An agent-based model was developed to simulate the emergent phenomenon known as "desakota", the rapid urbanization of densely populated rural populations in the newly developed world.
 
Our model first determines the rate of growth of different spatial aggregates using linear statistical analysis. It then allocates this growth to the local level using developer agents who determine the transformation or mutation of rural households to urban pursuits based on local land costs, accessibilities, and growth management practices. The model is applied to desakota development in the Suzhou region between 1990 and 2000. We show how the global rates of change predicted at the township level in the Wuxian City region surrounding Suzhou are tempered by local transformations of rural to urban land uses which we predict using cellular automata rules.
 
The model, which is implemented in the Repast toolkit, is validated using a blend of data taken from remote sensing and government statistical sources. It represents an example of generative social science that fuses plausible behavior with formalized logics matched against empirical evidence, essential in showing how novel patterns of urbanization such as desakota emerge.
 
Here are some sample simulations:
* Land use types at 1990, 1995 and 2000 and firstdifferences associated with modeling urban change.
* Predicted land use and changes in land use 1990–2000.
* An animation of urban development through the micro-time periods 1991 to 2010.