Local Moran and local G-statistic are commonly used to identify high-value (hot spot) and low-value (cold spot) spatial clusters for various purposes. However, these popular tools are based on the concept of spatial autocorrelation or association (SA), but do not explicitly consider if values are high or low enough to deserve attention. Resultant clusters may not include areas with extreme values that practitioners often want to identify when using these tools. Additionally, these tools are based on statistics that assume observed values or estimates are highly accurate with error levels that can be ignored or are spatially uniform. In this article, problems associated with these popular SA-based cluster detection tools were illustrated. Alternative hot spot-cold spot detection methods considering estimate error were explored. The class separability classification method was demonstrated to produce useful results. A heuristic hot spot-cold spot identification method was also proposed. Based on user-determined threshold values, areas with estimates exceeding the thresholds were treated as seeds. These seeds and neighboring areas with estimates that were not statistically different from those in the seeds at a given confidence level constituted the hot spots and cold spots. Results from the heuristic method were intuitively meaningful and practically valuable.

中文翻译：

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当前空间聚类检测实践中的问题和探索替代方法

局部 Moran 和局部 G 统计量通常用于识别高值（热点）和低值（冷点）空间集群，用于各种目的。然而，这些流行的工具基于空间自相关或关联 (SA) 的概念，但没有明确考虑值是否高或低到值得关注。结果聚类可能不包括从业者在使用这些工具时通常想要识别的具有极端值的区域。此外，这些工具基于假设观测值或估计值高度准确的统计数据，误差水平可以忽略或在空间上是均匀的。在本文中，说明了与这些流行的基于 SA 的集群检测工具相关的问题。探索了考虑估计误差的替代热点-冷点检测方法。类可分性分类方法被证明可以产生有用的结果。还提出了一种启发式热点-冷点识别方法。根据用户确定的阈值，估计值超过阈值的区域被视为种子。在给定的置信水平下，这些种子和邻近区域的估计值与种子中的估计值在统计上没有差异，构成了热点和冷点。启发式方法的结果具有直观意义和实际价值。在给定的置信水平下，这些种子和邻近区域的估计值与种子中的估计值在统计上没有差异，构成了热点和冷点。启发式方法的结果具有直观意义和实际价值。在给定的置信水平下，这些种子和邻近区域的估计值与种子中的估计值在统计上没有差异，构成了热点和冷点。启发式方法的结果具有直观意义和实际价值。