A Reconsideration of the NAS Rule from an Industrial Agglomeration Perspective

An empirical regularity designated as the Number-Average Size (NAS) Rule was first identified for the case of Japan by Mori, Nishikimi and Smith [71], and has since been extended to the US by Hsu [50]. 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. Indeed, chance occurrences of certain industry-choice cities may be quite likely if, for example, one includes cities where only a single industrial establishment happens to appear. An alternative approach to industry-choice cities is proposed in a companion paper, Mori and Smith [73], which is based on industrial clustering. More specifically, this approach utilizes the statistical procedure developed in Mori and Smith [72] 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. With respect to these cluster-based industry-choice cities, the central objective of the present paper is to reconfirm the persistence of the NAS Rule between the years 1981 and 2001, as first observed in Mori et al. [71]. Indeed the NAS Rule is in some ways stronger under this new definition of industry-choice cities in that none of outlier industries in the original analysis show any significant agglomeration, and hence can be excluded from the present analysis. A second objective is to show that there has been a substantial churning of the industry mix in individual cities between these two time periods, and hence that persistence of the NAS Rule is even more remarkable in this light. Finally, these persistence results are extended to both the Rank Size Rule and the Hierarchy Principle of Christaller [13], which were shown in Mori et al. [71] to be intimately connected to the NAS Rule.

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.

Alternative financial service providers and the spatial void hypothesis: the case of New Jersey and Delaware

This paper continues the use of the spatial void hypothesis methodology to analyze the location of alternative financial service providers, such as check cashing outlets and pawn shops, in New Castle County, Delaware, and Atlantic, Mercer, Monmouth, and Passaic counties in New Jersey. Also explores whether these providers are disproportionately serving minority and low-income areas.

Alternative financial service providers and the spatial void hypothesis

This paper examines the use of alternative financial service providers (AFSPs) such as check-cashing outlets and pawnshops in Philadelphia, Montgomery, Delaware, and Allegheny counties. Also explores whether these providers are disproportionately serving minority and low-income areas.

NATURE OF SERINA'S PROJECT RELATED WITH 2016 PORTO DECLARATION AND LINKED TO UNITED NATIONS SDGS

In 2015, the United Nations adopted the 2030 Agenda for Sustainable Development - "a plan for people, planet and prosperity", which includes 17 Sustainable Development Goals (SDGs) that serve as a roadmap for the national and international policies that should be implemented to achieve a better and more sustainable future for all. Society, Economy, and Environment are recognized as the three pillars for sustainable development. After several attempts to identify and agree upon a global policy for a sustainable future, it is time for implementation. It is time now for global Engineers to get involved in order to bring results in the direction of the implementation of the SDGs. Two European Civil Engineering associations, the European Civil Engineering Education and Training Association (EUCEET) and the Association of European Civil Engineering Faculties (AECEF) join forces to investigate the very important issue of "The role of education for Civil Engineers in the implementation of the SDGs". The topics of the conference are related, but not limited, to the following SDGs: SDG4 - Quality education SDG6 - Clean water and sanitation SDG7 - Affordable and clean energy SDG8 - Decent work and economic growth SDG9 - Industry, innovation, and infrastructure SDG11 - Sustainable cities and communities SDG13 - Climate action SDG14 - Life below water SDG15 - Life on lan

Analysis of Industrial Agglomeration Patterns: An Application to Manufacturing Industries in Japan

The standard approach to studying industrial agglomeration is to construct summary measures of the “degree of agglomeration” within each industry and to test for significant agglomeration with respect to some appropriate reference measure. But such summary measures often fail to distinguish between industries that exhibit substantially different spatial patterns of agglomeration. In a previous paper, a cluster-detection procedure was developed that yields a more detailed spatial representation of agglomeration patterns (Mori and Smith [28]). This methodology is here applied to the case of manufacturing industries in Japan, and is shown to yield a rich variety of agglomeration patterns. In addition, to analyze such patterns in a more quantitative way, a new set of measures is developed that focus on both the global extent and local density of agglomeration patterns. Here it is shown for the case of Japan that these measures provide a useful classification of pattern types that reflect a number of theoretical findings in the New Economic Geography.Industrial Agglomeration, Cluster Analysis, Spatial Patterns of Agglomerations, New Economic Geography

A Probabilistic Modeling Approach to the Detection of Industrial Agglomerations: Methodological Framework

Dating from the seminal work of Ellison and Glaeser [11] in 1997, a wealth of evidence for the ubiquity of industrial agglomerations has been published. However, most of these results are based on analyses of single (scalar) indices of agglomeration. Hence it is not surprising that industries deemed to be similar by such indices can often exhibit very different patterns of agglomeration - with respect to the number, size, and spatial extent of individual agglomerations. The purpose of this paper is thus to propose a more detailed spatial analysis of agglomeration in terms of multiple-cluster patterns, where each cluster represents a (roughly) convex set of contiguous regions within which the density of establishments is relatively uniform. The key idea is to develop a simple probability model of multiple clusters, called cluster schemes, and then to seek a “best” cluster scheme for each industry by employing a standard model-selection criterion. Our ultimate objective is to provide a richer characterization of spatial agglomeration patterns that will allow more meaningful comparisons of these patterns across industries.Industrial Agglomeration, Cluster Analysis, Geodesic Convexity, Bayesian Information Criterion

Analysis of Industrial Agglomeration Patterns: An application to manufacturing industries in Japan

The standard approach to studying industrial agglomeration is to construct summary measures of the "degree of agglomeration" within each industry and to test for significant agglomeration with respect to some appropriate reference measures. But such summary measures often fail to distinguish between industries that exhibit substantially different spatial patterns of agglomeration. In a previous paper, a cluster-detection procedure was developed that yields a more detailed spatial representation of agglomeration patterns (Mori and Smith [28]). This methodology is applied here to the case of manufacturing industries in Japan, and is shown to yield a rich variety of agglomeration patterns. In addition, to analyze such patterns in a more quantitative way, a new set of measures is developed that focuses on both the global extent and local density of agglomeration patterns. Here, it is shown for the case of Japan that these measures provide a useful classification of pattern types that reflect a number of theoretical findings in the New Economic Geography.

A Probabilistic Modeling Approach to the Detection of Industrial Agglomerations

Dating from the seminal work of Ellison and Glaeser [17] in 1997, a wealth of evidence for the ubiquity of industrial agglomerations has been published. However, most of these results are based on analyses of single (scalar) indices of agglomeration. Hence it is not surprising that industries deemed to be similar by such indices can often exhibit very different patterns of agglomeration - with respect to the number, size, and spatial extent of individual agglomerations. The purpose of this paper is thus to propose a more detailed spatial analysis of agglomeration in terms of multiple-cluster patterns, where each cluster represents a (roughly) convex set of contiguous regions within which the density of establishments is relatively uniform. The key idea is to develop a simple probability model of multiple clusters, called cluster schemes, and then to seek a "best" cluster scheme for each industry by employing a standard model-selection criterion. Our ultimate objective is to provide a richer characterization of spatial agglomeration patterns that will allow more meaningful comparisons of these patterns across industries.Industrial Agglomeration, Cluster Analysis, Geodesic Convexity, Bayesian Information Criterion

The impact of study support : a report of a longitudinal study into the impact of participation in out-of-school-hours learning on the academic attainment, attitudes and school attendance of secondary school students

Study support makes a difference. It has an impact on three key aspects of students’ school careers: • attainment at GCSE and KS3 SATs; • attitudes to school; • attendance at school. These findings were consistent for all groups of students in all schools in the study. - Study support can help to improve schools and can influence the attitudes to learning of teachers and parents as well as students