Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Makadirio ya Msongamano wa Msingi wa Nafasi-Wakati (ST-KDE)× | Uchambuzi wa Maeneo Moto (Getis-Ord Gi*)× | |
|---|---|---|
| Nyanja | Uchanganuzi wa Kimaeneo | Uchanganuzi wa Kimaeneo |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 2010 (space-time extension); 1956 (KDE origin) | 1992 |
| Mwanzilishi≠ | Nakaya & Yano (space-time formulation); KDE foundation by Rosenblatt and Parzen | Arthur Getis and J. Keith Ord |
| Aina≠ | Non-parametric density estimation | Local spatial statistic |
| Chanzo asilia≠ | 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 ↗ | Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189-206. DOI ↗ |
| Majina mbadala | ST-KDE, spatiotemporal kernel density estimation, space-time KDE, 3D kernel density estimation | Getis-Ord Gi* statistic, spatial hot spot detection, cluster and outlier analysis, HSA |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | 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. | Hot Spot Analysis uses the Getis-Ord Gi* local spatial statistic to identify geographic locations where high or low attribute values cluster together to a degree that is statistically significant. Each feature is evaluated in relation to its neighbours, producing a z-score that flags genuine spatial hot spots and cold spots against a background of random variation. |
| ScholarGateSeti ya data ↗ |
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