和应用新技术，同时检测的边界和集群内的概率框架。新的统计量“小B”（写作B（ij））评估具有不同值的相邻区域之间的边界，以及具有相似值的相邻区域之间的链接。然后通过连接邻接显著高的位置（例如，异常高的发病率），并且通过“链路”连接，使得相邻区域中的值没有显著差异。提出并评价了两种实现集群构建的技术：“大B”和“阶梯”方法。我们比较了这些方法的统计能力和经验I型和II型错误的wombling和当地的莫兰测试。可以使用基于两个连续（例如，非离散）变量。我们还提供了一个“分布免费”的算法的基础上的观测值的restaurant。该方法被施加到模拟数据的边界和集群的位置是已知的，并与集群使用当地的Moran统计和多边形Womble边界进行比较和对比。小B方法的边界检测是可比的多边形摆动的第一类错误，第二类错误和经验统计权力。对于聚类检测，大B和阶梯方法都具有较低的I型和II型错误，并且比局部Moran统计量更强大。新的方法不限于找到一个预先指定的形状，如圆形，椭圆形和甜甜圈的集群，并产生一个更准确的描述的地理变化比替代集群测试，预设一个特定的集群形状。我们建议这些技术在现有的集群和边界检测方法，不提供这样一个全面的空间模式的描述。
This paper develops and applies new techniques for the simultaneous detection of boundaries and clusters within a probabilistic framework. The new statistic "little b" (written b(ij)) evaluates boundaries between adjacent areas with different values, as well as links between adjacent areas with similar values. Clusters of high values (hotspots) and low values (coldspots) are then constructed by joining areas abutting locations that are significantly high (e.g., an unusually high disease rate) and that are connected through a "link" such that the values in the adjoining areas are not significantly different. Two techniques are proposed and evaluated for accomplishing cluster construction: "big B" and the "ladder" approach. We compare the statistical power and empirical Type I and Type II error of these approaches to those of wombling and the local Moran test. Significance may be evaluated using distribution theory based on the product of two continuous (e.g., non-discrete) variables. We also provide a "distribution free" algorithm based on resampling of the observed values. The methods are applied to simulated data for which the locations of boundaries and clusters is known, and compared and contrasted with clusters found using the local Moran statistic and with polygon Womble boundaries. The little b approach to boundary detection is comparable to polygon wombling in terms of Type I error, Type II error and empirical statistical power. For cluster detection, both the big B and ladder approaches have lower Type I and Type II error and are more powerful than the local Moran statistic. The new methods are not constrained to find clusters of a pre-specified shape, such as circles, ellipses and donuts, and yield a more accurate description of geographic variation than alternative cluster tests that presuppose a specific cluster shape. We recommend these techniques over existing cluster and boundary detection methods that do not provide such a comprehensive description of spatial pattern.