Fall Semester 2003
Geography Colloquium - Friday, October 3, 2003
Peter Deadman, University of Waterloo,
"LUCITA: An Agent Based Simulation of Land-Use
Change in the Amazon"
Abstract
This presentation will provide an overview of LUCITA,
an agent-based, spatially referenced, model designed to explore age,
period, and cohort effects associated with land use/cover change in the
Altamira region of Brazil. The study site on the Amazon frontier is
characterized by the development of family farms on 100 hectare lots
arranged along the Transamazon highway and a series of side roads, west
of Altamira, Brazil. The model simulates the land use behaviour of
farming households based on a heuristic decision making strategy that
utilizes burn quality, subsistence requirements, household
characteristics, and soil quality as key factors in the decision making
process. The actions and interactions of the household agents determine
the land-cover of their plots and ultimately that of the region. This
presentation describes the model, presents preliminary results, and
outlines the issues and goals for the future development of the model.
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Wednesday, September 24, 2003
R. Gil Pontius, Jr.,
Assistant Professor at Clark University in both The Graduate School of Geography
and The Department of International Development, Community and Environment,
"Will We Learn Anything
from Complex Land Change Models?:
The Empirical Evidence."
Abstract
Millions of dollars, CPU-hours, and researcher-years are being dedicated at the world's top research institutions to create quantitative models of land-use & -cover change (LUCC). This presentation examines whether these efforts are likely to result in a measurable increase in knowledge. The project leader of the Central Massachusetts - Human Environment Regional Observatory (CM-HERO) will dissect his own project in order to describe its promise and to expose its challenges. The presentation will put the CM-HERO research in the context of other efforts to model LUCC. After years of searching, the presenter has failed to find any prediction from a LUCC model that is more accurate than the prediction a scientist could have made without the model, at the resolution of the model's data. The seminar will examine whether this is a cause for concern by using the latest methods of LUCC model validation.
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Last Updated: May 11, 2005
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