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Languages : en
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Book Description
Regions, independent of their geographic level of aggregation, are known to be interrelated partly due to their relative locations. Similar economic performance among regions can be attributed to proximity. Consequently, a proper understanding, and accounting, of spatial liaisons is needed in order to effectively forecast regional economic variables. Several spatial econometric techniques are available in the literature, which deal with the spatial autocorrelation in geographically-referenced data. The experiments carried out in this paper are concerned with the analysis of the spatial autocorrelation observed for unemployment rates in 439 NUTS-3 German districts. We employ a semi-parametric approach - spatial filtering - in order to uncover spatial patterns that are consistently significant over time. We first provide a brief overview of the spatial filtering method and illustrate the data set. Subsequently, we describe the empirical application carried out: that is, the spatial filtering analysis of regional unemployment rates in Germany. Furthermore, we exploit the resulting spatial filter as an explanatory variable in a panel modelling framework. Additional explanatory variables, such as average daily wages, are used in concurrence with the spatial filter. Our experiments show that the computed spatial filters account for most of the residual spatial autocorrelation in the data.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Regions, independent of their geographic level of aggregation, are known to be interrelated partly due to their relative locations. Similar economic performance among regions can be attributed to proximity. Consequently, a proper understanding, and accounting, of spatial liaisons is needed in order to effectively forecast regional economic variables. Several spatial econometric techniques are available in the literature, which deal with the spatial autocorrelation in geographically-referenced data. The experiments carried out in this paper are concerned with the analysis of the spatial autocorrelation observed for unemployment rates in 439 NUTS-3 German districts. We employ a semi-parametric approach - spatial filtering - in order to uncover spatial patterns that are consistently significant over time. We first provide a brief overview of the spatial filtering method and illustrate the data set. Subsequently, we describe the empirical application carried out: that is, the spatial filtering analysis of regional unemployment rates in Germany. Furthermore, we exploit the resulting spatial filter as an explanatory variable in a panel modelling framework. Additional explanatory variables, such as average daily wages, are used in concurrence with the spatial filter. Our experiments show that the computed spatial filters account for most of the residual spatial autocorrelation in the data.
Author: Roberto Patuelli
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Regions, independent of their geographic level of aggregation, are known to be interrelated partly due to their relative locations. Similar economic performance among regions can be attributed to proximity. Consequently, a proper understanding, and accounting, of spatial liaisons is needed in order to effectively forecast regional economic variables. Several spatial econometric techniques are available in the literature, which deal with the spatial autocorrelation in geographically-referenced data. The experiments carried out in this article are concerned with the analysis of the spatial autocorrelation observed for unemployment rates in 439 NUTS-3 German districts. We employ a semi-parametric approach - spatial filtering - in order to uncover spatial patterns that are consistently significant over time. We first provide a brief overview of the spatial filtering method and illustrate the data set. Subsequently, we describe the empirical application carried out: that is, the spatial filtering analysis of regional unemployment rates in Germany. Furthermore, we exploit the resulting spatial filter as an explanatory variable in a panel modelling framework. Additional explanatory variables, such as average daily wages, are used in concurrence with the spatial filter. Our experiments show that the computed spatial filters account for most of the residual spatial autocorrelation in the data.
Author: Griffith, Daniel A.
Publisher: Edward Elgar Publishing
ISBN: 1800372825
Category : Social Science
Languages : en
Pages : 125
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Book Description
This Advanced Introduction provides a critical review and discussion of research concerning spatial statistics, differentiating between it and spatial econometrics, to answer a set of core questions covering the geographic-tagging-of-data origins of the concept and its theoretical underpinnings, conceptual advances, and challenges for future scholarly work. It offers a vital tool for understanding spatial statistics and surveys how concerns about violating the independent observations assumption of statistical analysis developed into this discipline.
Author: Daniel A. Griffith
Publisher:
ISBN:
Category :
Languages : en
Pages : 21
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Book Description
Author: Daniel A. Griffith
Publisher: Springer
ISBN: 331972553X
Category : Business & Economics
Languages : en
Pages : 272
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Book Description
This book treats the notion of morphisms in spatial analysis, paralleling these concepts in spatial statistics (Part I) and spatial econometrics (Part II). The principal concept is morphism (e.g., isomorphisms, homomorphisms, and allomorphisms), which is defined as a structure preserving the functional linkage between mathematical properties or operations in spatial statistics and spatial econometrics, among other disciplines. The purpose of this book is to present selected conceptions in both domains that are structurally the same, even though their labelling and the notation for their elements may differ. As the approaches presented here are applied to empirical materials in geography and economics, the book will also be of interest to scholars of regional science, quantitative geography and the geospatial sciences. It is a follow-up to the book “Non-standard Spatial Statistics and Spatial Econometrics” by the same authors, which was published by Springer in 2011.
Author: Norbert Schanne
Publisher: wbv Media GmbH & Company KG
ISBN: 3763940960
Category : Law
Languages : en
Pages : 209
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Book Description
Warum sollen Regionen innerhalb eines Landes unabhängige Inseln sein? Und warum sollen, über das gesamte Land hinweg, einheitlich starke ökonomische oder soziale Wirkungszusammenhänge bestehen? Diese zwei Annahmen werden in der angewandten empirischen Wirtschafts- und Sozialforschung üblicherweise implizit unterstellt. Wie in statistischen Verfahren von dieser unrealistischen Modellstruktur unter Ausnutzung der räumlichen Strukturen in beobachteten Variablen und unterstellten Zusammenhängen abgewichen werden kann, diskutiert Norbert Schanne im vorliegenden Band. Möglichkeiten, unser Verständnis der Ökonomie zu vertiefen, werden ebenso verdeutlicht, wie Chancen und Tücken beim Einsatz der Methoden in Studien zu verschiedenen Aspekten der Arbeitsmarktdynamik.
Author: Yongwan Chun
Publisher: SAGE
ISBN: 1446291626
Category : Reference
Languages : en
Pages : 202
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Book Description
"Ideal for anyone who wishes to gain a practical understanding of spatial statistics and geostatistics. Difficult concepts are well explained and supported by excellent examples in R code, allowing readers to see how each of the methods is implemented in practice" - Professor Tao Cheng, University College London Focusing specifically on spatial statistics and including components for ArcGIS, R, SAS and WinBUGS, this book illustrates the use of basic spatial statistics and geostatistics, as well as the spatial filtering techniques used in all relevant programs and software. It explains and demonstrates techniques in: spatial sampling spatial autocorrelation local statistics spatial interpolation in two-dimensions advanced topics including Bayesian methods, Monte Carlo simulation, error and uncertainty. It is a systematic overview of the fundamental spatial statistical methods used by applied researchers in geography, environmental science, health and epidemiology, population and demography, and planning. A companion website includes digital R code for implementing the analyses in specific chapters and relevant data sets to run the R codes.
Author: Thomas Scherngell
Publisher: Springer Science & Business Media
ISBN: 3319026992
Category : Business & Economics
Languages : en
Pages : 382
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Book Description
The geography of networks and R&D collaborations, in particular the spatial dimension of interactions between organisations performing joint R&D, have attracted a burst of attention in the last decade, both in the scientific study of the networks and in the policy sector. The volume is intended to bring together a selection of articles providing novel theoretical and empirical insights into the geographical dynamics of such networks and R&D collaborations, using new, systematic data sources and employing cutting-edge spatial analysis and spatial econometric techniques. It comprises a section on analytic advances and methodology and two thematic sections on structure and spatial characteristics of R&D networks and the impact of R&D networks and policy implications. The edited volume provides a collection of high-level research contributions with an aim to contribute to the recent debate in economic geography and regional science on how the structure of formal and informal networks modifies and influences the spatial and temporal diffusion of knowledge.
Author: Roberto Patuelli
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Socio-economic interrelationships among regions can be measured in terms of economic flows, migration, or physical geographically-based measures, such as distance or length of shared areal unit boundaries. In general, proximity and openness tend to favour a similar economic performance among adjacent regions. Therefore, proper forecasting of socio-economic variables, such as employment, requires an understanding of spatial (or spatio-temporal) autocorrelation effects associated with a particular geographic configuration of a system of regions. Several spatial econometric techniques have been developed in recent years to identify spatial interaction effects within a parametric framework. Alternatively, newly devised spatial filtering techniques aim to achieve this end as well through the use of a semi-parametric approach. The experiments presented in this paper deal with the analysis of and accounting for spatial autocorrelation by means of spatial filtering techniques for data pertaining to regional unemployment in Germany. The available dataset comprises information about the share of unemployed workers in 439 German districts (the NUTS-III regional aggregation level). In this paper, various results based upon an eigenvector spatial filter model formulation (that is, the use of orthogonal map pattern components), constructed for the 439 German districts, are presented, with an emphasis on their consistency over several observation years.
Author: Felix Haifeng Liao
Publisher: Routledge
ISBN: 1351669788
Category : Business & Economics
Languages : en
Pages : 213
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Book Description
This book investigates uneven regional development in China – with particular focus on the cases of Guangdong and Zheijiang provinces – which have been at the forefront of debate since Chinese economic reform. Rapid economic growth since the ‘opening-up’ of China has been accompanied by significant disparities in the regional distribution of income: this book represents one of the most recent studies to present a picture of this inequality. Built upon a multi-scale and multi-mechanism framework, it provides systematic examination of both the patterns and mechanisms of regional development and inequality in provincial China, emphasizing the effects of economic transition. Approaching from a geographical perspective, its authors consider the interplay between the local, the state, and the global forces in shaping the landscape of regional inequality in China. Extensive empirical findings will prove useful to those researching other developing countries within the frontier of globalization and economic transition. Regional Inequality in Transitional China will appeal to scholars and students of geography, economics and Chinese studies more broadly.
