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Simulating urban growth under planning policies through parcel-based cellular automata (ParCA) model.

Authors :
Abolhasani, Somaie
Taleai, Mohammad
Karimi, Mohammad
Rezaee Node, Adel
Source :
International Journal of Geographical Information Science. Nov2016, Vol. 30 Issue 11, p2276-2301. 26p.
Publication Year :
2016

Abstract

In recent decades, the cellular automata model, among the urban development prediction models, has been applied considerably. Studies show that the output of conventional cellular automata models is sensitive to cell size and neighborhood structure, and varies with changes in the size of these parameters. To solve this problem, vector-based cellular automata models have been introduced which have overcome the mentioned limitations and presented better results. The aim of this study was to present a parcel-based cellular automata (ParCA) model for simulating urban growth under planning policies. In this model, undeveloped areas are first subdivided into smaller parcels, based on some geometric parameters; then, neighborhood effect of parcels is defined in a radial structure, based on a weighted function of distance, area, land-use, and service level of irregular cadastral parcels. After that, neighborhood effect is evaluated using three components, including compactness, dependency and compatibility. The presented model was implemented and analyzed using data from municipal region 22 of Tehran. The obtained results indicated the high ability of ParCA model in allocating various land-uses to parcels in the appropriateness of the layout of different land-uses. This model can be used in decision-making and urban land-use planning activities, since it provides the possibility of allocating different urban land-use types and assessing different urban-growth scenarios. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
13658816
Volume :
30
Issue :
11
Database :
Academic Search Index
Journal :
International Journal of Geographical Information Science
Publication Type :
Academic Journal
Accession number :
118862584
Full Text :
https://doi.org/10.1080/13658816.2016.1184271