Abstract
Many real-world spatial systems can be conceptualized as networks. In these conceptualizations, nodes and links represent system components and their interactions, respectively. Traditional network analysis applies graph theory measures to static network datasets. However, recent interest lies in the representation and analysis of evolving networks. Existing network automata approaches simulate evolving network structures, but do not consider the representation of evolving networks embedded in geographic space nor integrating actual geospatial data. Therefore, the objective of this study is to integrate network automata with geographic information systems (GIS) to develop a novel modelling framework, Geographic Network Automata (GNA), for representing and analyzing complex dynamic spatial systems as evolving geospatial networks. The GNA framework is implemented and presented for two case studies including a spatial network representation of (1) Conway’s Game of Life model and (2) Schelling’s model of segregation. The simulated evolving spatial network structures are measured using graph theory. Obtained results demonstrate that the integration of concepts from geographic information science, complex systems, and network theory offers new means to represent and analyze complex spatial systems. The presented GNA modelling framework is both general and flexible, useful for modelling a variety of real geospatial phenomena and characterizing and exploring network structure, dynamics, and evolution of real spatial systems. The proposed GNA modelling framework fits within the larger framework of geographic automata systems (GAS) alongside cellular automata and agent-based modelling.
Highlights
As geospatial data becomes increasingly available, networks are used as a powerful conceptual framework to represent and analyze a wide array of complex spatial systems in social, urban, and ecological contexts [1,2,3]
The output of the GNAGOL is a series of spatial networks SNGOL that evolve as a function of transition rules R that are applied to the underlying network UNGOL
The measured evolving network SNGOL structure is limited by the underlying random geometric graph (RGG) structure UNGOL and the GNAGOL in both scenarios produce an evolving spatial network SNGOL that is an RGG
Summary
As geospatial data becomes increasingly available, networks are used as a powerful conceptual framework to represent and analyze a wide array of complex spatial systems in social, urban, and ecological contexts [1,2,3]. Some examples of this include exploring processes of gentrification on a static network of residential properties [11], ecological dispersal dynamics on static landscape connectivity networks [3,12], mobility dynamics on static road networks [13,14], and epidemics on static contact networks [7,15] and on airline networks [16] Models representing phenomena such as predator–prey dynamics [17], fungal growth [9], and human epidemics [18] as evolving non-spatial networks have been developed by applying sub-rules representing network dynamics to network structures that alter the network structure itself over time. This section first presents the general GNA modelling framework for the network representation of real-world spatial phenomena and second introduces the theoretical background for the application of graph theory to analyze the GNA spatial network SN outputs
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