Simulating urban growth under planning policies through parcel-based cellular automata (ParCA) model

In recent decades, the cellular automata model, among the urban development prediction models, has been applied considerably. Studies show that the output of conventional cellular automata models is sensitive to cell size and neighborhood structure, and varies with changes in the size of these parameters. To solve this problem, vector-based cellular automata models have been introduced which have overcome the mentioned limitations and presented better results. The aim of this study was to present a parcel-based cellular automata (ParCA) model for simulating urban growth under planning policies. In this model, undeveloped areas are first subdivided into smaller parcels, based on some geometric parameters; then, neighborhood effect of parcels is defined in a radial structure, based on a weighted function of distance, area, land-use, and service level of irregular cadastral parcels. After that, neighborhood effect is evaluated using three components, including compactness, dependency and compatibility. The presented model was implemented and analyzed using data from municipal region 22 of Tehran. The obtained results indicated the high ability of ParCA model in allocating various land-uses to parcels in the appropriateness of the layout of different land-uses. This model can be used in decision-making and urban land-use planning activities, since it provides the possibility of allocating different urban land-use types and assessing different urban-growth scenarios.     

Assessing the effect of temporal dynamics on urban growth simulation: Towards an asynchronous cellular automata

Time is a fundamental dimension in urban dynamics, but the effect of various definitions of time on urban growth models has rarely been evaluated. In urban growth models such as cellular automata (CA), time has typically been defined as a sequence of discrete time steps. However, most urban growth processes such as land‐use changes are asynchronous. The aim of this study is to examine the effect of various temporal dynamics scenarios on urban growth simulation, in terms of urban land‐use planning, and to introduce an asynchronous parcel‐based cellular automata (AParCA) model. In this study, eight different scenarios were generated to investigate the impact of temporal dynamics on CA‐based urban growth models, and their outputs were evaluated using various urban planning indicators. The obtained results show that different degrees of temporal dynamics lead to various patterns appearing in urban growth CA models, and the application of asynchronous (event‐driven) CA models achieves better simulation results than synchronous models.