Towards the integration of complex systems theory, geographic information science, and network science for modelling geospatial phenomena

A complex systems approach conceptualizes spatial systems from the bottom-up to better understand how local spatial interactions generate emergent system-level behavior and spatial patterns at large spatial extents. This approach can be applied to examine ecological, urban, and social systems within contexts of geographic space and time. Geographic automata systems (GAS) including cellular automata (CA) and agent-based models (ABM) are spatio-temporal modelling frameworks that are rooted in complex systems theory. In a similar manner, network theory uses a complex systems approach to represent and analyze spatial systems as sets of georeferenced nodes and links that form measurable spatial networks. Separately, GAS and network-based approaches offer unique advantages in exploring and analyzing complex systems, however the two approaches are rarely integrated. Therefore, the purpose of this dissertation is to explore the intersection of complex systems theory, geographic information science, and network theory to leverage the advantages of each field for better understanding a variety of complex spatial systems. The main objective is to develop a suite of novel network-based automata modelling approaches that simulate complex dynamic spatial systems as measurable, evolving, spatial networks. Three novel modelling approaches are developed including: a geographic network automata (GNA) model that uses spatial networks, network-based transition rules, and network analysis for the representation of complex spatial systems; a network-based ABM (N-ABM) that integrates networks not as inputs for the ABM, but as a novel way to conceptualize, analyze, and communicate the model and model results; and a network based validation approach for the testing of ABMs. Obtained results demonstrate that the integration of complex systems theory, geographic information science, and network theory offers new means for the representation, analysis, communication, and testing of GAS and the complex systems they represent, thus helping to thus helping to "open the black box". Furthermore, the presentation of modelling results in application to insect infestation and disease transmission contribute to the enhancement of decision-making processes by providing tools that can be used in forecasting and scenario testing. This dissertation contributes new methodological frameworks to the fields of geographic information science, GAS, and network theory.