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Advances in Artificial Intelligence
Volume 2012 (2012), Article ID 964148, 11 pages
http://dx.doi.org/10.1155/2012/964148
Research Article

Simulation of Land-Use Development, Using a Risk-Regarding Agent-Based Model

1Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, ValiAsr Street, Mirdamad Cross, Tehran 19967-15433, Iran
2School of Urban Planning, University of Tehran, Enghelab Avenue,Tehran 14155-6135, Iran

Received 31 May 2012; Revised 6 October 2012; Accepted 6 October 2012

Academic Editor: Joanna Józefowska

Copyright © 2012 F. Hosseinali et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The aim of this paper is to study the spatial consequences of applying different Attitude Utility Functions (AUFs), which reflect peoples’ simplified psychological frames, to investment plans in land-use decision making. For this purpose, we considered and implemented an agent-based model with new methods for searching landscapes, for selecting parcels to develop, and for allowing competitions among agents. Besides this, GIS (Geographic Information Systems) as a versatile and powerful medium of analyzing and representing spatial data is used. Our model is implemented on an artificial landscape in which land is being developed by agents. The agents are assumed to be mobile developers that are equipped with several land-related objectives. In this paper, agents mimic various risk-bearing attitudes and sometimes compete for developing the same parcel. The results reveal that patterns of land-use development are different in the two cases of regarding and disregarding AUFs. Therefore, it is considered here that using the attitudes of people towards risk helps the model to better simulate the decision making of land-use developers. The different attitudes toward risk used in this study can be attributed to different categories of developers based on sets of characteristics such as income, age, or education.