A Linguistic Approach to Model Urban Growth

A Linguistic Approach to Model Urban Growth

Lefteris Mantelas (Institute of Applied and Computational Mathematics, Foundation for Research and Technology-Hellas, Greece), Poulicos Prastacos (Institute of Applied and Computational Mathematics, Foundation for Research and Technology-Hellas, Greece), Thomas Hatzichristos (National Technical University of Athens, Greece) and Kostis Koutsopoulos (National Technical University of Athens, Greece)
DOI: 10.4018/jaeis.2012070103
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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.
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The urban transition can be seen as the passage from a predominantly rural to a predominantly urban society (Marshall, 2007) which takes place by the expansion of existing urban areas and the development of new cities. People accumulate in urban areas in their attempt to gain better access to goods, services, facilities and job opportunities. As a result, financial, social and cultural activities flourish in large urban areas attracting thus more people to live, work, produce and consume within the urban environment. In 2007, 50% of the global population was living in town and cities while it is estimated that 60 million people move to cities in a yearly basis. What is more, these rates are expected to be preserved for the next 30 years (Marshall, 2007).

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 decision-making 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.

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