So sánh phương pháp

	Ước lượng mật độ hạt nhân không-thời gian (ST-KDE)	Tự tương quan không gian thời gian
Lĩnh vực	Phân tích không gian	Phân tích không gian
Họ	Regression model	Regression model
Năm ra đời	2010 (space-time extension); 1956 (KDE origin)	1981–1992
Người khởi xướng	Nakaya & Yano (space-time formulation); KDE foundation by Rosenblatt and Parzen	Cliff & Ord; extended by Anselin and others
Loại	Non-parametric density estimation	Spatial autocorrelation statistic
Công trình gốc	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.	Clifford, P., Richardson, S., & Hemon, D. (1989). Assessing the significance of the correlation between two spatial processes. Biometrics, 45(1), 123–134.
Tên gọi khác	ST-KDE, spatiotemporal kernel density estimation, space-time KDE, 3D kernel density estimation	STSA, spatiotemporal autocorrelation, space-time Moran's I, temporal spatial dependence
Liên quan	5	5
Tóm tắt	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.