Tomoya Mori

On the evolution of hierarchical urban systems1

Abstract The rapid urbanization trend of the world economy implies an increasing importance of cities as basic units of national and international trade. Given that the cities within an economy constitute some form of hierarchical structure, we model the endogenous formation of a hierarchical urban system. To overcome the multiplicity of equilibria, we propose an evolutionary approach which combines a general equilibrium model with an adjustment dynamics. It is demonstrated that as the economys population size increases gradually, the urban system self-organizes into a highly regular hierarchical system a la Christaller.

Spatial distribution of economic activities in Japan and China

In this chapter we look at the spatial distribution of economic activities in China and Japan. Japan has excellent data and relatively uniform institutions since World War II, which allow us to track its spatial evolution and detail its key features today. For Japan we show how structural shifts in the national economy involved major transformations of the regional structure of economic activity. We address a central policy issue, the high extent of urban agglomeration in Tokyo. Then we turn to the details of the spatial distribution of industrial activity across cities, to see what general patterns hold and what they imply for our understanding of the role of different cities in the urban hierarchy. For China the approach is different. With the radical institutional changes since 1978 moving China away from being a planned economy, there is little consistency in data definitions and coverage over time and less detailed data are available. We focus on the last decade and on policy issues. We observe that Chinese cities tend to be overcapitalized and undersized, with strong spatial biases to policies, concerning migration, capital allocations, infrastructure allocations and location of FDI.

A Divergence Statistic for Industrial Localization

We propose a statistical index of industrial localization based on the Kullback-Leibler divergence. This index is particularly well suited to cases where industrial data are available only at the regional level. Unlike existing regional-level indices, our index can be employed to test the significance of industrial localization relative to a hypothesized reference distribution of probable locations across regions. In addition, one can test relative degrees of localization among industries. Finally, as with all Kullback-Leibler divergence indices, our index can be decomposed into components representing localization at various levels of spatial aggregation.

ECONOMIES OF TRANSPORT DENSITY AND INDUSTRIAL AGGLOMERATION

Abstract This paper develops a model of a spatial economy in which interregional trade patterns and the structure of the transport network are determined endogenously as a result of the interaction between industrial location behavior and increasing returns in transportation, in particular, economies of transport density . The traditional models assume either the structure of the transport network or industrial location patterns, and hence, they are unable to explain the interdependence of the two. It is shown that economies of transport density can be the primary source of industrial localization.

The Number-Average Size Rule: A New Empirical Relationship Between Industrial Location And City Size

The spatial intensities of both industries and population are highly uneven across space. Moreover, these intensities differ not only across industries, but also change through time. Nevertheless, we show using Japanese data for metropolitan areas in two time periods that the location intensities of both industries and population are linked by surprisingly simple and persistent patterns. In particular, we identify a strong negative log-linear relation between the number and the average (population) size of metro areas in which a given industry is found. This relation, which we designate as the Number Average Size (NAS) Rule, is also shown to be intimately connected to both the Rank-Size Rule and Christallers (1966) Hierarchy Principle applied to metropolitan areas. In particular, we show mathematically that in the presence of the Hierarchy Principle (which holds quite well in Japan) this NAS Rule is essentially equivalent to the Rank Size Rule.

Skills, Agglomeration and Segmentation

We investigate the role of skill heterogeneity in explaining location patterns induced by pecuniary externalities (Krugman (1991)). In our setting, sellers with higher skills perform better in the marketplace, and their sales are larger. Selling to distant locations leads to lower sales because of both (pecuniary) transport costs and communication costs that reduce the perceived quality of goods. A symmetry-breaking result is obtained: symmetric configurations cannot be stable, and regional inequality is inevitable. The relatively more skilled choose to stay in the location with higher aggregate income and skill, while the relatively less skilled stay in the other. The model allows us to analyse the links between the extent of interregional inequality and the extent of interpersonal skill inequality.

An Industrial Agglomeration Approach to Central Place and City Size Regularities

An empirical regularity designated as the Number-Average Size (NAS) Rule was first identified for the case of Japan by Mori, Nishikimi and Smith [13], and has since been extended to the US by Hsu [6]. This rule asserts a negative log-linear relation between the number and average population size of cities where a given industry is present, i.e., of industry-choice cities. Hence one of its key features is to focus on the presence or absence of industries in each city, rather than the percentage distribution of industries across cities. But despite the strong empirical regularity of this rule, there still remains the statistical question of whether such location patterns could simply have occurred by chance. In this paper an alternative approach to industry-choice cities is proposed. This approach utilizes the statistical procedure developed in Mori and Smith [15] to identify spatially explicit patterns of agglomeration for each industry. In this context, the desired industry-choice cities are taken to be those (economic) cities that constitute at least part of a significant spatial agglomeration for the industry. These cluster-based choice cities are then used to reformulate both the NAS Rule and the closely related Hierarchy Principle of Christaller [2]. The key empirical result of the paper is to show that the NAS Rule not only continues to hold under this new definition, but in some respects is even stronger. The Hierarchy Principle is also shown to hold under this new definition. Finally, the present notion of cluster-based choice cities is also used to develop tests of both the locational diversity of industries and the industrial diversity of cities in Japan.

A Clique-Based Method Using Dynamic Programming for Computing Edit Distance Between Unordered Trees

Many kinds of tree-structured data, such as RNA secondary structures, have become available due to the progress of techniques in the field of molecular biology. To analyze the tree-structured data, various measures for computing the similarity between them have been developed and applied. Among them, tree edit distance is one of the most widely used measures. However, the tree edit distance problem for unordered trees is NP-hard. Therefore, it is required to develop efficient algorithms for the problem. Recently, a practical method called clique-based algorithm has been proposed, but it is not fast for large trees. This article presents an improved clique-based method for the tree edit distance problem for unordered trees. The improved method is obtained by introducing a dynamic programming scheme and heuristic techniques to the previous clique-based method. To evaluate the efficiency of the improved method, we applied the method to comparison of real tree structured data such as glycan structures. For large tree-structures, the improved method is much faster than the previous method. In particular, for hard instances, the improved method achieved more than 100 times speed-up.

Stochastic simulation of Boolean rxncon models: towards quantitative analysis of large signaling networks

BackgroundCellular decision-making is governed by molecular networks that are highly complex. An integrative understanding of these networks on a genome wide level is essential to understand cellular health and disease. In most cases however, such an understanding is beyond human comprehension and requires computational modeling. Mathematical modeling of biological networks at the level of biochemical details has hitherto relied on state transition models. These are typically based on enumeration of all relevant model states, and hence become very complex unless severely – and often arbitrarily – reduced. Furthermore, the parameters required for genome wide networks will remain underdetermined for the conceivable future. Alternatively, networks can be simulated by Boolean models, although these typically sacrifice molecular detail as well as distinction between different levels or modes of activity. However, the modeling community still lacks methods that can simulate genome scale networks on the level of biochemical reaction detail in a quantitative or semi quantitative manner.ResultsHere, we present a probabilistic bipartite Boolean modeling method that addresses these issues. The method is based on the reaction-contingency formalism, and enables fast simulation of large networks. We demonstrate its scalability by applying it to the yeast mitogen-activated protein kinase (MAPK) network consisting of 140 proteins and 608 nodes.ConclusionThe probabilistic Boolean model can be generated and parameterized automatically from a rxncon network description, using only two global parameters, and its qualitative behavior is robust against order of magnitude variation in these parameters. Our method can hence be used to simulate the outcome of large signal transduction network reconstruction, with little or no overhead in model creation or parameterization.

An Improved Clique-Based Method for Computing Edit Distance between Unordered Trees and Its Application to Comparison of Glycan Structures

The tree edit distance is one of the most widely used measures for comparison of tree structured data and has been used for analysis of RNA secondary structures, glycan structures, and vascular trees. However, it is known that the tree edit distance problem is NP-hard for unordered trees while it is polynomial time solvable for ordered trees. We have recently proposed a clique-based method for computing the tree edit distance between unordered trees in which each instance of the tree edit distance problem is transformed into an instance of the maximum vertex weighted clique problem and then an existing clique algorithm is applied. In this paper, we propose an improved clique-based method. Different from our previous method, the improved method is basically a dynamic programming algorithm that repeatedly solves instances of the maximum vertex weighted clique problem as sub-problems. Other heuristic techniques, which do not violate the optimality of the solution, are also introduced. When applied to comparison of large glycan structures, our improved method showed significant speed-up in most cases.