I have a list of sensor measurements for air quality with geo-coordinates, and I would like to implement outlier detection. The list of sensors is relatively small (~50).

The air quality can gradually change with the distance, but abrupt local spikes are likely outliers. If one sensor in the group of closely located sensors shows a higher value it could be an outlier. If the same higher value is shown by more distant sensors it might be OK.

Of course, I can ignore coordinates and do simple outlier detection assuming the normal distribution, but I was hoping to do something more sophisticated. What would be a good statistical way to model this and implement outlier detection?


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