Sergio J. Rey

US Regional Income Convergence: A Spatial Econometric Perspective

REY S. J. and MONTOURI B. D. (1999) US regional income convergence: a spatial econometric perspective, Reg. Studies 33 , 143-156. This study reconsiders the question of US regional economic income convergence from a spatial econometric perspective. Recently developed methods of exploratory spatial data analysis provide new insights on the geographical dynamics of US regional income growth patterns over the 1929-94 period. Strong patterns of both global and local spatial autocorrelation are found throughout the study period, and the magnitude of global spatial autocorrelation is also found to exhibit strong temporal co-movement with regional income dispersion. A spatial econometric analysis of the familiar Baumol specification reveals strong evidence of misspecification due to ignored spatial error dependence. Because of this dependence, shocks originating in one state can spillover into surrounding states, potentially complicating the transitional dynamics of the convergence process. REY S. J. et MONTOURI...

PySAL: A Python Library of Spatial Analytical Methods

This chapter describes PySAL, an open source library for spatial analysis written in the object oriented language Python. PySAL grew out of the software development activities that were part of the Center for Spatially Integrated Social Sciences Tools Project (Goodchild et al. 2000). This National Science Foundation infrastructure project had as its goals to facilitate dissemination of spatial analysis software to social sciences, to develop a library of spatial data analysis modules, to develop prototypes implementing state of the art methods, and to initiate and nurture a community of open source developers.

THE MAX-P-REGIONS PROBLEM*

In this paper, we introduce a new spatially constrained clustering problem called the max‐‐regions problem. It involves the clustering of a set of geographic areas into the maximum number of homogeneous regions such that the value of a spatially extensive regional attribute is above a predefined threshold value. We formulate the max‐‐regions problem as a mixed integer programming (MIP) problem, and propose a heuristic solution.

A Taxonomy of Spatial Econometric Models for Simultaneous Equations Systems

The spatial econometric literature has developed a large number of approaches that can handle spatial dependence and heterogeneity, yet almost all of these approaches are single equation techniques. For many regional economic problems there are both multiple endogenous variables and data on observations that interact across space. To date, researchers have often been in the undesirable position of having to choose between modeling spatial interactions in a single equation framework, or using multiple equations but losing the advantages of a spatial econometric approach. This chapter establishes a framework for applying spatial econometrics within the context of multi-equation systems. Specifically, we discuss the need for multi-equation spatial econometric models and we develop a general model that can subsume many interesting special cases. We also examine the small sample properties of common estimators for specific cases of the general model.

Perspectives on Spatial Data Analysis

This volume is inspired by the many contributions of Arthur Getis to the field of spatial analysis. In 2004, Arthur Getis formally retired as the Stephen and Mary Birch Foundation Chair of Geographical Studies in the Department of Geography at San Diego State University. That transition to emeritus status marked the end of a magnificent career spanning more than four decades. It started with undergraduate education in geography at Pennsylvania State University, followed by a PhD from the University of Washington in 1961. At Washington, he was part of the generation that initiated the “quantitative revolution” in geography under the tutelage of William Garrison. His graduate cohort included, among others, Brian Berry, Waldo Tobler, Duane Marble, John Nystuen, Richard Morrill and William Bunge. His academic appointments started with a position at Michigan State University, after which he moved to Rutgers University. He went on to become head of the Geography Department at the University of Illinois in 1977, and joined San Diego State University in 1989. In addition, he held many visiting scholar appointments at leading international institutions, including Cambridge University and the University of Bristol in the UK and the University of California, Santa Barbara and Harvard University in the USA. During his career, Arthur Getis was awarded several honors and distinctions, such as the 1995 Albert Johnson Research Lecture at San Diego State University (captured in Getis, 1995c), the Walter Isard Award from the North American Regional Science Council (1997), the Robinson Lecture at The Ohio State University (1999), and the 2002 Distinguished Scholarship Award from the Association of American Geographers (AAG). In 2005, he was elected Fellow of the Regional Science Association International. He served as president of the Western Regional Science Association (1999) and of the University Consortium of Geographic Information Science (2002).

The Performance of Alternative Integration Strategies for Combining Regional Econometric and Input-Output Models

As the number of integrated econometric and input-output models appearing in the literature grows, the need for a closer examination of the different approaches towards integration is warranted. This paper suggests a taxonomy of strategies for combining econometric and input-output models at the regional scale. The properties of the different integration strategies are then examined from a theoretical perspective, focusing on the treatment of model closure, dynamic representation. interregional linkages, sectoral aggregation, and estimation issues. Initial empirical insight as to the simulation and impact analysis capabilities of the alternative integration strategies is provided through a series of experiments employing integrated multiregional econometric and input-output models for five regions in Southern California. The results suggest that integrated models as a class can provide for improved forecasting accuracy as well as more comprehensive impact analysis capabilities over traditional non-integrated models. Additional insights are provided regarding the sensitivity of model performance to the choice of integration strategy.

Show me the code: spatial analysis and open source

This paper considers the intersection of academic spatial analysis with the open source revolution. Its basic premise is that the potential for cross-fertilization between the two is rich, yet some misperceptions about these two communities pose challenges to realizing these opportunities. The paper provides a primer on the open source movement for academicians with an eye towards correcting these misperceptions. It identifies a number of ways in which increased adoption of open source practices in spatial analysis can enhance the development of the next generation of tools and the wider practice of scientific research and education.

Spatial Analysis of Economic Convergence

This chapter reviews some of the major econometric models, approaches and issues related to the spatial dimensions of economic convergence and inequality. Key themes concern the implications of spatial dependence (autocorrelation) and heterogeneity for the specification, estimation, and interpretation of convergence models on the one hand and, on the other, the treatment of these spatial effects in the analysis of distributional dynamics and the application of related exploratory data analysis methods. We draw linkages between recent contributions in the mainstream econometric literature and developments in spatial econometrics and regional science We identify a number of areas where cross-fertilization between these fields would be beneficial.

Econometrics for Spatial Models: Recent Advances

In the introduction to New Directions in Spatial Econometrics (Anselin and Florax, 1995b), the precursor to the current volume, we set out by arguing that “it would be an overstatement to suggest that spatial econometrics has become accepted practice in current empirical research in regional science and regional economics.” However, we also pointed out that “there is evidence of an increased awareness of the importance of space in recent empirical work in ‘mainstream’ economics” (Anselin and Florax, 1995a, p. 3). In the few years since New Directions appeared, the latter observation has been confirmed by a tremendous growth in the number of publications in which spatial econometric techniques are applied, not only within regional science and economic geography, but also increasingly in the leading journals of economics, sociology and political science. This has not gone unnoticed, and the wealth of new publications has resulted in a separate classification in the Journal of Economic Literature devoted solely to cross-sectional and spatial models.1 Parallelling the growth in applications, several new methods have been introduced as well, yielding a spatial econometric toolbox that is becoming ever more sophisticated.

Spatial patterns of tropical forest trees in Western Polynesia suggest recruitment limitations during secondary succession

Spatial analysis can be used to relate the patterns of tree species to their regeneration syndromes - pioneer to late-successional - and is a first step in refining hypotheses about the species traits and biotic and abiotic factors that give rise to forest community dynamics. This study examines the spatial pattern of the most abundant trees in three 0.45-ha plots in species-poor lowland rain forests on oceanic islands in Tonga, Western Polynesia, that experience frequent natural disturbance and have a 3000-y history of shifting cultivation. We contrast secondary vs. remnant late-successional forest, with particular attention paid to the spatial dispersion and clustering of tree species, and the presence of spatial dependence in the density of seedlings and saplings. Shade-tolerant species were not strongly clustered at any scale. They did not appear to be dispersal limited, in late successional forest, and only some showed patterns consistent with density-dependent mortality (more clumped when small). Shade-tolerant species were more clumped in secondary forest, and may be dispersal-limited there because vertebrate dispersers prefer primary forest. Shade-intolerant species were clumped in gaps in late-successional forest, but some were also clumped in secondary forest, indicating that they too may be dispersal limited during secondary succession. We also compared the species composition of seedlings and saplings in the centre of plots with trees in the surrounding area and inferred that active dispersal (by vertebrate frugivores) contributed as much as 50% to site species richness.