Foundations of sustainability information representation theory: spatial–temporal dynamics of sustainable systems

Abstract

A critical need exists to broaden and deepen sustainability information foundations that can foster growth of actionable knowledge about human–environment relations to address grand challenges in sustainable system domains such as sustainable development, social–ecological systems, and hazards influencing global environmental change. Broad-based information is needed to integrate across domains to address sustainability problems cast as complex systems problems that vary widely across space and time. Deep-based information is needed to address nuanced and contextual relationships that exist within and across domains. Both broad and deep information together are needed to better address spatial–temporal dynamics in complex sustainable systems. According to many publications, the concept of sustainable systems is considered to be at the core of self-organizing systems; and in turn, the concept of self-organizing systems is at the core of social–ecological systems, coupled natural–human systems, and human–environment systems. The sustainable systems concept is elucidated in terms of ontological and epistemological foundations from geographic information representation theory. A framework for Measurement-informed Ontology and Epistemology for Sustainability Information Representation, drawing from research about ontology and epistemology in geographic information science, provides the foundation for elucidating concepts and relations about sustainability information representation in general and sustainable systems in particular. The framework is developed to form a core of sustainability information representation theory, and consequently provides a basis for articulating first principles about the character of space–time data models that can be used to create computational models within geographic information systems (GIS). An example applies the framework to common pool resources as sustainable systems. Developing the framework and exploring an example fosters intellectual bridge building between sustainability science and sustainability management in the form of a sustainability information science. That intellectual bridge building of sustainability information science supports societal progress moving knowledge into decision action toward sustainable development, encouraging new insight for designs of space–time data models, and extending GIS as an information technology foundation for sustainability management. Conclusions and directions for next steps in research about sustainability information representation theory in general and sustainable systems in particular are offered.