Linganisha mbinu

	Uainishaji wa Picha za Anga na Wakati kwa Kutumia Mbinu za Kurekodi kwa Mbali	Uchambuzi wa Maeneo Moto (Getis-Ord Gi*)
Nyanja	Uchanganuzi wa Kimaeneo	Uchanganuzi wa Kimaeneo
Familia	Regression model	Regression model
Mwaka wa asili	1980s-2000s	1992
Mwanzilishi	Woodcock, Zhu, and remote sensing community	Arthur Getis and J. Keith Ord
Aina	Multi-temporal image classification	Local spatial statistic
Chanzo asilia	Zhu, Z. (2017). Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 370-384.	Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189-206.
Majina mbadala	multi-temporal remote sensing classification, spatio-temporal image classification, temporal remote sensing analysis, STRSC	Getis-Ord Gi* statistic, spatial hot spot detection, cluster and outlier analysis, HSA
Zinazohusiana	4	5
Muhtasari	Space-Time Remote Sensing Classification extends standard image classification to multi-temporal satellite or aerial imagery, enabling analysts to track land cover change, phenological cycles, and environmental dynamics across both space and time. By incorporating the temporal dimension, classifiers achieve higher accuracy and can detect transitions that a single-date analysis would miss.	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.