Border analysis for spatial clusters.
Border analysis for spatial clusters.
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DOI：
10.1186/s12942-018-0124-1
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发表时间：
2018-02-17
影响因子：
4.9
通讯作者：
Kulldorff M
中科院分区：
文献类型：
--
作者：
Oliveira FLP;Cançado ALF;de Souza G;Moreira GJP;Kulldorff M
空间扫描统计被公共卫生专业人员广泛用于非齐次点过程中的空间聚类检测。最流行的空间扫描统计版本使用圆形扫描窗口。还可以使用其他几种使用其他参数或非参数形状的变体。然而，它们都没有提供有关检测到的簇边界的不确定性的信息。我们提出了一种新方法来评估通过泊松数据的空间扫描统计识别的空间簇边界的不确定性。对于每个空间数据位置 i，计算函数 F(i)。虽然不是概率，但该函数采用 [0, 1] 区间内的值，值越高表明该位置属于真实聚类的证据越多。通过一组模拟研究，我们表明 F 函数提供了一种定义、测量和可视化属于真实簇的每个特定位置的确定性或不确定性的方法。无论地图上检测到一个还是多个簇，都可以应用该方法。我们在巴西米纳斯吉拉斯州有关恰加斯病的数据集上说明了这种新方法。给予某个区域的强度越高，该特定区域属于真实簇（如果存在）的可能性就越高。这样，F 函数提供的信息可供公共卫生从业者对检测到的空间扫描统计聚类进行边界分析。我们在空间聚合泊松数据的圆形空间扫描统计的背景下实现并说明了边界分析 F 函数。该定义显然与扫描窗口的形状和生成数据的概率模型无关。为了使新方法广泛供用户使用，它已在免费提供的 SaTScan 软件 www.satscan.org 中实施。
The spatial scan statistic is widely used by public health professionals in the detection of spatial clusters in inhomogeneous point process. The most popular version of the spatial scan statistic uses a circular-shaped scanning window. Several other variants, using other parametric or non-parametric shapes, are also available. However, none of them offer information about the uncertainty on the borders of the detected clusters. We propose a new method to evaluate uncertainty on the boundaries of spatial clusters identified through the spatial scan statistic for Poisson data. For each spatial data location i, a function F(i) is calculated. While not a probability, this function takes values in the [0, 1] interval, with a higher value indicating more evidence that the location belongs to the true cluster. Through a set of simulation studies, we show that the F function provides a way to define, measure and visualize the certainty or uncertainty of each specific location belonging to the true cluster. The method can be applied whether there are one or multiple detected clusters on the map. We illustrate the new method on a data set concerning Chagas disease in Minas Gerais, Brazil. The higher the intensity given to an area, the higher the plausibility of that particular area to belong to the true cluster in case it exists. This way, the F function provides information from which the public health practitioner can perform a border analysis of the detected spatial scan statistic clusters. We have implemented and illustrated the border analysis F function in the context of the circular spatial scan statistic for spatially aggregated Poisson data. The definition is clearly independent of both the shape of the scanning window and the probability model under which the data is generated. To make the new method widely available to users, it has been implemented in the freely available SaTScan software www.satscan.org.
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DOI：
10.1007/978-0-8176-4749-0_6
发表时间：
2009-01-01
期刊：
SCAN STATISTICS: METHODS AND APPLICATIONS
影响因子：
--
作者：
Costa, Marcelo Azevedo;Kulldorff, Martin
通讯作者：
Kulldorff, Martin
DOI：
10.1198/106186006x112396
发表时间：
2006-06-01
影响因子：
2.4
作者：
Duczmal, L;Kulldorff, M;Huang, L
通讯作者：
Huang, L
影响因子：
1.8
作者：
Duczmal, Luiz;Cancado, Andre L. F.;Bessegato, Lupercio E.
通讯作者：
Bessegato, Lupercio E.
影响因子：
--
作者：
DWASS, M
通讯作者：
DWASS, M
影响因子：
2.3
作者：
Elliott, P;Martuzzi, M;Shaddick, G
通讯作者：
Shaddick, G