A linguistic approach to model urban growth

Mantelas, L; Prastacos, P; Hatzichristos, T; Koutsopoulos, K

dc.contributor.author	Mantelas, L	en
dc.contributor.author	Prastacos, P	en
dc.contributor.author	Hatzichristos, T	en
dc.contributor.author	Koutsopoulos, K	en
dc.date.accessioned	2014-03-01T02:07:19Z
dc.date.available	2014-03-01T02:07:19Z
dc.date.issued	2012	en
dc.identifier.issn	19473192	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/29541
dc.subject	Cellular Automata	en
dc.subject	Fuzzy Logic	en
dc.subject	Parallel Connection of Partial Knowledge	en
dc.subject	Urban Growth	en
dc.subject	Urban Growth Modeling	en
dc.subject.other	Data driven	en
dc.subject.other	Data limitations	en
dc.subject.other	Growth factor	en
dc.subject.other	Growth modeling	en
dc.subject.other	Input variables	en
dc.subject.other	Linguistic approach	en
dc.subject.other	Methodological frameworks	en
dc.subject.other	Parallel connections	en
dc.subject.other	Partial knowledge	en
dc.subject.other	Thematic layers	en
dc.subject.other	Urban features	en
dc.subject.other	Cellular automata	en
dc.subject.other	Electric connectors	en
dc.subject.other	Fuzzy logic	en
dc.subject.other	Fuzzy set theory	en
dc.subject.other	Linguistics	en
dc.subject.other	Urban growth	en
dc.subject.other	cellular automaton	en
dc.subject.other	fuzzy mathematics	en
dc.subject.other	mapping	en
dc.subject.other	model test	en
dc.subject.other	model validation	en
dc.subject.other	parallel computing	en
dc.subject.other	urban growth	en
dc.subject.other	urbanization	en
dc.subject.other	Attica	en
dc.subject.other	Greece	en
dc.title	A linguistic approach to model urban growth	en
heal.type	journalArticle	en
heal.identifier.primary	10.4018/jaeis.2012070103	en
heal.identifier.secondary	http://dx.doi.org/10.4018/jaeis.2012070103	en
heal.publicationDate	2012	en
heal.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 feature s 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 fow 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. Copyright © 2010, IGI Global.	en
heal.journalName	International Journal of Agricultural and Environmental Information Systems	en
dc.identifier.doi	10.4018/jaeis.2012070103	en
dc.identifier.volume	3	en
dc.identifier.issue	2	en
dc.identifier.spage	35	en
dc.identifier.epage	53	en