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| Anggaran Ketumpatan Kernel Ruang-Masa (ST-KDE)× | Autokorelasi Spatial Ruang-Masa× | |
|---|---|---|
| Bidang | Analisis Reruang | Analisis Reruang |
| Keluarga | Regression model | Regression model |
| Tahun asal≠ | 2010 (space-time extension); 1956 (KDE origin) | 1981–1992 |
| Pengasas≠ | Nakaya & Yano (space-time formulation); KDE foundation by Rosenblatt and Parzen | Cliff & Ord; extended by Anselin and others |
| Jenis≠ | Non-parametric density estimation | Spatial autocorrelation statistic |
| Sumber perintis≠ | Nakaya, T., & Yano, K. (2010). Visualising crime clusters in a space-time cube: An exploratory data-analysis approach using space-time kernel density estimation and scan statistics. Transactions in GIS, 14(3), 223-239. DOI ↗ | Clifford, P., Richardson, S., & Hemon, D. (1989). Assessing the significance of the correlation between two spatial processes. Biometrics, 45(1), 123–134. DOI ↗ |
| Alias | ST-KDE, spatiotemporal kernel density estimation, space-time KDE, 3D kernel density estimation | STSA, spatiotemporal autocorrelation, space-time Moran's I, temporal spatial dependence |
| Berkaitan | 5 | 5 |
| Ringkasan≠ | Space-Time Kernel Density Estimation extends classical KDE into three dimensions — two spatial and one temporal — to reveal how the intensity of point events (crimes, accidents, disease cases) varies continuously across both geographic space and time. It produces a smooth probabilistic surface that highlights where and when events concentrate most densely. | Space-Time Spatial Autocorrelation extends classic spatial autocorrelation measures — most notably Moran's I — to data that vary across both geographic units and time periods. It detects whether nearby locations that are also temporally close tend to share similar attribute values, revealing clusters, trends, or anomalies that purely spatial or purely temporal analyses would miss. |
| ScholarGateSet data ↗ |
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