Extending Getis–Ord Statistics to Account for Local Space–Time Autocorrelation in Spatial Panel Data. Issue 3 (2nd July 2020)

• Record Type: Journal Article
• Authors: Wang, Zheye; Lam, Nina S. N.
• Is Part Of: Professional geographer. Volume 72:Issue 3(2020)
• Page Start: 411
• Page End: 420
• Publication Date: 2020-07-02
• Subjects: exploratory space–time data analysis (ESTDA) -- local space–time autocorrelation -- space–time Gi and Gi* -- space–time contemporaneous weight matrix -- space–time lagged weight matrix; 时空数据探索分析 (ESTDA) -- 局部时空自相关 -- 时空Giand Gi* -- 时空同期权重矩阵 -- 时空滞后权重矩阵。; análisis exploratorio de datos del espacio–tiempo (ESTDA) -- autocorrelaciãn del espacio–tiempo local -- espacio–tiempo Gi y Gi* -- matriz de peso contemporánea del espacio–tiempo -- matriz de peso rezagada del espacio–tiempo; Geography -- Periodicals; 910
• DOI: 10.1080/00330124.2019.1709215
• Languages: English
• ISSNs: 0033-0124

Abstract

Abstract : 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 G i and G i * 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.