Abstract
In this chapter, we demonstrate the detailed record of our efforts to forecast changes in the number of visitors and sales of the city center at Fukuoka City, Japan, caused by a large-scale commercial redevelopment, JR Hakata City. The purpose of doing this is twofold; The first is to describe and record the present state of the art to forecast the effects of urban development policies based coherently on consumer behavior changes, particularly consumer Kaiyu behavior changes. The second is to provide some challenging problems we faced in the present methodology employed and suggest a direction to improve the methodology further.
The uniqueness of our forecasting efforts lies in the approach based on the consumers’ behavior models explaining their choices about the frequency of visits to destinations and, in particular, their choices about how they undertake Kaiyu behaviors or shop-around behaviors among shop** sites in the city center. Consequently, our models become not probability-based ones but frequency-based ones. We also deal with the Kaiyu flows among various districts within a city center in terms of the number of people, revealing the accompanied money flows within the city center.
Furthermore, Fukuoka City is a twin city with two core CBDs, Ten**, the area with the largest retail agglomeration, and Hakata, having the railroad station terminal redeveloped as JR Hakata City this time. Thus the focus of our forecasting efforts is how Ten**’s supremacy will change through the large-scale development carried out on one side of the twin cores. The novel feature of our efforts to explore this is that we dig deep into how the supremacy of Ten** as a destination will change by exploring a causal path, the Hakata’s intervening opportunity effects on the destination Ten**, from predicting the number of visitors to Ten** intercepted by Hakata, the midway to the destination Ten**, after the large-scale retail development at Hakata.
When carrying out our forecastings, we utilize several methods we developed ourselves, such as the weighted Poisson models for the on-site samples, the Kaiyu Markov model with covariates, and the consistent estimation method for the Kaiyu path density from the on-site samples. These methods correspond to respective aspects of a unique individual’s entire behavior. Thus the results obtained from these methods should have coherency. However, the most challenging problem we faced in our forecasting task was how to keep consistency between the results from different models and data. Therefore, while describing the detailed records of our forecasting efforts, we also indicate and discuss how to address the problem and improve the present methodology.
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Notes
- 1.
For more details on Kyushu Shinkansen’s wider effects on the Kyushu region, refer to the chapter “How Would the Kyushu Super-Express Railway Opening Change the Flow of Tourists from the Kansai Region within the Kyushu Wide Area, Japan?: A Micro Behavior Analysis of the Destination’s Hub Function” in this volume, which discusses the changes in the number of tourists from the Kansai region and the changes in the people’s flows among three prefectures within the Kyushu region, including the route map and the more detailed description of Kyushu Shinkansen.
- 2.
When we say “Hakata,” we usually divide the city center of Fukuoka City by the river flowing south to north in two districts, east and west or Ten** and Hakata, and refer to the opposite side of Ten** as the Hakata district. However, this sense of the Hakata district is considerably broader than the JR Hakata Station and its neighborhood because of including other shop** core sites, Canal City Hakata, an independent shop** mall, and Hakata Riverain, the commercial redevelopment complex implemented by Fukuoka City Government. Therefore, this chapter uses the term JR Hakata Station district if referring to JR Hakata Station and its neighborhood to distinguish it from the broader Hakata district.
- 3.
Japan consists of four islands, from north to south, Hokkaido, Honshu, the largest main island, Shikoku, and Kyushu islands. The Kyushu island is the southmost one composed of seven prefectures, roughly from north to south, Fukuoka, Saga, Oita, Kumamoto, Miyazaki, and Kagoshima Prefectures, with a population of 12.8 million people as of 2020, according to census 2020.
- 4.
Our consumer behavior approach started with forecasting the changes in consumers’ choices of the frequency of visits and their effects on retail sales. Refer to Saito and Yamashiro [3] and Saito et al. [4]. After these studies, the treatment of economic effects by consumer behavior approach has evolved from the effort for measuring consumers’ time value of shop** (refer to Saito and Yamashiro [5], Saito et al. [6]) to the direct estimation of consumers’ utility functions to evaluate the welfare changes. (Refer to Yamashiro and Saito [7], Saito et al. [8].)
- 5.
- 6.
For more details on the intervening opportunity, refer to Saito et al. [14], Chapter 9, “A Micro Behavior Approach to Estimating and Forecasting the Intervening Opportunity Effects with a Multivariate Poisson Model: A Case for the New Terminal Complex of Kyushu Super-Express Railway, JR Hakata City,” in this volume, which has discussed the estimation and prediction of Ten** and Hakata’s intervening opportunity effects on the destination, Hakata and Ten**, measured by the number of visitors to the destination. Also see Yamashiro et al. [15], and Saito et al. [16], which dealt with the closely related topics of Hub functions.
- 7.
- 8.
Hakata Riverain was developed by the redevelopment project led by the subsidiary of the Fukuoka City Government. Saito et al. [21] discussed the background and bankruptcy of Hakata Riverain’s redevelopment process from the perspective of the risk inherent in the collective social decision-making process.
- 9.
Canal City Hakata is the first large-scale commercial complex developed by the private sector in the city center of Fukuoka City in 1994. It comprises a large retail shop, a shop** mall, hotels, a theater, a cinema complex, restaurants, and an amusement park, attracting many visitors for shop** and leisure. Saito [22] and Saito et al. [23] discussed its physical design with many curves from the angle of information evolution by town walking.
- 10.
For more detailed description of Ten**, refer to Saito et al. [24] which discussed and verified that the connectivity of Ten** has increased by the extension of the Ten** underground shop** mall.
- 11.
Precisely speaking, the predicted number of visitors to Ten** and Hakata while considering the intervening opportunities are not the entrance visits to Ten** and Hakata since the question items to ask the respondents about their frequency of visits to Ten** and Hakata are not referring to the number of joint visits to Ten** and Hakata but just total frequency of visits to each of Ten** and Hakata.
- 12.
Similarly, since we are using the same question items to ask the respondents about their frequency of visits to Ten**, Hakata, and Canal City, in this logit model, after all, we have hypothesized that the respondents’ frequency of visits to each destination is proportional to their entrance choice probability for each destination.
- 13.
More precisely, we have expanded by 10,000. There are no sound theoretical foundation to determing the number.
- 14.
To avoid misunderstandings and multi-understandings about the entire city center of Fukuoka City, we show the respondents its district extent definition in the questionnaire sheet.
- 15.
We say the Kaiyu behavior proceeds if the Kaiyu step progresses or the Kaiyu movement occurs. We assume that a Kaiyu movement occurs if the place or the purpose of the Kaiyu behavior changes. Thus we can describe the phenomenon that a consumer continues to undertake his/her Kaiyu behavior at the same place with changing his/her purpose of continuing his/her Kaiyu.
- 16.
We discussed the estimation and forecasting of the intervening opportunity effects that both Ten** and Hakata Station districts affect each other in a separate chapter in this volume. Refer to Saito et al. [14], Chapter 9, “A Micro Behavior Approach to Estimating and Forecasting the Intervening Opportunity Effects with a Multivariate Poisson Model: A Case for the New Terminal Complex of Kyushu Super-Express Railway, JR Hakata City,” in this volume.
- 17.
Saito et al. [14] estimated another formulation of the intervening opportunity, which replaces squk with squk/tik for \( k=j,\overline{j} \) in Chapter 9, “A Micro Behavior Approach to Estimating and Forecasting the Intervening Opportunity Effects with a Multivariate Poisson Model: A Case for the New Terminal Complex of Kyushu Super-Express Railway, JR Hakata City,” in this volume.
- 18.
Another reason for adding the visit frequency to Canal City Hakata to the multivariate Poisson model is that the present formulation expresses the intervening opportunity for the destination j as a constant under the dependent variable Yij. Thus we must have more than three dependent variables to incorporate two, Ten** and Hakata’s, intervening opportunity explanatory variables.
- 19.
In later sections concerning the consistent estimation and the Kaiyu Markov model, we address the problem of how visitors’ entrance visits to the destinations and their Kaiyu behaviors between shop** sites determine the total visits to the destinations. Thus the total visits to Ten** and Hakata Station districts forecasted here are independent results not related to the forecast in the later Kaiyu Markov model sections because the models they base on are different. Thus, a future challenge remains to formulate how consumers jointly determine the visit frequency to the destinations and the number of Kaiyu steps between them.
- 20.
Saito et al. [20] first developed the algorithm for consistently estimating the Kaiyu path density over all Kaiyu paths. Then, Saito and Nakashima [19] first applied the consistent estimation method to the estimation for the pedestrian flows or Kaiyu flows within the Daimyo district neighboring Ten** in the city center of Fukuoka City.
- 21.
Saito and Ishibashi [31] first introduced this definition to deal with the staying within the same large retail facility; see also Saito and Ishibashi [32, 33], Chapter 5, “Kaiyu Markov Model with Covariates to Forecast the Change of Consumer Kaiyu Behaviors Caused by a Large-Scale City Center Retail Redevelopment,” in this volume.
- 22.
The department store, Daimaru Ten** Fukuoka, cooperates with our on-site survey, kindly providing us with the observed numbers of incoming visitors for 2 days of our survey period, 26th June, Saturday, and 27th June, Sunday, 2010. The data are the total incoming number of visitors per day for these 2 days, measured by the people counting system equipped, respectively 56,400 and 53,600 persons per day. Therefore, for expansion, we employed 55,000 persons per day, the average for the 2 days.
- 23.
From Table 10, we obtain the total visit density excluding staying for node six as follows. The column sum for the node six minus the staying density = 0.3224 − 0.1381 = 0.1843. Thus the estimated number of net incoming visitors to the entire city center of Fukuoka City becomes 55,000/0.1843 = 298,417.
- 24.
More precisely, we can obtain the observations of the respondents’ entrance choices by pooling the on-site survey data from different sampling points. However, it induces serious choice-based sampling biases. Another way to estimate the entrance choice model is by using the individual micro Kaiyu behavior data with giving each individual the weight obtained from the consistent estimation method. This method might be the most general way to address the choice-based biases related to the on-site random sampling surveys, which remain future challenges.
- 25.
Also, refer to Ishibashi and Saito [34] and Saito and Ishibashi [33], respectively, Chapters 7 and 5, “A Disaggregate Kaiyu Markov Model to Forecast the Sales of Retail Establishments Based on the Consumers’ Frequency of Visit” and “Kaiyu Markov Model with Covariates to Forecast the Change of Consumer Kaiyu Behaviors Caused by a Large-Scale City Center Retail Redevelopment,” in this volume. As to the fundamentals for the Kaiyu Markov model estimation, refer to Saito et al. [35], Chapter 6, “Estimation of Disaggregate Huff and Kaiyu Markov Model: A Lecture Note on Conditional Logit Model,” in this volume.
- 26.
Here, we used the explanatory variable, the number of shops, when a district lacks the data for the shop floor area. Thus, we set the number of shops for District 20, the JR Hakata Station, to one or log(1) = 0.
- 27.
All calculations to estimate and forecast the Kaiyu flows in this section are based on the postulates presented in Chapter 6, “Basics of Kaiyu Markov Models: Reproducibility Theorems—A Validation of Infinite Kaiyu Representation,” in this volume, Saito et al. [36]. For more details on Kaiyu Markov models, readers are requested to refer to Chapter 6, “Basics of Kaiyu Markov Models: Reproducibility Theorems—A Validation of Infinite Kaiyu Representation”. However, for completeness, we list below the formula for calculating the number of Kaiyu flows between the shop** sites or districts. We prepare the notations. Let I and H denote the shop** nodes and exit/entrance, respectively. Let P be the probability matrix of Kaiyu choice probabilities, including exit, comprising PII and PIH. Also, we denote by FHI the number of entrance visitors to shop** nodes, I. Let RM denote the number of Kaiyu flows. We use the convention for the superscript x to denote before and after the opening when x = Before or After, respectively. With these notations, the number of Kaiyu flows between the shop** nodes or districts, I, is calculated by the following formula:
$$ {RM}^x=\operatorname{diag}\left({F}_{HI}^x{\left(I-{P}_{II}^x\right)}^{-1}\right){P}_{II}^x,\kern1.25em x=\mathrm{Before},\mathrm{After} $$ - 28.
See the matrix formed by the first three rows and columns or three districts in Table 19(A). For example, the number in the cell for the first row and the second column means that the Kaiyu movement from Ten** to Hakata is 35,287 persons per day.
- 29.
Note that we excluded the Kaiyu movements within the same retail facility as stated before. Thus, this result does not reflect the Kaiyu movements in JR Hakata City.
- 30.
On 26 February 2011, 4 days before the pre-opening of JR Hakata City, FQBIC (Fukuoka University Institute of Quantitative Behavioral Informatics for City and Space Economy) publicized the newsrelease [37] about the impacts of JR Hakata City’s opening on the city center of Fukuoka City; Forecasts of the number of visitors, retail sales, and the changes in people’s Kaiyu flows between Ten** and Hakata. The newsrelease was reported in the articles in the newspapers (cf. [38,39,40]).
- 31.
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Saito, S., Yamashiro, K., Iwami, M. (2023). How Would the Opening of JR Hakata City, a New Terminal Complex of the Kyushu Super-Express Railway, Change the Number of Visitors, Retail Sales, and Consumers’ Kaiyu Flows in the City Center Commercial District of Fukuoka City?. 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_10
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