Statistics for Spatial Data.

An introduction to spatial econometric models and methods is provided that discusses spatial autoregressive processes that can be used to extend conventional regression models. Estimation and interpretation of these models are illustrated with an applied example that examines the relationship between commuting to work times and transportation mode choice for a sample of 3,110 US counties in the year 2000. These extensions to conventional regression models are useful when modeling cross-sectional regional observations or and panel data samples collected from regions over both space and time can be easily implemented using publicly available software. Use of these models for the case of non-spatial structured dependence is also discussed.

/pdf/introduction-to-spatial-econometrics-9xcsp6v7d6.pdf

Introduction to spatial econometrics

With digital equipment becoming increasingly networked, either on wired or wireless networks, for personal and professional use alike, distributed software systems have become a crucial element in information and communications technologies. The study of these systems forms the core of the ARLES' work, which is specifically concerned with defining new system software architectures, based on the use of emerging networking technologies. In this context, we concentrate on the study of distributed systems which bring to life the vision of ubiquitous computing systems, also known as ambient intelligence.

반도체 공정 overview

The development of specialized software for spatial data analysis has seen rapid growth since the lack of such tools was lamented in the late 1980s by Haining (1989) and cited as a major impediment to the adoption and use of spatial statistics by GIS researchers. Initially, attention tended to focus on conceptual issues, such as how to integrate spatial statistical methods and a GIS environment (loosely vs. tightly coupled, embedded vs. modular, etc.), and which techniques would be most fruitfully included in such a framework. Familiar reviews of these issues are represented in, among others, Anselin and Getis (1992), Goodchild et al. (1992), Fischer and Nijkamp (1993), Fotheringham and Rogerson (1993, 1994), Fischer et al. (1996), and Fischer and Getis (1997). Today, the situation is quite different, and a fairly substantial collection of spatial data analysis software is readily available, ranging from niche programs, customized scripts and extensions for commercial statistical and GIS packages, to a burgeoning open source effort using software environments such as R, Java and Python. This is exemplified by the growing contents of the software tools clearing house maintained by the U.S.- based Center for Spatially Integrated Social Science [CSISS] (see http://www.csiss.org/clearinghouse/).

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.0016-7363.2005.00671.x

GeoDa: An Introduction to Spatial Data Analysis

Economics and Information Theory

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...

/pdf/us-regional-income-convergence-a-spatial-econometric-58m9i2re8h.pdf

US Regional Income Convergence: A Spatial Econometric Perspective

Specification searches in spatial econometrics: the relevance of Hendry's methodology

This paper suggests some new empirical strategies for analyzing the evolution of regional income distributions over time and space. These approaches are based on extensions to the classical Markov transition matrices that allow for a more comprehensive analysis of the geographical dimensions of the transitional dynamics. This is achieved by integrating some recently developed local spatial statistics within a Markov framework. Insights to not only the frequency with which one economy may transition across different classes in the income distribution, but also how those transitions may or may not be spatially dependent are provided by these new measures. A number of indices are suggested as ways to characterize the space-time dynamics and are illustrated in a case study of U. S. regional income dynamics over the 1929–1994 period.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1538-4632.2001.tb00444.x

Spatial Empirics for Economic Growth and Convergence

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.

/pdf/pysal-a-python-library-of-spatial-analytical-methods-jxuvcx3xhd.pdf

Sergio J. Rey

Papers

US Regional Income Convergence: A Spatial Econometric Perspective

Specification searches in spatial econometrics: the relevance of Hendry's methodology

Spatial Empirics for Economic Growth and Convergence

PySAL: A Python Library of Spatial Analytical Methods

Advances in Spatial Econometrics