The computational testing of urban growth modeling

Modeling spatial systems with the Complex Systems approach has been applied to many geographic phenomena with wide applications. Simulation modeling is a powerful tool in its ability to create a system in silico, and then test the outcomes of different scenarios. Simulation modeling can help understand the complex and complicated dynamics of a system, as well as lend insight to the role of the parts of the systems, and their context. Using Complex Systems Simulation (CSS) techniques to model urban regions has had a long and promising history. The unique fabric of the city, where people, nature, culture, technology, and government all meet, has provided a rich palette to examine major geographic issues. The spatial modeling of urban systems, with or without CSS approaches, has importance as the results of such endeavors can have far-reaching impacts in our most dominating human-created geographic feature, the City.; Even with their increasingly widespread application, no formal methodology exists for the rigorous testing of CSS models and their applications. This work presents a methodology to test certain aspects of CSS models, as they are applied to Urban Growth modeling. This will lead to (i) a greater understanding of the urban simulation itself, (ii) a greater understanding of the real urban system it represents, as well as (iii) a greater understanding of the usage of complex simulations in general.; This was accomplished by examining four components of the computational testing of the SLEUTH urban growth model. These chapters examine (1) the utility of CSS approaches to recreating historical urban extent, (2) the improvement of the calibration phase of an urban growth model, (3) a new metric of assessing the topological variety of the outputs of an urban growth model, and (4) the challenges and contextual issues surrounding the linking of and urban model to a wildfire risk model.