Modeling Complex Urban Landscape Dynamics: A Pattern-Oriented Hierarchical Approach Workshop on Agent-Based Modeling of Complex Spatial Systems

As complex dynamic systems, urban landscapes emerge from the local interactions of socioeconomic and biophysical agents and processes. These complex systems are highly heterogeneous, spatially nested, and hierarchically structured (Wu and David 2002). They are prototypical complex adaptive systems, which are open, nonlinear, and highly unpredictable (Levin 1998, Portugali 2000, Gunderson and Holling 2002). Patterns emerge from numerous locally made decisions involving multiple human and biophysical agents interacting among themselves and with their environment. These agents are autonomous, adaptive and change their rules of action based upon new information. Interactions within this complex domain between agents and processes are scale dependent. While important progress has been made in modeling complex human and natural systems, the ability to simulate emergent behavior in ways that reasonably capture patterns observed in urban landscapes remains a significant research challenge. One major challenge in modeling urban landscape dynamics is in representing explicitly the human and biophysical agents at a level of disaggregation that allows us to explore the mechanisms linking patterns to processes (Portugali 2000). A second challenge in modeling the interactions between human and natural systems is that many factors operate simultaneously at different levels of organization. Additionally, since urban landscapes are spatially heterogeneous, changes in driving forces may be relevant only at certain scales (Turner et al. 1995). Yet our current understanding of the interactions between spatial scales is limited. Simulating the behavior of urban landscapes requires not only an explicit consideration of the temporal and spatial dynamics of these systems, but it also requires identifying the interactions between human and biophysical agents across the different temporal and spatial scales at which various processes operate. A new Biocomplexity research project at the University of Washington (UW) and Arizona State University (ASU) aims to develop a new framework for modeling the complex coupled human-natural system dynamics of Seattle and Phoenix metropolitan areas.* We propose a pattern-oriented hierarchical approach to model how complex agent-based interactions generate landscape patterns at multiple temporal and spatial scales. We hypothesize that similarly to other ecosystems described by Scheffer et al. (2001), in urban landscapes changes from one state (characterized by a set of processes) to another (characterized by a new set of processes) can be triggered either by the action of slowly changing variables or by relatively discrete shocks. We hypothesize that urban landscapes are spatially nested hierarchies in which the hierarchical levels correspond to structural and functional units …