Regression modelGIS / spatial
时空空间自相关
时空空间自相关将经典的度量方法——最著名的是莫兰指数I——扩展到跨越地理单元和时间段变化的数据。它检测在空间上邻近且在时间上也接近的位置是否倾向于共享相似的属性值，从而揭示纯空间或纯时间分析会遗漏的聚类、趋势或异常。
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Method map
The neighbourhood of related methods — select a node to explore.
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来源
- Clifford, P., Richardson, S., & Hemon, D. (1989). Assessing the significance of the correlation between two spatial processes. Biometrics, 45(1), 123–134. DOI: 10.2307/2532039 ↗
- Anselin, L., & Getis, A. (1992). Spatial statistical analysis and geographic information systems. The Annals of Regional Science, 26(1), 19–33. DOI: 10.1007/BF01581478 ↗
如何引用本页
ScholarGate. (2026, June 3). Space-Time Spatial Autocorrelation Analysis. ScholarGate. https://scholargate.app/zh/spatial-analysis/space-time-spatial-autocorrelation
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 地理加权回归 (GWR)空间分析↔ compare
- 全局莫兰指数空间分析↔ compare
- 局部莫兰指数 (LISA)空间分析↔ compare
- 空间滞后模型（SAR / 空间自回归）空间分析↔ compare
- 空间面板数据模型（固定效应/随机效应）空间分析↔ compare