Simulating Emergent Urban Form Using Agent-Based Modeling: Desakota in the Suzhou-Wuxian Region in China

Abstract

We propose that the emergent phenomenon know as “desakota,” the rapid urbanization of densely populated rural populations in the newly developed world, particularly China, can be simulated using agent-based models that combine bottom-up actions with global interactions. We argue that desakota represents a surprising and unusual form of urbanization well-matched to processes of land development that are locally determined but moderated by the higher-level macroeconomy. We develop a simple logic that links local household reform to global urban reform, translating these ideas into a model structure that reflects these two scales. 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 for the period 1990 to 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 is implemented in the RePast 3 software and 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.