<![CDATA[Using Kriging models, a complex geostatistical technique for extrapolating and forecasting unknown spatial values based on known data, this study investigates spatial data analysis. Traditional statistical techniques that suppose observations to be independent are considerably challenged by spatial autocorrelation—the tendency for nearby spatial points to show comparable features. The research highlights the application of Kriging to environmental data, especially air quality measurements like PM2.5 concentrations, in order to better comprehend and forecast pollution patterns over several geographical areas. Using both Ordinary and Universal Kriging approaches, the research shows how these methods can efficiently address spatial dependencies, nonstationarity (where data characteristics change across space), and anisotropy (directional spatial variability). Moreover, the research combines Kriging with machine learning algorithms to record more sophisticated spatial interactions, therefore enhancing prediction accuracy. Methods of crossvalidation are used to thoroughly evaluate the models' performance. The study emphasizes how Kriging enables precise spatial predictions, hence giving important information for environmental monitoring and well-informed decision-making]]>
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