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 12: Forecasting in social science research: imperatives and pitfalls

Tony Sorensen

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

Extract

Social science research typically investigates society’s evolving economic and social conditions and the processes shaping them. The theories we develop or refine to explain such conditions and processes are often tested retrospectively. One approach is to apply simulation models, grounded in our theories, to previously known conditions and project them forward to assess how well they predict present circumstance. Somewhat less frequently, research topics look beyond the present to identify society’s looming conditions, their associated problems and opportunities, and possible need for public regulation or control. This task requires not just a sound conceptualization of contemporary processes, but also an understanding of how they are likely to change during the forecast period. We know, for example, that the relative importance of different component variables in a system will almost certainly rise and fall, while others will be added or deleted, and the patterns of causality between them will reconfigure. These, in turn, reflect technological advance; shifting supply and demand relationships in resources, goods and services; and human perceptions about needs and wants. Unsurprisingly, forecasting is the more difficult of the two tasks because it combines sound knowledge of current circumstance and process with informed, but nevertheless speculative, analysis about their future trajectories. One of the distinguishing traits of humanity, as distinct from other species, is our ability to forecast, but the ends and means employed have evolved massively over millennia.

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