Parameter Sensitivity Analyses in Agent-Based Urban Growth Models
Land use activity is a major issue and challenge for town and country planners. Modelling and managing urban growth is a complex problem. Cities are now recognised as complex, non-linear and dynamic process systems. The design of a system that can handle these complexities is a challenging prospect. Local governments that implement urban growth models need to estimate the amount of urban land required in the future given anticipated growth of housing, business, recreation and other urban uses within the boundary. There are so many negative implications related to the type of inappropriate urban development, such as increased trafﬁc and demand for mobility, reduced landscape attractiveness, land use fragmentation, loss of biodiversity and alterations of the hydrological cycle. The aim of this study was to use an agent-based model as a powerful tool for simulating urban growth patterns. Our study area was Sanandaj city located in the west of Iran. Landsat imageries acquired in 2000 and 2006 were used. The dataset used included distance to principle roads, distance to residential areas, elevation, slope, distance to green spaces and distance to region centres, land price and distance to fault. In this study, an appropriate methodology for urban growth modelling using satellite remotely sensed data was presented and evaluated. Percent correct match (PCM), figure of merit and kappa statistics were used to evaluate the simulation results.