A Statistical Method for the Detection of Geographic Clustering
A Statistical Method for the Detection of Geographic Clustering
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DOI：
10.1111/j.1538-4632.2001.tb00445.x
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发表时间：
2010-09
影响因子：
3.6
通讯作者：
P. Rogerson
中科院分区：
文献类型：
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作者：
P. Rogerson
基于核的空间变量平滑估计值在探索性分析中很有用，因为它们可以清晰地显示基础变量的地理变异性。在本文中，我提出了一种方法来评估的平滑内核的应用程序的结果在表面上的峰值的意义。该方法也可以被认为是一种方法，用于评估一组适当定义的局部统计数据中的最大值。首先使用高斯核来定义规则网格单元上的数据的局部统计。然后使用积分几何的结果来找到最大局部统计量（M）超过给定临界值（M）的概率。提供的近似，使该方法的实施简单。未来的工作将解决其他几个问题与当地的统计数据，已经以这种方式定义，包括边缘效应，以及全球空间自相关的临界值的选择的影响。
Kernel-based, smoothed estimates of spatial variables are useful in exploratory analyses because they yield a clear visual image of geographic variability in the underlying variable. In this paper I suggest an approach for assessing the significance of peaks in the surface that result from the application of the smoothing kernel. The approach may also be thought of as a method for assessing the maximum among a set of suitably defined local statistics. Local statistics for data on a regular grid of cells are first defined by using a Gaussian kernel. Results from integral geometry are then used to find the probability that the maximum local statistic (M) exceeds a given critical value (M). Approximations are provided that make implementation of the approach straightforward. Future work will address several other issues associated with local statistics that have been defined in this way, including edge effects, and the effects of global spatial autocorrelation on the choice of critical value.