Ranking locations for facility selection based on potential influences

We study the following location ranking problem. Given a set of existing facilities and a set of customers, we rank a set of candidate locations based on their potential influences, where the potential influence of a candidate location is defined as the number of customers the candidate location can attract. This problem is important in deciding where to establish a new facility. We formulate the problem as the potential influence location ranking query and analyze its basic properties. The analysis shows that the query is computationally expensive and efficient query processing techniques are needed. We propose a nearest location circle algorithm and a Voronoi diagram based algorithm to process the query. Experiments on both real and synthetic datasets show that the proposed algorithms are effective and efficient.