I'm building an application where a specific location is chosen, multiple services are polled to return results for that specific location and shown on a map.
I have the results from the different sources shown in different colours on the map, so you can get a feel for which results are the "best". In this instance "best" is hard to precisely define, but means which are generally the closest to the starting point.
Because everything is lat/lon, I can then, calculate the distance each one is from the starting point. I was thinking calculating the average of the sum of the squares of the distances, as this would favour those result sets who have the closest points. This might skew otherwise good results that have a just a couple of distant outliers, but it's worth a go.
Are there any good algorithms out there that would help in this situation? I've looked at "clustering" algorithms, but they are good for organising data into groups. However, in this case, I already know what the groups are, I just want to know which group is closest to my target point.