Human activity events are often recorded with their geographic locations and temporal stamps, which form spatial patterns of the events during individual time periods. Temporal attributes of these events help us understand the evolution of spatial processes over time. A challenge that researchers still face is that existing methods tend to treat all events as the same when evaluating the spatiotemporal pattern of events that have different properties. This article suggests a method for assessing the level of spatiotemporal clustering or spatiotemporal autocorrelation that may exist in a set of human activity events when they are associated with different categorical attributes. This method extends the Voronoi structure from 2D to 3D and integrates a sliding-window model as an approach to spatiotemporal tessellations of a space-time volume defined by a study area and time period. Furthermore, an index was developed to evaluate the partial spatiotemporal clustering level of one of the two event categories against the other category. The proposed method was applied to simulated data and a real-world dataset as a case study. Experimental results show that the method effectively measures the level of spatiotemporal clustering patterns among human activity events of multiple categories. The method can be applied to the analysis of large volumes of human activity events because of its computational efficiency.

中文翻译：

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衡量多类别地理事件的时空聚类水平

人们经常将人类活动事件与它们的地理位置和时标一起记录下来，从而形成各个时间段内事件的空间格局。这些事件的时间属性有助于我们了解空间过程随时间的演变。研究人员仍然面临的挑战是，当评估具有不同属性的事件的时空模式时，现有方法倾向于将所有事件视为相同。本文提出了一种评估时空聚类或时空自相关水平的方法，这些水平可能在一组人类活动事件与不同的分类属性相关联时可能存在。此方法将Voronoi结构从2D扩展到3D，并集成了滑动窗口模型，作为研究由研究区域和时间段定义的时空量的时空镶嵌的一种方法。此外，开发了一个指标来评估两个事件类别之一相对于另一类别的部分时空聚类水平。将该方法应用于案例研究的模拟数据和真实数据集。实验结果表明，该方法有效地测量了多类人类活动事件之间的时空聚类模式。由于该方法的计算效率高，因此可以应用于大量人类活动事件的分析。开发了一个指标来评估两个事件类别之一相对于另一类别的部分时空聚类水平。将该方法应用于案例研究的模拟数据和真实数据集。实验结果表明，该方法有效地测量了多类人类活动事件之间的时空聚类模式。由于该方法的计算效率高，因此可以应用于大量人类活动事件的分析。开发了一个指标来评估两个事件类别之一相对于另一个类别的部分时空聚类水平。将该方法应用于案例研究的模拟数据和真实数据集。实验结果表明，该方法有效地测量了多类人类活动事件之间的时空聚类模式。由于该方法的计算效率高，因此可以应用于大量人类活动事件的分析。