Landuse Evolution and Impact Assessment Model
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.
What is LEAM?
Overview 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 Corps 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.View the LEAM Tri-fold Brochure View the LEAM Technical Brochure
LEAM urban land-use transformation modeling begins with drivers, those forces (typically human) that contribute to land-use change. Model drivers represent the dynamic interactions between the urban system and the surrounding landscape. Each driver is developed as a contextually independent sub-model which allows for calibration before being run simultaneously in the LEAM model. Environmental, economic and social system impacts of alternative scenarios such as different land-use policies, growth trends, and unexpected events can be tested out in the LEAM modeling environment. Scenario results and impact assessments can be displayed in a number of ways: as simulation movies, through a built-in mapping tool, in graph or chart displays, or simply as raw data. LEAM's visual representation of each scenario's outcome provides an intuitive means of understanding the potential of decisions and acts as a catalyst for discussion and communal decision-making. All driver models figure into creating the development probability model, while the impact models respond to the land use change that is triggered by the development probability model. Impacts assessed by the LEAM model are also used in the creation of sustainable indices and indicators that can feed back into the model drivers for new policy formation.
We all use our own mental models of the way the world works, such as the process of crossing a busy street or ducking a flying rock. Components of a mental model may include time delays before the appearance of any effect from our action, the effects of random events, positive and negative feedback from our action or the environment. Crossing the street is a relatively simple system for us to use. When we deal with complex systems such as an entire ecosystem, community and changes through time, our mental models need help. The process of dynamic computer modeling is an aid to the mind that increases our ability to understand the relationships between specific components of the system we are looking at. Linking dynamic model components in a spatial referenced environment (a GIS layer or map) is a valuable tool for understanding possible impacts of land-use decision through visualization of what things might look like under various scenarios and policies. Modeling is a creative endeavor. New solutions to problems can be investigated to understand their relative impact on a system, and new questions become apparent through as the process evolves.
This model determines the growth potential of all land within the greater LEAM model. 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 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 according to local uniqueness of the study area. 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. Most land use types are assessed this way. However, if the initial land use is agricultural, its future land use is determined by comparing the results of the Development Probability Model to those generated by a competing Open Space Probability Model. Consequently, an agricultural cell has the potential of becoming urbanized, conserved as open space, or it may remain agricultural.
As land use changes from one type to another during the course of the model runs, they often impact other features, species, and forces present in the landscape. The LEAM framework is able to track these changes using impact models. Current impact models consider the implications of land use change for regional water quality, runoff volume, natural resources, green infrastructure and high school districts.
LEAM Works' Study Board
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 in Stockholm Sweden and the American University of Sharjah, Sharjah. Together, we are working to improve the LEAM model and extend its functionality.
The KTH Team is led by Dr. Vladimir Cvetkovic. Student collaborators at KTH include Romain Goldenberg. The AUS Team is led by Dr. Varkki Pallathucheril.
The Plone® is the platform on which people can freely calculate the cities’ developing possibility map and estimated land use maps.
After you upload the required information of your cities/regions on Plone®, these inputs would be analyzed by LEAM following the steps introduced in the study board.
What we can do for you
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