Urban growth and the resultant sprawling patterns of development are causing social, economic, and environmental strains on U.S. communities (Schmidt 1998). According to the Sierra Club, undesirable urban growth, also known as urban sprawl, has become one of costliest problems in America. With growing concerns about the negative impacts of these development patterns, public agencies and policy officials are seeking principles and tools designed to manage land-use change under the flag of "smart growth" or "sustainable growth."
During the past two decades, spatial analysis tools, geographic information systems (GIS), and remote sensing (RS) technologies have been widely deployed to monitor, analyze, and visualize the urban growth phenomena. Maps and satellite images, however, are limited to static displays of past and current data sets. They portray the current state of the system, with neither the reasons for it nor any possible future outlooks. Although GIS-based tools provide useful analysis and have been widely used to assist urban planners, the static mapping concepts on which they are built are clearly insufficient to study the dynamics of urban growth (Hopkins 1999). The causal mechanisms associated with land-use change remain relatively poorly understood, in part because of the complexity of urban systems. Consequently, policy makers and planners often are faced with the difficult tasks of making land-use decisions without sufficient analyses or vision.
Very recently, computer-based urban system simulation models are being employed to forecast and evaluate land-use change (Batty and Xie 1994, Birkin 1994, Landis 1994, Engelen et al. 1995, Wu and Webster 2000, Waddell 2002). These models represent a spatial and dynamic approach that enables planners to view and analyze the future outcomes of current decisions and policies before they are put into action. These models have the ability to help improve our fundamental understanding of the dynamics of land-use transformation and the complex interactions between urban change and sustainable systems (Deal 2001). These spatial dynamic modeling techniques are becoming essential elements in the Planning Support System (PSS) literature (Hopkins 1999, Kammeier 1999).
To date, however, spatial dynamic urban modeling is still in its infancy. Few models have been built that are able to represent the complex dynamics of urban land-use change that are consistent with observable data (Almeida 2003). As a result, few such models are operational and are used to assist urban planning practices.
In this paper, we present a comprehensive dynamic spatial urban simulation model, the Land-use Evolution and Impact Assessment Model (LEAM). LEAM originally was developed as a research project by an interdisciplinary team of researchers at the University of Illinois with support from the National Science Foundation. After a successful full-scale pilot application in Peoria, Illinois, LEAM has been selected to assist planning practices in the St. Louis metropolitan area, as part of the Department of Defense (DOD) encroachment analysis and as part of the Smart Growth initiative introduced by the state of Illinois. Described here is a bistate application of LEAM consisting of the five counties in southwestern Illinois and the five counties in east central Missouri that make up the St. Louis metropolitan region. In the following sections, the conceptual framework and relevant features of the LEAM simulation environment is described, followed by the results of the St. Louis metropolitan regional application.
LEAM is a new modeling environment designed to support regional planning practices. Understanding the interactions between subsystems in complex urban environments will enable policy makers and planners to make better land-use management decisions. However, interacting systems behave in very complex and dynamic ways. …