Spatial data infrastructures as complex adaptive systems

Abstract

Many researchers throughout the world have been struggling to better understand and describe spatial data infrastructures (SDIs). Our knowledge of the real forces and mechanisms behind SDIs is still very limited. The reason for this difficulty might lie in the complex, dynamic and multifaceted nature of SDIs. To evaluate the functioning and effects of SDIs we must have a proper theory and understanding of their nature. This article describes a new approach to understanding SDIs by looking at them through the lens of complex adaptive systems (CASs). CASs are frequently described by the following features and behaviours: complexity, components, self-organization, openness, unpredictability, nonlinearity and adaptability, scale-independence, existence of feedback loop mechanism and sensitivity to initial conditions. In this article both CAS and SDI features are presented, examined and compared using three National SDI case studies from the Netherlands, Australia and Poland. These three National SDIs were carefully analysed to identify CAS features and behaviours. In addition, an Internet survey of SDI experts was carried out to gauge the degree to which they consider SDIs and CASs to be similar. This explorative study provides evidence that to a certain extent SDIs can be viewed as CASs because they have many features in common and behave in a similar way. Studying SDIs as CASs has significant implications for our understanding of SDIs. It will help us to identify and better understand the key factors and conditions for SDI functioning. Assuming that SDIs behave much like CASs, this also has implications for their assessment: assessment techniques typical for linear and predictable systems may not be valid for complex and adaptive systems. This implies that future studies on the development of an SDI assessment framework must consider the complex and adaptive nature of SDIs.

Keywords:

• spatial data infrastructures
• complex adaptive systems
• SDI assessment

Acknowledgements

We acknowledge and thank the participants of the workshop on ‘Multi-view framework to assess National Spatial Data Infrastructures’, held in Wageningen in 2007, for taking the trouble to complete the online questionnaire. We also thank the Dutch ‘Space for Geo-Information’ (RGI) innovation programme for providing the necessary resources to conduct this research. We are particularly grateful to Wojciech Pachelski, Ben Searle, Ian Philip Williamson, Kevin McDougall and Watse Castelein for reviewing the facts contained in this article about the Polish, Australian and Dutch SDIs respectively. We also thank the anonymous reviewers of this article for their valuable remarks and suggestions.

Notes

1. The main research institutes dedicated to this research are Santa Fe Institute (http://www.santafe.edu); University of Michigan Center for the Study of Complex Systems (CSCS) (http://www.cscs.umich.edu/); Northwestern Institute on Complex Systems (NICO) (http://www.northwestern.edu/nico/); Max-Planck Institute for Physics of Complex Systems (http://www.mpipks-dresden.mpg.de/); Center for Complex Systems Research (http://www.ccsr.uiuc.edu/); New England Complex Systems Institute (NECSI) (http://www.necsi.org/); Center for Complex Systems (OBUZ) – ISS Warsaw University (http://www.iss.uw.edu.pl/osrodki/obuz/OBUZNEW_ENG/obuz.htm); Banding Fe Institute Official Web (http://www.bandungfe.net/)