An Optimized Spatial Query Method for Nearest Neighbor-Candidate Identification in Time Variant Probabilistic Spatial Data

Spatial data is represented with X and Y coordinates (or latitude and longitude) which might be a fixed point in a map or the position of an object in a map. In this paper we are using vector model for representing spatial data. Probabilistic spatial data is one where the coordinates or the position of an object may be one of 'n' different locations. If there are 'm' such objects then finding the probabilistic nearest neighborhood of such objects is known as spatial query. Finding the nearest neighbor (NN) of probabilistic spatial data is quite challenging because from time to time the objects will not be in the same position. In this paper we propose a fast and efficient method for finding the NN with linear optimization of position vectors. The method first finds the probable objects whose any of the position is nearer to the query object and then optimized amongst obtained result set to find the precise NN-Candidate. Result shows significant closeness of the query object with the obtained NN-candidate.