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Kaiyu Markov Model with Covariates to Forecast the Change of Consumer Kaiyu Behaviors Caused by a Large-Scale City Center Retail Redevelopment

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Recent Advances in Modeling and Forecasting Kaiyu

Part of the book series: New Frontiers in Regional Science: Asian Perspectives ((NFRSASIPER,volume 36))

Abstract

It is common for a person with several purposes to start a trip from home and return home after visiting several places. This phenomenon is called a trip chain, which is likely to occur, for instance, in leisure, sightseeing travel trips, sales, or commodity transport trips. Among others, a shopping trip chaining behavior of a consumer occurs ubiquitously in an agglomerated commercial district. We call it consumer’s shop-around or Kaiyu behavior. The apparent cause is in the district’s accumulated and proximate locations of retail facilities. Thus, the consumer’s shop-around behavior can be considered the agglomeration effect of the locational configuration of retail facilities. Hence, their actual locational arrangement can be evaluated by such a criterion as what amount of the agglomeration effect, equivalently, the consumer’s shop-around or Kaiyu behavior the arrangement induces. Based on this standpoint, this study proposes an evaluative framework for assessing retail redevelopment programs in the city center retail environment. This study develops a stationary Markov chain model with covariates to forecast consumers’ shop-around or Kaiyu behaviors. The model was applied to the city center of Fukuoka City, Japan, and used to evaluate redevelopment programs there from its forecasts. Meanwhile, this study proves the observed aggregate stationarity theorem or reproducibility theorem to show that the aggregate stationary Markov chain modeling has a rigorous validity even if any arbitrary non-stationary process rules each disaggregate process.

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Notes

  1. 1.

    For more details on the development of this theorem, the readers are asked to refer to Saito, Ishibashi, Yamashiro, Iwami [1], Chap. 4 in this volume.

  2. 2.

    Refer to, for example, Kondo [9], Kondo and Kitamura [10], and Isobe [11] for trip chaining studies in a general context.

  3. 3.

    Details of the survey are reported in Saito [14].

  4. 4.

    Refer to, for example, Sasaki [18], Lerman [19], and O’Kelly [20]. See also Hason [6] p. 9).

  5. 5.

    See also Saito and Sakamoto [23], “Kaiyu Markov model and evaluation of retail spatial structures,” Chap. 3 in this volume.

  6. 6.

    See also Saito, Ishibashi, Yamashiro, Iwami [1], “Basics of Kaiyu Markov models: Reproducibility theorems—a validation of infinite Kaiyu representation,” Chap. 4 in this volume.

  7. 7.

    Also see Saito [27], “A disaggregate hierarchical decision Huff model incorporating consumer Kaiyu choices among shopping sites,” Chap. 1 in this volume.

  8. 8.

    In retrospect, this problem is the same as the theme Fotheringham [29] was concerned with.

  9. 9.

    Refer to Wrigley and Dunn [31, 32].

  10. 10.

    This saying does not mean that the problem of difference in the distance exponent in the Huff or gravity-type model was resolved. While Fotheringham [29] diagrammatically showed the reason, it seems to remain still open to give rigorous proof.

  11. 11.

    Saito [36] discussed again the topic.

  12. 12.

    For a more detailed exposition of the Kaiyu Markov model, refer to Saito, Ishibashi, Yamashiro, and Iwami [1], “Basics of Kaiyu Markov models: Reproducibility theorems—a validation of infinite Kaiyu representation,” Chap. 4 in this volume, which includes various developments of the model and new results for the reproducibility theorems.

  13. 13.

    See Saito [12, 13], Saito et al. [15], and Saito and Motomura [16].

  14. 14.

    Details are reported in Ando [37].

  15. 15.

    For ease of explanation, here we said that the numbers of entry visits for case 1 and case 2 were fixed the same as the present case 0 in order to assess the agglomeration effects induced by the locational changes induced by the redevelopment programs. There may be seen, however, a slight difference in the numbers of entry visits between case 0, case 1 and case 2, though those of case 1 and case 2 are identical. This is because we assumed the new bus stop and the new parking developed in case 1 and case 2 automatically attracted additional passengers who get off there and enter the “Tenjin” district. This increases the entry visits by 0.9% from case 0 to case 1 and case 2. The increase might be said negligible.

  16. 16.

    The original version of this chapter was presented as Saito and Ishibashi [38], whose abridged version appeared in Saito and Ishibashi [39].

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Authors and Affiliations

  1. Fukuoka University Institute of Quantitative Behavioral Informatics for City and Space Economy (FQBIC), Fukuoka, Japan

    Saburo Saito

  2. Faculty of Policy Studies, Aichi Gakuin University, Nisshin City, Aichi, Japan

    Kenichi Ishibashi

Authors
  1. Saburo Saito
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  2. Kenichi Ishibashi
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Correspondence to Saburo Saito .

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Editors and Affiliations

  1. Informatics for City and Space Economy (FQBIC), Fukuoka University Institute of Quantitative Behavioral, Fukuoka-shi, Fukuoka, Japan

    Saburo Saito

  2. Faculty of Policy Studies, Aichi Gauin University, Nisshin-shi, Aichi, Japan

    Kenichi Ishibashi

  3. Department of Business and Economy, Nippon Bunri University, Oita-shi, Oita, Japan

    Kosuke Yamashiro

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Saito, S., Ishibashi, K. (2023). Kaiyu Markov Model with Covariates to Forecast the Change of Consumer Kaiyu Behaviors Caused by a Large-Scale City Center Retail Redevelopment. In: Saito, S., Ishibashi, K., Yamashiro, K. (eds) Recent Advances in Modeling and Forecasting Kaiyu. New Frontiers in Regional Science: Asian Perspectives, vol 36. Springer, Singapore. https://doi.org/10.1007/978-981-99-1241-4_5

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  • DOI: https://doi.org/10.1007/978-981-99-1241-4_5

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