Linganisha mbinu

	Uchanganuzi wa Msongamano wa Kiini cha Paneli	Makadirio ya Msongamano wa Msingi wa Nafasi-Wakati (ST-KDE)
Nyanja	Uchanganuzi wa Kimaeneo	Uchanganuzi wa Kimaeneo
Familia	Regression model	Regression model
Mwaka wa asili	1962 (KDE); panel extension: 1990s–2000s	2010 (space-time extension); 1956 (KDE origin)
Mwanzilishi	Parzen (1962); Silverman (1986); extended to panel contexts in spatial econometrics literature	Nakaya & Yano (space-time formulation); KDE foundation by Rosenblatt and Parzen
Aina	Nonparametric density estimation	Non-parametric density estimation
Chanzo asilia	Parzen, E. (1962). On estimation of a probability density function and mode. Annals of Mathematical Statistics, 33(3), 1065-1076.	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.
Majina mbadala	Panel KDE, longitudinal kernel density estimation, repeated-measures KDE, panel nonparametric density estimation	ST-KDE, spatiotemporal kernel density estimation, space-time KDE, 3D kernel density estimation
Zinazohusiana	5	5
Muhtasari	Panel Kernel Density Estimation (Panel KDE) extends the standard kernel density estimator to panel (longitudinal) data, estimating smooth density surfaces for spatial or attribute variables observed across multiple units and time periods. It reveals how the distribution of a phenomenon shifts, concentrates, or disperses over time and across groups, making it a natural tool for tracking spatial patterns in repeated-measures or panel datasets.	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.