A Linguistic Approach to Model Urban Growth

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. DOI: 10.4018/jaeis.2012070103 36 International Journal of Agricultural and Environmental Information Systems, 3(2), 35-53, July-December 2012 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. The changes in financial and social activities within the city, the settlement of new population and the emergence of new activities lead to the reorganization of land use and the production of buildings and services’ networks in accordance to the population needs (Κομνηνός, 1986). What is more, changes are also driven by the increasing expectations of the urban population which are reflected by peoples’ residential choice. People’s expectations may refer to buildings’ attributes such as more floor-space and better quality of construction but are also referring to locational characteristics. People desire to live in areas that among others: • Provide accessibility to high speed road networks, parking areas and public transportation system. • Are in the vicinity of urban green areas and parks. • Provide access to goods, services and facilities. • Consist a healthy and safe environment. Apparently, seldom do the above criteria overlap and when they do they lead to high real estate values. In this respect, urban growth can be described as the spontaneous spatially referenced tradeoff between different types of human needs and expectations. As a result, monitoring and comprehending urban growth relies heavily on identifying the residential choice criteria and the factors that attract or repel new settlements. For this reason, fuzzy logic has a key role to play in the challenging field of urban modeling; a role whose importance stems from the fact that it mimics the ways in which humans make decisions in an environment of uncertainty and imprecision (Zadeh, 1993). CHALLENGES IN URBAN MODELING The term ‘modeling’ refers to creating a strictly defined analog of real world by subtraction (Κουτσόπουλος, 2002). Yet there is no rigorous framework for modeling such a spatio-temporal phenomenon as urban growth since there lies great inherent spatial, temporal and decisionmaking heterogeneity (Cheng & Masser, 2003), which results from socio-economic and ecological heterogeneity itself. Moreover there is something special regarding the spatio-temporal nature of the urban growth. Urban growth does not simply evolve in time; it also spreads in space and not always continuously. This means that apart from the difficulties of studying a spatial phenomenon, when studying urban growth we may come across first-seen qualitative phenomena and interactions, that cannot be modeled mathematically in an easy way. The problem seems to be that our knowledge, both theory-driven and data-driven, is not really describing urban growth dynamics in general, but instead the part of the urban growth dynamics that have already occurred and have been observed and experienced. What is more, knowledge about the operational scale(s) of urban form and process, and the interaction and parallelism among different scales, is poor (Dietzel & Clarke, 2004). We deal with a phenomenon which exists but it is also recreated in space, extending itself both continuously and discontinuously in space while evolving in time. Moreover, its dynamics evolve in time as well and all there is for modeling urban growth is our experience of the phenomenon itself, which might be inaccurate for describing its future evolution. On top of these, for an urban model to be useful it should be able to describe the objects, the relations and it’s assumptions in an open, visible and comprehensible way. This way, results and the underlying mechanisms can be challenged by experts (Ness & Low, 2000) and get improved. At the same time, an urban model should be able to be used both for forecasting and describing urban growth (Liu, 2009). This allows the model not only to estimate the future evolution of an urban system but also to unveil the underlying structure and procedures of the urban growth phenomenon. Apparently, the usability of the estimations and the knowledge provided by the model are as much important as the model’s consistency to the real world. 17 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the product's webpage: www.igi-global.com/article/linguistic-approach-model-urbangrowth/68008?camid=4v1 This title is available in InfoSci-Journals, InfoSci-Journal Disciplines Engineering, Natural, and Physical Science. Recommend this product to your librarian: www.igi-global.com/e-resources/libraryrecommendation/?id=2