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Imputing relevant information from multi-day GPS tracers for retail planning and management using data fusion and context-sensitive learning

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  • Moiseeva, Anastasia
  • Timmermans, Harry

Abstract

It is well known that the right location of shopping centres is of paramount importance. Unless stores succeed in attracting their own clientele, they rely to a large extent on the impulse behaviour of shoppers. To evaluate alternative locations, models of pedestrian behaviour may be useful. Modern technologies such as GPS and RFID offer new possibilities providing data on routes and stops, which are required as input for such models. An automatic interpretation of GPS tracers with respect to the activities being conducted could enhance the applicability of such technologies to retail management applications. This paper reviews this rapidly growing literature, and shows how automatic data imputation can be established by using Bayesian belief networks and how GPS traces can be fused with land use data of retail location.

Suggested Citation

  • Moiseeva, Anastasia & Timmermans, Harry, 2010. "Imputing relevant information from multi-day GPS tracers for retail planning and management using data fusion and context-sensitive learning," Journal of Retailing and Consumer Services, Elsevier, vol. 17(3), pages 189-199.
  • Handle: RePEc:eee:joreco:v:17:y:2010:i:3:p:189-199
    DOI: 10.1016/j.jretconser.2010.03.011
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    Cited by:

    1. Shatu, Farjana & Yigitcanlar, Tan, 2018. "Development and validity of a virtual street walkability audit tool for pedestrian route choice analysis—SWATCH," Journal of Transport Geography, Elsevier, vol. 70(C), pages 148-160.
    2. Saxena, Neha & Sarkar, Biswajit, 2023. "How does the retailing industry decide the best replenishment strategy by utilizing technological support through blockchain?," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    3. Mingardo, G. & van Meerkerk, J., 2012. "Is parking supply related to turnover of shopping areas? The case of the Netherlands," Journal of Retailing and Consumer Services, Elsevier, vol. 19(2), pages 195-201.
    4. Carling, Kenneth & Håkansson, Johan & Jia, Tao, 2013. "Out-of-Town Shopping and Its Induced CO2-Emissions," HUI Working Papers 87, HUI Research.
    5. Shatu, Farjana & Yigitcanlar, Tan & Bunker, Jonathan, 2019. "Objective vs. subjective measures of street environments in pedestrian route choice behaviour: Discrepancy and correlates of non-concordance," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 1-23.
    6. Ferracuti, N. & Norscini, C. & Frontoni, E. & Gabellini, P. & Paolanti, M. & Placidi, V., 2019. "A business application of RTLS technology in Intelligent Retail Environment: Defining the shopper's preferred path and its segmentation," Journal of Retailing and Consumer Services, Elsevier, vol. 47(C), pages 184-194.
    7. Hoskins, Jake D., 2020. "The evolving role of hit and niche products in brick-and-mortar retail category assortment planning: A large-scale empirical investigation of U.S. consumer packaged goods," Journal of Retailing and Consumer Services, Elsevier, vol. 57(C).
    8. Olga E. Ustinova, 2019. "Social Media In The Digital Ecosystem: Functions, Role And Value In Marketing," Annals of marketing-mba, Department of Marketing, Marketing MBA (RSconsult), vol. 4, December.
    9. Valentin-Florentin Dumitru & Gabriel Jinga & Florin Mihai & Aurelia Stefanescu, 2015. "Innovative information technologies and their impact on the performance of the entities which activate in the retail industry," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 17(39), pages 520-520, May.
    10. Carling, Kenneth & HÃ¥kansson, Johan & Jia, Tao, 2013. "Out-of-town shopping and its induced CO2-emissions," Journal of Retailing and Consumer Services, Elsevier, vol. 20(4), pages 382-388.
    11. Torres de Oliveira, Rui & Indulska, Marta & Steen, John & Verreynne, Martie-Louise, 2020. "Towards a framework for innovation in retailing through social media," Journal of Retailing and Consumer Services, Elsevier, vol. 54(C).

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