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A method for analyzing the daily variation in the spatial pattern of market area

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  • Sadahiro, Yukio

Abstract

Market area analysis has long been an important research topic in retailing, marketing, and geography. Numerous studies have been conducted in the literature to describe and understand the market area and consumers' behavior. The daily pattern of the market area, however, has not yet been fully analyzed. The market area of department stores and shopping malls is larger on weekends than on weekdays. Many shops and restaurants are closed on Christmas days, which shrinks the market area of shopping malls. To describe and grasp these patterns, this paper proposes a new procedure for analyzing the daily pattern of the market area. Three measures evaluate the difference in the market area between different days and the variation within a day group. A loglikelihood based statistical measure visualizes the spatial difference in the market area between different days. A method for detecting anomalous days on which the market areas are quite different from those of other days. The proposed procedure is applied to the analysis of the visitors of five towns in Tokyo. The results indicate the effectiveness of the procedure as well as provide useful findings for understanding the daily pattern of visitors to the towns.

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  • Sadahiro, Yukio, 2021. "A method for analyzing the daily variation in the spatial pattern of market area," Journal of Retailing and Consumer Services, Elsevier, vol. 58(C).
    • Handle: RePEc:eee:joreco:v:58:y:2021:i:c:s0969698920313448
    DOI: 10.1016/j.jretconser.2020.102336
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    1. Chinh Ho & Corinne Mulley, 2013. "Tour-based mode choice of joint household travel patterns on weekend and weekday," Transportation, Springer, vol. 40(4), pages 789-811, July.
    2. Chieh-Hua Wen & Frank Koppelman, 2000. "A conceptual and methdological framework for the generation of activity-travel patterns," Transportation, Springer, vol. 27(1), pages 5-23, February.
    3. T. Limanond & D.A. Niemeier & P.L. Mokhtarian, 2005. "Specification of a tour-based neighborhood shopping model," Transportation, Springer, vol. 32(2), pages 105-134, March.
    4. Felgate, Melanie & Fearne, Andrew, 2012. "Using Supermarket Loyalty Card Data to Inform Better Promotional Strategies," 2012 International European Forum, February 13-17, 2012, Innsbruck-Igls, Austria 144966, International European Forum on System Dynamics and Innovation in Food Networks.
    5. Soora Rasouli & Harry Timmermans, 2014. "Activity-based models of travel demand: promises, progress and prospects," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 18(1), pages 31-60, March.
    6. Yun, Dae-Sic & O'Kelly, M. E., 1997. "Modeling the day-of-the-week shopping activity and travel patterns," Socio-Economic Planning Sciences, Elsevier, vol. 31(4), pages 307-319, December.
    7. Francesco Battaglia & Lia Orfei, 2005. "Outlier Detection And Estimation In NonLinear Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(1), pages 107-121, January.
    8. Guillermo Marshall & Tiago Pires, 2018. "Measuring the Impact of Travel Costs on Grocery Shopping," Economic Journal, Royal Economic Society, vol. 128(614), pages 2538-2557, September.
    9. McCurley Hortman, Sandra & Allaway, Arthur W. & Barry Mason, J. & Rasp, John, 1990. "Multisegment analysis of supermarket patronage," Journal of Business Research, Elsevier, vol. 21(3), pages 209-223, November.
    10. Swilley, Esther & Goldsmith, Ronald E., 2013. "Black Friday and Cyber Monday: Understanding consumer intentions on two major shopping days," Journal of Retailing and Consumer Services, Elsevier, vol. 20(1), pages 43-50.
    11. Peter Widhalm & Yingxiang Yang & Michael Ulm & Shounak Athavale & Marta González, 2015. "Discovering urban activity patterns in cell phone data," Transportation, Springer, vol. 42(4), pages 597-623, July.
    12. Bhat, Chandra R. & Steed, Jennifer L., 2002. "A continuous-time model of departure time choice for urban shopping trips," Transportation Research Part B: Methodological, Elsevier, vol. 36(3), pages 207-224, March.
    13. Bhat, Chandra R. & Koppelman, Frank S., 1993. "A conceptual framework of individual activity program generation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 27(6), pages 433-446, November.
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    1. Chung, Yi-Shih & Ku, Ya-Han, 2023. "Effect of time stress and store visibility on the dynamics of passenger activity choices at airport terminals based on indoor trajectory data," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).

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