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GIS enabled service site selection: Environmental analysis and beyond

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Abstract

Given its importance, the problem of selecting the right site for a service entity has attracted great attention in the literature. However, due to its complexity, the quantification of the interrelationships between the service site and its nearby business types is still a challenging task. To this end, in this paper, we propose a novel joint learning scheme for service site selection. This scheme employs both the Probabilistic Latent Semantic Analysis (PLSA) on the Geographical Information System (GIS) data and the partitional clustering on the service performance data. A case study for bank branch selection is provided to demonstrate the usefulness of our method. Finally, based on the joint learning scheme, we present a conceptual framework for the complete procedure of service site selection with a particular emphasis on the GIS enabled network analysis.

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Notes

  1. Due to the confidential requirements, we cannot tell more details about the bank and the city.

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Acknowledgements

This research was partially supported by the National Natural Science Foundation of China (NSFC) (nos. 70901002, 71031001, 70890082). Also, we are grateful to the Information Systems Frontiers anonymous referees for their constructive comments on the paper.

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Authors and Affiliations

  1. School of Economics and Management, Beihang University, Beijing, China

    Junjie Wu & Yili Ren

  2. Research Center for Contemporary Management, Key Research Institute of Humanities and Social Sciences at Universities, School of Economics and Management, Tsinghua University, Beijing, China

    Jian Chen

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  1. Junjie Wu
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  2. Jian Chen
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Correspondence to Jian Chen.

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Wu, J., Chen, J. & Ren, Y. GIS enabled service site selection: Environmental analysis and beyond. Inf Syst Front 13, 337–348 (2011). https://doi.org/10.1007/s10796-010-9284-7

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