I assume this is in 2 dimensions? Very simply, you could do this -- I used a similar technique to quickly optimize spatial clustering in a large scale data mining project.
Divide your coordinate space into a fixed number of grid lines in the X and Y directions (which it seems you have already done, by equally spacing these 4 points).
When you insert a point, divide its distance (integer division) from the origin in the X and Y direction by your grid step interval. Then you're left with two coordinates that identify the nearest X/Y grid intersection. Use the remainder to establish which side of the grid intersection your point belongs in.
If you want to get really complicated you could start using kD-Trees, etc... but I think this example is simple enough not to warrant any more complicated spatial partitioning.