Defining the Spatial Scale in Modern Regional Analysis

This book explores different approaches to defining the concept of region depending on the specific question that needs to be answered. While the typical administrative spatial data division fits certain research questions well, in many cases, defining regions in a different way is fundamental in order to obtain significant empirical evidence. The book is divided into three parts: The first part is dedicated to a methodological discussion of the concept of region and the different potential approaches from different perspectives. The problem of having sufficient information to define different regional units is always present. This justifies the second part of the book, which focuses on the techniques of ecological inference applied to estimating disaggregated data from observable aggregates. Finally, the book closes by presenting several applications that are in line with the functional areas definition in regional analysis.

Auteur

Esteban Fernández-Vázquez is associate professor in the Department of Applied Economics of the University of Oviedo (Spain) and is a member of REGIOlab. He received his PhD in 2004 and was appointed associate professor in 2011. He is also Affiliate Research Professors at the Regional Economics Applications Laboratory (REAL) of the University of Illinois at Urbana-Champaign. His research focus on regional modelling in general, particularly on input-output models and the use of entropy econometrics estimators.

Fernando Rubiera is associate professor in the Department of Applied Economics of the University of Oviedo (Spain). He is also the coordinator of the Laboratory of Regional Economics REGIOlab. He received his PhD in 2003 and was appointed associate professor in 2008. His research lines are focused on the contribution of cities to regional growth and economic specialization.

Contenu
Preface.- Part I: Building Economic Areas from Local Data.- Part II: Estimation of Spatial Desaggregated Data.- Part III: Applications of Spatial Analysis with Small Area Observations.