IDW can be useful in some situations, especially when working with sparse, non-stationary, or highly variable datasets; however, Ordinary Kriging is frequently chosen for its statistical rigor and capacity to produce objective estimates with reduced variance. The particulars of the data and the analysis's goals should guide the decision between the two approaches.

Mahmoud Abdelrahim Abdelgiom But could we not say that sparse, and highly variable datasets affect IDW just as much as kriging if not more 🤔? Or is that not the case? I am very new to geostatistics so thank you in advance 😊.

It outprform when:

1)The data are poorly correlated or sparse,

2) The data or sampling points are highly clustered,

3) The data includes high uncertainty or instrumental error,

4) Strong local variation in the data etc.