document: High-performance spatial indexing for location-based services Much attention has been accorded to Location-Based Services and location tracking , a necessary component in active , trigger-based LBS applications . Tracking the location of a large population of moving objects requires very high update and query performance of the underlying spatial index . In this paper we investigate the performance and scalability of three main-memory based spatial indexing methods under dynamic update and query loads : an R-tree , a ZB-tree , and an array\/hashtable method . By leveraging the LOCUS performance evaluation testbed and the City Simulator dynamic spatial data generator , we are able to demonstrate the scalability of these methods and determine the maximum population size supported by each method , a useful parameter for capacity planning by wireless carriers .
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