Abstract
Given a set S of sites and a set O of weighted objects, an optimal location query finds the location(s) where introducing a new site maximizes the total weight of the objects that are closer to the new site than to any other site. With such a query, for instance, a franchise corporation (e.g., McDonald’s) can find a location to open a new store such that the number of potential store customers (i.e., people living close to the store) is maximized. Optimal location queries are computationally complex to compute and require efficient solutions that scale with large datasets. Previously, two specific approaches have been proposed for efficient computation of optimal location queries. However, they both assume p-norm distance (namely, L1 and L2/Euclidean); hence, they are not applicable where sites and objects are located on spatial networks. In this article, we focus on optimal network location (ONL) queries, i.e., optimal location queries in which objects and sites reside on a spatial network. We introduce two complementary approaches, namely EONL (short for Expansion-based ONL) and BONL (short for Bound-based ONL), which enable efficient computation of ONL queries with datasets of uniform and skewed distributions, respectively. Moreover, with an extensive experimental study we verify and compare the efficiency of our proposed approaches with real world datasets, and we demonstrate the importance of considering network distance (rather than p-norm distance) with ONL queries.
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Notes
There is a user defined parameter called θ𝜖∈ (0,1] used in the FGP-OTF algorithm. For this experiment, we ran the FGP-OTF algorithm with different θ values in the range of [0.0001, 1]. The θ value equal to 0.001 resulted in less computation cost and the corresponding execution time is reported in Fig. 13.
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Acknowledgments
The authors would like to thank Professor FeiFei Li for making the source code and the corresponding datasets used in [16] accessible.
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Ghaemi, P., Shahabi, K., Wilson, J.P. et al. A comparative study of two approaches for supporting optimal network location queries. Geoinformatica 18, 229–251 (2014). https://doi.org/10.1007/s10707-013-0179-x
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DOI: https://doi.org/10.1007/s10707-013-0179-x