IDW vs. Kriging: Which Interpolation is better? A Python Case Study on Accuracy

In this tutorial, we are going to examine spatial interpolation using Python in Google Colab. We will be comparing two of the most used methods, IDW (Inverse Distance Weighting) and Kriging, against each other for rainfall estimation. We used data collected from 130 meteorological stations from all around Colombia by the IDEAM for periods between 1991 and 2020, through which we analyze the monthly average rainfall and the performances of each approach. By practical coding, we will visualize the differences, calculate error metrics, and finally derive the best approach to take for rainfall interpolation. For those who are just stepping into the world of geospatial analysis or have been doing this for quite a while and still need to sharpen their skills, this guide will help as a step-by-step guide along that journey

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