Introduction to Spatiotemporal Kriging: A Powerful Tool for Space-Time Data Imputation In many real-world applications, data are collected across both space and time, think of rainfall measurements across a network of weather stations over several months, or air pollution levels recorded hourly at various urban locations. Analyzing such data requires methods that can account for both spatial and temporal dependencies. This is where spatiotemporal kriging comes in, a geostatistical technique designed to interpolate or predict missing values in datasets that vary across space and time. In this article, I will guide you step by step through the process of performing spatio-temporal kriging for imputing missing values. Consider a spatiotemporal process denoted as: { Z ( s , t ) : ( s , t ) ∈ D ⊆ R d × R } Here, s ∈ R d \mathbf{s} \in \mathbb{R}^d , with d = 2 d = 2 , represents the spatial location (typically latitude and longitude), and t ∈ R denotes time. This formulatio...