Table of Contents

Handbook of Research Methods and Applications in Spatially Integrated Social Science

Handbook of Research Methods and Applications in Spatially Integrated Social Science

Handbooks of Research Methods and Applications series

Edited by Robert Stimson

The chapters in this book provide coverage of the theoretical underpinnings and methodologies that typify research using a Spatially Integrated Social Science (SISS) approach. This insightful Handbook is intended chiefly as a primer for students and budding researchers who wish to investigate social, economic and behavioural phenomena by giving explicit consideration to the roles of space and place. The majority of chapters provide an emphasis on demonstrating applications of methods, tools and techniques that are used in SISS research, including long-established and relatively new approaches.

Chapter 26: Graphical models and Bayesian networks as a spatial analytical tool

David Rohde and Jonathan Corcoran

Subjects: economics and finance, regional economics, geography, economic geography, environmental geography, human geography, research methods in geography, research methods, research methods in economics, research methods in geography, urban and regional studies, regional economics, research methods in urban and regional studies


The role of location is central to spatially integrated social science in which the focus is to enhance our understanding, explanation and prediction of complex human behaviours and social processes (Goodchild and Janelle, 2004). A key approach to addressing these foci is the use of models that facilitate the synthesis of complex spatial data, many of which are probabilistic in nature and can be analysed through graphical models. The purpose of graphical models is to combine probability theory with graph theory allowing for increased interpretability of underlying assumptions, obtaining computational tractability of models and the inferring of causality. In the field of spatially integrated social science there are two key contributions of graphical models: · The first is the construction of sophisticated spatial models allowing a complex synthesis of spatial information. · The second is the capacity to infer causal relationships, the benefit here is that in social science it is rarely applicable to run large-scale experimentation in contrast to, for example, medical trials; causal graphical models however still allow causal relationships to be inferred as if an experimentation was undertaken. Recent innovation in both of these areas is of potentially great interest in developing more sophisticated methods for spatially integrated social science.

You are not authenticated to view the full text of this chapter or article.

Elgaronline requires a subscription or purchase to access the full text of books or journals. Please login through your library system or with your personal username and password on the homepage.

Non-subscribers can freely search the site, view abstracts/ extracts and download selected front matter and introductory chapters for personal use.

Your library may not have purchased all subject areas. If you are authenticated and think you should have access to this title, please contact your librarian.

Further information