I have a data set that includes locations of where certain rocks were observed on the Earth. Populated areas have a higher number of observations in general. Remote areas have less observations. I'm confident that the low number of observations in rural areas does not mean there are fewer rocks of that type present.
Is there a way to correct/reduce this bias? I've looked into oversampling and bias correction and have not been able to find something I think is appropriate.
My current thought is to look at each observation point, count the number of points that are near it, and use a PDF (Normal or Gaussian) to generate new points around that point. If the number of points near the original point is low, I will generate a larger number of new points. If there are a lot of points around the point, I will generate few (if any) new points. (Basically using rough estimates of the PDF parameters and number of surrounding points to add points to the data set).
Is this an acceptable way to supplement the data set with the goal of overcoming some of the bias? Is there something similar or a different approach I should use?