Urban growth models and calibration methods: a case study of Athens, Greece

Abstract

A number of urban growth models have been developed to simulate and predict urban expansion. Most of these models have common objectives; however, they differ in terms of calibration and execution methodologies. GIS spatial computations and data processing capabilities have given us the ability to draw more effective simulation results for increasingly complex scenarios. In this paper, we apply and evaluate a methodology to create a hybrid cellular-automaton-(CA) and agent-based model (ABM) using raster and vector data from the Urban Atlas project as well as other open data sources. We also present and evaluate three different methods to calibrate and evaluate the model. The model has been applied and evaluated by a case study on the city of Athens, Greece. However, it has been designed and developed with the aim of being applicable to any city available in the Urban Atlas project.