Location Analytics for Optimal Business Retail Site Selection (original) (raw)

Abstract

The issue on location placement for next business establishment is always a challenging topic. It presents businesses with many opportunities to uncover the most sophisticated approach on selecting the next location of physical stores to establish its presence. The traditional approach of manual survey of land, competition landscape and also related to demographic factor analysis comes with high cost and longer time to complete. Our proposed work leveraging Google Maps to survey the surrounding and records the existing characteristics such as whether the shop is a corner shop lot, can be viewed from main road or having a sizable parking space. Based on the findings, the characteristics listing mainly relates to the business type. The approach of this paper can be used as one of the alternative input to decision making for physical placement of store. With the proposed work, optimal store location placement is determined based on a set of characteristics of an existing location. This research may help new business to gain optimal in flux of customers based on the location identified.

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Authors and Affiliations

  1. Faculty of Computing and Informatics, Multimedia University, 63000, Cyberjaya, Selangor, Malaysia
    Ahmad Murad Bin Mohamed Rohani & Fang-Fang Chua

Authors

  1. Ahmad Murad Bin Mohamed Rohani
  2. Fang-Fang Chua

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Correspondence toFang-Fang Chua .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy
    Osvaldo Gervasi
  2. University of Basilicata, Potenza, Italy
    Beniamino Murgante
  3. Covenant University, Ota, Nigeria
    Sanjay Misra
  4. Saint Petersburg State University, Saint Petersburg, Russia
    Elena Stankova
  5. Polytechnic University of Bari, Bari, Italy
    Carmelo M. Torre
  6. University of Minho, Braga, Portugal
    Ana Maria A.C. Rocha
  7. Monash University, Clayton, Victoria, Australia
    David Taniar
  8. Kyushu Sangyo University, Fukuoka shi, Fukuoka, Japan
    Bernady O. Apduhan
  9. Politecnico di Bari, Bari, Italy
    Eufemia Tarantino
  10. Myongji University, Yongin, Korea (Republic of)
    Yeonseung Ryu

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Rohani, A.M.B.M., Chua, FF. (2018). Location Analytics for Optimal Business Retail Site Selection. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10960. Springer, Cham. https://doi.org/10.1007/978-3-319-95162-1\_27

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