Complex spatial networks: Theory and geospatial applications

Abstract Complex systems modeling approaches offer the means to examine the way in which local interactions between system components form emergent systems. Using these bottom‐up modeling approaches in combination with geographic information systems (GIS) and geospatial data, the complexity inherent to spatial phenomena including geographical, urban, ecological, or geophysical systems can be captured and represented. Scientific research in the field of network science also uses a complex systems approach to conceptualize, model, and analyze geospatial systems as networks. Despite having common characteristics, complexity, geographic information, and network sciences are not yet fully integrated. Therefore, the main objective of this article is to provide a comprehensive review of scientific research related to network theory and to evaluate the potential of their integration with complex systems modeling approaches originating in the field of geographic information science (GISc). This article finds that existing literature focuses on characterizing static spatial network structures to better understand the dynamics that take place on or within them. This article argues for a necessity in research advancements to explore the way in which real spatial network structures evolve in response to spatial dynamics and advocates for the integration of geographic automata systems (GAS) modeling approaches with networks to do so. The mathematical foundation for graph theory, including the measures that are used to describe networks and the theoretical graph types, are introduced. Geospatial applications of networks and graph theory are also presented. Examples of network‐based automata models are presented as avenues for future research work in evolving spatial networks as part of GISc and computational geography.