Kernel Density Estimation

Benefits of using kernel smoothing:

1. It is a non-parametric method, so it does not assume any distribution for the data.
2. Spatial autocorrelation is taken into account - meaning that the value at any location is influences not only by the nearby data points but also by more distant ones.
3. Smoothing parameter control: Kernel smoothing allows for adjusting the bandwidth or smoothing parameter, which controls the level of smoothing. This enables researchers to fine-tune the analysis and choose an appropriate level of smoothing based on the characteristics of the data and the research question.