This book is the first comprehensive presentation of a Kaiyu Markov model with covariates and a multivariate Poisson model with competitive destinations. These two models are core techniques when the authors and colleagues conduct their Kaiyu studies. The two models are usually used to forecast the effects of specific urban redevelopment on both the number of visitors and consumer shop-around or Kaiyu movements. Their Kaiyu studies originated from the constructions of a Kaiyu Markov model and the disaggregated hierarchical decision Huff model almost simultaneously around the early 1980s. This book retrospectively reviews how these models have evolved from the start to the present state, and previews the ongoing efforts to make further extensions of these models. The extension of the Huff model started from the disaggregated hierarchical decision Huff model with shop-arounds. In retrospect, the model formulated the consumer’s simultaneous choice of destinations as a joint probability. The mechanism to determine this joint probability was a recursive conditional probability system. Now the Huff model has shifted from joint probability to multivariate frequency Poisson with competitive destinations. On the other hand, the Kaiyu Markov model started from a descriptive model. Because it cannot forecast changes in shop-arounds or consumer Kaiyu behaviors, the Kaiyu Markov model with covariates was developed in which entrance and shop-around choice probabilities are explained by the respective two logit models with covariates such as distances and shop-floor areas. The noticeable point is that it can explain consumers’ probability of quitting their shop-arounds. Thus, the model enables one to evaluate the effects of urban revitalization policy that promotes consumers’ shop-arounds or Kaiyu behaviors. Furthermore, if the Kaiyu Markov model can estimate the actual numbers of flows of consumers’ shop-arounds among shopping sites, the corresponding money flows also can be estimated as economic effects. This book discusses from scratch the evolution of all these topics. Thus this book provides the basics of the Kaiyu Markov model, a tutorial for the theory and estimation of the conditional logit model, and a chapter serving as a practical research manual for forecasting changes caused by urban development based on consumers’ Kaiyu behaviors.
Chapter
1. A disaggregate hierarchical decision Huff model incorporating
consumer Kaiyu choices among shopping sites.
Chapter
2. A dynamical Huff
model: Computing the competitive equilibrium distribution of shop floor areas
over a city center commercial district using the Fixed-Point Algorithm.-
Chapter
3. Kaiyu Markov model and evaluation of retail spatial structures.-
Chapter
4. Basics of Kaiyu Markov models: Reproducibility theoremsa
validation of infinite Kaiyu representation.
Chapter
5. Kaiyu Markov Model
with Covariates to Forecast the Change of Consumer Kaiyu Behaviors caused by
a Large-Scale City Center Retail Redevelopment.
Chapter
6. Estimation of
Disaggregate Huff and Kaiyu Markov Model: A Lecture Note on Conditional Logit
Model.
Chapter
7. A Disaggregate Kaiyu Markov Model to Forecast the Sales of
Retail Establishments based on the Consumers Frequency of Visits.
Chapter
8. 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 Destinations Hub Function.
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.
Chapter
10. 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?.
Chapter
11. How Many Customers Would be Brought Back from Suburban
Shopping Malls to the City Center by Redeveloping the City Center Station
Building, JR Oita City, Japan?: A Multivariate Poisson Model with Competitive
Destinations.
Chapter
12. An Opportunity Cost Approach to Valuation of the
River in a City Center Retail Environment: Another Application of Kaiyu
Markov Model.
Chapter
13. Extraction of Long Sightseeing Kaiyu Routes in the
Kyushu Wide Region, Japan.
Chapter
14. A Bayesian Network Model of
Consumers Kaiyu Behaviors.
Saburo Saito, Fukuoka University Kenichi Ishibashi, Nagoya Sangyo University Kosuke Yamashiro, Fukuoka University
