The Prescott Spatial Growth Model (PSGM), originally designed for commercial use, has been used to develop growth scenarios from which to evaluate environmental impacts of urbanization. To facilitate further use of the PSGM in scientific research, a rigorous verification and validation of the model's capabilities is needed. Given the model's previous use in the Atlanta region to perform growth projections, this was the logical choice for a study area to perform model verification and validation. This allowed the use of historical and current data for the Atlanta regional area (see Figure 1), which was modeled in previous work by the authors (Estes et al. 2006, 2007). The purpose of this project was to develop growth scenarios for the time period of 1980-2000 using historical population, employment and land-use data. The intent of this endeavor was to validate the PSGM through comparison of scenarios generated with "blind" growth projections and those generated using actual growth for the time period. The drivers of growth are ever-changing for elected officials come and go, planning practices evolve, and current "hot-button" issues change. An exact agreement between projected and observed growth is not possible because of the complexity of decision drivers, previous development trends, and the inherent political and social variability.
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Numerous land-use and land-cover change (LULCC) models have been developed with various perspectives. Growth models may be spatial or nonspatial and typically are used for prediction and scenario generation in the context of integrated assessments of LULCC. Such models usually are implemented at local scales and may not be scalable to continental or global scales. Growth models may be grouped into two broad categories, empirical models and dynamic process simulation models. Empirically fitted models are based on statistically matching temporal trends and/or spatial patterns with a set of predictor variables (Brown et al. 2004).
Dynamic process models seek to represent the most important interactions between agents, organisms, and their environment (Brown et al. 2004). Examples of process models are cellular automata (CA) (Clarke and Gaydos 1998) and agent-based models (ABMs) White and Engelen 1993). In CA models, cells have fixed neighborhood relations and update rules. In some cases, the CA represents the state and dynamics of the environment. Cells can represent parcels of land with unique characteristics, each changing as a result of rules applied to the state of the cell and that of its neighbors. Challenges include how to establish rules that govern system behavior and incorporating heterogeneity and dynamism in these rules (Brown et al. 2004).
A widely used CA model is SLEUTH (Clarke et al. 1997) in which each grid cell is classified as either urbanized or non-urbanized. Such CA models are probabilistic, run quickly, and can be applied to any region with the necessary data. However, they lack the ability to distinguish activity types for they operate on simple "urban" and "nonurban" designations. The SLEUTH model runs in the UNIX environment and requires a tremendous amount of spatial data. The model also has neither coherent economic theory nor a behavioral component to help understand its results (EPA 1999).
Agent-based models (ABMs) are defined in terms of entities and dynamics at microlevels such as individuals (householders, farmers, developers) and/or institutions (industries, governments, etc.). Agents need their state to be defined, decision-making rules developed, and other mechanisms to perform particular behaviors. Agents' behaviors affect each other and the environment. The environment changes in response to agents and by following its own dynamics. This allows complex feedback relationships that lead to nonlinear path-dependent dynamics often observed in complex systems. …