Comparison of Spline with Kriging in an Epidemiological Problem

TL;DR

This paper compares spline and Kriging methods for spatial data interpolation in epidemiology, finding that Kriging outperforms spline in a real two-dimensional dataset.

Contribution

It elucidates the relationship between spline and Kriging methods and evaluates their performance on epidemiological spatial data.

Findings

Kriging performs better than Spline for the dataset analyzed.

The paper demonstrates the practical differences between the two methods.

It provides insights into method selection for spatial epidemiological data.

Abstract

There are various methods to analyze different kinds of data sets. Spatial data is defined when data is dependent on each other based on their respective locations. Spline and Kriging are two methods for interpolating and predicting spatial data. Under certain conditions, these methods are equivalent, but in practice they show different behaviors. Amount of data can be observed only at some positions that are chosen as positions of sample points, therefore, prediction of data values in other positions is important. In this paper, the link between Spline and Kriging methods is described, then for an epidemiological two dimensional real data set, data is observed in geological longitude and in latitude dimensions, and behavior of these methods are investigated. Comparison of these performances show that for this data set, Kriging method has a better performance than Spline method.