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Using Spatially Explicit Marketing Data to Build Social Simulations

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Empirical Agent-Based Modelling - Challenges and Solutions

Abstract

To construct a population of artificial agents, modellers either can use available large-scale e.g. demographic orland-use data of built-up areas. Or they rely on detailed data on cognitive and behavioural variables e.g. gathered through a domain-specific survey to craftspecific behavioural agent rules.However, both scales cannot easilybe connected. This chapter describes a method of using data stemming from geo-marketing research to support this scaling-up process with lifestyles and their localisation that are used as an empirical bridge between the micro and the macro levels.

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Acknowledgements

The author likes to thank the German Ministry for Education and Research (BMBF) for three consecutive grants to support GLOWA-Danube (Environmental Psychology) that helped to develop and refine the generic method presented here. We also acknowledge the support from Sinus Sociovision and Microm® Micromarketing Systeme und Consult GmbH for the research presented here. The author would also like to thank the numerous persons that helped carry through the research presented here, including Wolfram Mauser, Nina Schwarz, Silke Kuhn, Roman Seidl, Michael Elbers, Carsten Schulz, and Daniel Klemm.

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Authors and Affiliations

  1. Center for Environmental Systems Research, University of Kassel, Wilhelmshöher Allee 47, 34109, Kassel, Germany

    Andreas Ernst

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  1. Andreas Ernst
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Correspondence to Andreas Ernst .

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Editors and Affiliations

  1. CSIRO Ecosystem Sciences, Townsville, Queensland, Australia

    Alexander Smajgl

  2. Campus Agropolis 361, Montpellier Cedex 05, French S.Territ

    Olivier Barreteau

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Ernst, A. (2014). Using Spatially Explicit Marketing Data to Build Social Simulations. In: Smajgl, A., Barreteau, O. (eds) Empirical Agent-Based Modelling - Challenges and Solutions. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6134-0_5

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