Space and time are both crucial characteristic dimensions of geographic events and phenomena. Although exploratory spatial data analysis (ESDA) can be used to visualize and summarize complex spatial patterns, it has limitations in capturing the temporal dynamics of geographic features. Efforts have been made to incorporate the time dimension into ESDA techniques to detect space–time clustering or trends. Localized space–time statistics that could help in exploratory space–time data analysis (ESTDA), however, are still lacking. Focusing on spatial panel data, our work extended Getis–Ord and statistics using a space–time contemporaneous weight matrix and a space–time lagged weight matrix to account for local space–time autocorrelation. Two applications in this article show that the newly developed method can be used to summarize space–time patterns from spatial panel data, identify changes of landscape more consistently, and lend the results readily to visualization.

中文翻译：

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扩展 Getis-Ord 统计以解释空间面板数据中的局部时空自相关

空间和时间都是地理事件和现象的关键特征维度。尽管探索性空间数据分析 (ESDA) 可用于可视化和总结复杂的空间模式，但它在捕捉地理特征的时间动态方面存在局限性。已经努力将时间维度纳入 ESDA 技术以检测时空聚类或趋势。然而，仍然缺乏有助于探索性时空数据分析 (ESTDA) 的本地化时空统计数据。专注于空间面板数据，我们的工作使用时空同期权重矩阵和时空滞后权重矩阵扩展了 Getis-Ord 和统计数据，以解释局部时空自相关。