Modeling urban dynamics through GIS-based cellular automata

Abstract

In urban systems modeling, there are many elaborate dynamic models based on intricate decision processes whose simulation must be based on customized software if their space±time properties are to be explored eectively. In this paper we present a class of urban models whose dynamics are based on theories of development associated with cellular automata (CA), whose data is ®ne-grained, and whose simulation requires software which can handle an enormous array of spatial and temporal model outputs. We ®rst introduce the generic problem of modeling within GIS, noting relevant CA models before outlining a generalized model based on Xie's (1996, A general model for cellular urban dynamics. Geographical Analysis, 28, 350±373)``dynamic urban evolutionary modeling'' (DUEM) approach. We present ways in which land uses are structured through their life cycles, and ways in which existing urban activities spawn locations for new activities. We de®ne various decision rules that embed distance and direction, density thresholds, and transition or mutation probabilities into the model's dynamics, and we then outline the software designed to generate eective urban simulations consistent with GIS data inputs, outputs and related functionality. Finally, we present a range of hypothetical urban simulations that illustrate the diversity of model types that can be handled within the framework as a prelude to more realistic applications which will be reported in later papers. # 1999 Published by Elsevier Science Ltd. All rights reserved.

A highly detailed land-use vector map for Madrid region based on photo-interpretation

“…MLU can also be used as a basis for dynamic models of urban LULC change such as cellular automata models (Barredo, Demicheli, Lavalle, Kasanko, & McCormick, 2004;Batty, Xie, & Sun, 1999;White & Engelen, 1993), or agent-based models (Brown, Page, Riolo, Zellner, & Rand, 2005;Veldkamp & Verburg, 2004). Future work is likely to include the incorporation of LULC information for new neighboring regions that are usually considered to fall under the influence of the Madrid metropolitan region, such as Toledo, Segovia and Guadalajara.…”

Urban sprawl assessment and modeling using landsat images and GIS

“…This method is also, unable to recognize non-linear relations. And finally, cellular automata is the most popular method in urban growth modeling (Triantakonstantis and Mountrakis 2012) which has been used by (Batty et al 1999;Liu 2009;Maithani 2010;Foroutan and Delavar 2012). Finding the proper transferring rule is the most important disadvantage of cellular automata method.…”

Urban Simulation Using Neural Networks and Cellular Automata for Land Use Planning

“…In general CA models are to estimate the conversion probability between states. For example, a simple urban CA model can be expressed by the following neighbourhood-based transition rules (Batty, 1999): and Where is the urban development probability for cell and cell is the set of all the cells which are from the Moore neighbourhood including the cell itself. The probability of a cell being developed is decided by the number of already developed cells in the neighbourhood.…”

“…More factors have been incorporated in CA models to improve simulation performance for either hypothetical or realistic applications. Distance, direction, density thresholds, and transition or mutation probabilities have been included in the transition rules of various CA models (Batty and Xie, 1994;1999). Various types of constraints based on site features can also be used to regulate development patterns for land-use planning (Li and Yeh, 2000;Yeh and Li, 2001a).…”

“…Recently, a variety of urban CA models have been developed to simulate either artificial or realistic cities (Batty and Xie, 1994;Clarke et al, 1997;Li and Yen, 2000;White and Engelen, 1993;Wu, 1998;Wu and Webster, 1998). Cities, like most geographical phenomena phenomena, are complex nonlinear systems involving spatial and sect oral interactions which cannot easily be modeled with the functionalities of current GIS software (Batty et al, 1999).CA-based approaches are useful in the study of urban and regional spatial structure and evolution..A critical issue in CA simulation is the provision of proper parameter values or weights so that realistic results can be generated. Real cities are complex dynamic systems that require the use of many spatial variables in CA simulation.…”