Extending Getis–Ord Statistics to Account for Local Space–Time Autocorrelation in Spatial Panel Data
Extending Getis–Ord Statistics to Account for Local Space–Time Autocorrelation in Spatial Panel Data
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DOI：
10.1080/00330124.2019.1709215
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发表时间：
2020-02
期刊：
影响因子：
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通讯作者：
Zheye Wang;N. Lam
中科院分区：
文献类型：
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作者：
Zheye Wang;N. Lam
空间和时间都是地理事件和现象的重要特征维度。虽然探索性空间数据分析（ESDA）可以用于可视化和总结复杂的空间格局，但它在捕捉地理特征的时间动态方面存在局限性。人们努力将时间维度纳入ESDA技术，以检测时空聚类或趋势。然而，能够帮助探索性时空数据分析（ESTDA）的局域时空统计仍然缺乏。聚焦于空间面板数据，我们的工作扩展了Getis-Ord和统计，使用时空同步权重矩阵和时空滞后权重矩阵来解释局部时空自相关。本文的两个应用表明，该方法可以从空间面板数据中总结时空格局，更一致地识别景观变化，并易于将结果用于可视化。
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.