Temporal GIS for Agent-Based Modeling of Complex Spatial Systems

Agent-based modeling has become one of the key computational approaches to simulate collective outcomes out of individual decisions in complex spatial systems. Much effort has been devoted to identifying, formulating, and experimenting with rules of local behavior for discovery of emergent, self-organizing global patterns. With emphases on computation, agent-based modeling mostly operates on cell-, lattice-, or network-based data structures (Batty 2005; Andersson et al. 2006; Andersson et al. 2006; Bithell and Macmillan 2007). While agent-based modeling aims at discerning higher orders from complex disintegrated actions, it is limited by these confined data structures that restrict neighborhood geometry and possible locations, spatial interaction structures, and local spatial scale on actions and interactions. On one hand, agent-based modeling attempts to capture spatial complexity, but on the other hand, spatial data structures used for the modeling approach inevitably over-simplify the complex nature of geographic space. In this position paper, my premise is posited upon the need for temporal GIS representation of complex properties that manifest spatiotemporal presence of geographic dynamics.