Better tools are needed to manage regional dynamics, not just as economic systems or static inventories of resources, but as complex systems that are part of regional and global networks. However, effective management requires both that we understand the systems to be administered and that we understand the implications of our strategies. We have attempted here to outline an approach for understanding the dynamics of urban systems and the potential implications of urban policy and investment management decisions. We described one modeling approach — LEAM — that utilizes cellular automata and other technological advances in spatial simulation modeling to help improve a community’s ability to make ecologically and economically sound decisions. LEAM was intended to enable users to capture stochastic influences and view the reported probable consequences of intended events in a scenario-based format that is comprehensible by local experts, decision-makers, and stakeholders.
The LEAM Model, its development, and its application to several regions within the continental United States is conducted and managed by a team of faculty, staff, and students at the University of Illinois at Urbana-Champaign. This team brings together expertise in substantive issues, modeling, high-performance computing, and visualization coming from the departments of Landscape Architecture, Urban Planning, Geography, Economics, Natural Resources and Environmental Sciences, the National Center for Supercomputing Applications (NCSA), the United States Army Corp of Engineers, and private industry. The mission of the LEAM group is to help others understand the relationships between human economic/cultural activities and biophysical cycles from a changing land use perspective. All of us must realize that these interacting systems behave in very complex and dynamic ways. Understanding the extent of how one system affects another will allow us to make better land use management decisions in the future.
LEAM urban land-use transformation modeling begins with drivers, those forces (typically human) that contribute to land-use change.Population, geography, and land use for a particular study area serve as background information from which decisions are based concerning future land use changes. The development probability for any cell within the study area is firstly determined by the input of other driver sub-models. Economics, transportation, utilities, neighboring land uses, and random chance all contribute to a final growth decision within a given cell. Each of these factors is weighted to determine the cell's development probability value. Based on this probability value, the land use classification of a given cell either remains as its initial type or transitions to a new urban type. Here is a study board to show how LEAM works step by step.
The University of Illinois LEAM TEAM works with the KTH Royal Institute of Technology and Stockholm University in Stockholm Sweden and the American University of Sharjah, Sharjah. Together, we are working to improve the LEAM model and extend its functionality in urban-regional development and sustainable planning.