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.

This book is the first coherent presentation of the latest research and practices concerned with how recent advances in mobile information and communication technology (ICT) and the Internet of Things (IoT) are utilized to enhance the value of the city and change the way that city planning and management are carried out. Its salient feature is the pursuit of the individual-oriented evaluative point of view regarding the city. This view considers the value of the city to be the total of visit-values individuals feel and appreciate when they visit the city. The visit-value is conceptualized as the intangible asset value of the attractiveness of the city that visitors form in their minds based on their experiences and activities in the city, transactions with city space, and communications with other people. Visitors to the city may well be quite heterogeneous individuals with different motives and preferences. Thus, to enhance the value of the city, quite different visit values of heterogeneous individuals should be enhanced simultaneously, which necessitates the use of ICT and IoT in living spaces. Based on this view, the city utilizing ICT and IoT to enhance the value of the city is called the social city. Whereas many other books deal with the impacts of the advances in mobile ICT on the city, they only discuss how these advances change the infrastructure of the city but do not discuss how these technological advances can be utilized to enhance the city’s value. This book first develops the concept of the social city based on an individual micro-behavioral approach. Then, it presents the latest studies on technological components of the social city, such as the human-sensing technology for estimating individual behavior, decision making, and mood; the visualizing technology of the thermal 3-dimensional environment of the city; and the social-sensing technology using social networking service (SNS) for measuring and creating an atmosphere of city space. Finally, it envisages the future of the social city.