Reverse k Nearest Neighbor and Reverse Farthest Neighbor Search on Spatial Networks

One of the problems that arise in geographical information systems is finding objects that are influenced by other objects. While most research focuses on kNN (k Nearest Neighbor) and RNN (Reverse Nearest Neighbor) queries, an important type of proximity queries called Reverse Farthest Neighbor (RFN) has not received much attention. Since our previous work shows that kNN and RNN queries in spatial network databases can be efficiently solved using Network Voronoi Diagram (NVD), in this paper, we aim to introduce a new approach to process reverse proximity queries including RFN and RkNN/RkFN queries. Our approach is based on NVD and pre-computation of network distances, and is applicable for spatial road network maps. Being the most fundamental Voronoi-based approach for RFN and RkNN/RkFN queries, our solutions show that they can be efficiently used for networks that have a low and medium level of density.