A Linguistic Approach to Model Urban Growth

Abstract

This paper presents a linguistic approach for modeling urban growth. The authors developed a methodological framework which utilizes Fuzzy Set theory to capture and describe the effect of urban features on urban growth and applies Cellular Automata techniques to simulate urban growth. Although several approaches exist that combine Fuzzy Logic and Cellular Automata for urban growth modeling, the authors focused on the ability to use partial knowledge and combine theory-driven and data driven knowledge. To achieve this, a parallel connection between the input variables is introduced which further allows the model to disengage from severe data limitations. In this approach, a number of parallel fuzzy systems are used, each one of which focuses on different types of urban growth factors, different drivers or restrictions of development. The effects of all factors under consideration are merged into a single internal thematic layer that maps the suitability for urbanization for each area, providing thus an information flow familiar to the human conceptualization of the phenomenon. Following, cellular automata techniques are used to simulate urban growth. The proposed methodology is applied in the Mesogeia area in the Attica basin (Athens) for the period 1990-2004 and provides realistic estimations for urban growth.