Transition Rule Elicitation Methods for Urban Cellular Automata Models

Jiao, Junfeng; Boerboom, L.G.J.

doi:10.1007/978-1-4020-5060-2_4

Cited by 8 publications

(9 citation statements)

References 15 publications

(11 reference statements)

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“…The transition rules could not include conflict-resolving rules in the model (Jiao and Boerboom 2006). It was confirmed, as previously demonstrated by (Twumasi 2008) that CA examines the synchronous dynamics of urban environment, in essence all cells update simultaneously at each iterative step, which means the cells were simply changed to the function to which they have the highest potential in terms of the factors modelled.…”

Section: Conclusion and Limitationssupporting

confidence: 53%

Land use scenarios and projections simulation using an integrated GIS cellular automata algorithms

Gharbia¹,

Alfatah²,

Gill³

et al. 2016

Model. Earth Syst. Environ.

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Over the years, urban growth models have proven to be effective in describing and estimating urban development and have consequently proven to be valuable for informed urban planning decision. Therefore, this paper investigates the implementation of an urban growth Cellular automata (CA) model using a GIS platform as a support tool for city planners, economists, urban ecologists and resource managers to help them establish decision making strategies and planning towards urban sustainable development. The area used as a test case is the River Shannon Basin in Ireland. This paper investigates the spatio-temporally varying effects of urbanization using a combined method of CA and GIS rasterization. The results generated from Cellular automata model indicated that the historical urban growth patterns in the River Shannon Basin area, in considerable part, be affected by distance to district centres, distance to roads, slope, neighbourhood effect, population density, and environmental factors with relatively high levels of explanation of the spatial variability. The optimal factors and the relative importance of the driving factors varied over time, thus, providing a valuable insight into the urban growth process. The developed model for Shannon catchment has been calibrated, validated, and used for predicting the future land use scenarios for the future time intervals 2020, 2050 and 2080. By involving natural and socioeconomic variables, the developed Cellular automata (CA) model had proved to be able to reproduce the historical urban growth process and assess the consequence of future urban growth. This paper presented as a novel application to the integrated CA-GIS model using a complicated land use dynamic system for Shannon catchment. The major conclusion from this paper was that land use simulation and projection without GIS rasterization formats cannot perform a multi-class, multi factors analysis which makes high accuracy simulation is impossible.

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Section: Conclusion and Limitationssupporting

confidence: 53%

Land use scenarios and projections simulation using an integrated GIS cellular automata algorithms

Gharbia¹,

Alfatah²,

Gill³

et al. 2016

Model. Earth Syst. Environ.

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show abstract

“…Constraints, expressed as Boolean images, in this context are used to constrain or limit the extent of development. Factors used in MCE account for suitability, accessibility, and neighborhood effects (Jiao and Boerboom, 2006). Local field characteristics such as soil properties, slope and elevation are used to determine and assign physical suitability scores.…”

Section: Modeling Approachmentioning

confidence: 99%

A cellular automata model of land cover change to integrate urban growth with open space conservation

Mitsova

Shuster

Wang

2011

Landscape and Urban Planning

286

109

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“…There are several advantages of integrating MCDA into GIS-based CA (Wu 1998;Jiao and Boerboom 2006;Yu et al 2011;Lai et al 2013;Cao et al 2014). The multicriteria approaches improve the procedures for calibrating CA parameters (Cao et al 2014) and providing behaviour-driven transition rules, as opposed to the traditional data-driven methods such as multiple regression analysis and principal components analysis (see Liu 2009).…”

Section: Cellular Automatamentioning

confidence: 99%

“…The multicriteria approaches improve the procedures for calibrating CA parameters (Cao et al 2014) and providing behaviour-driven transition rules, as opposed to the traditional data-driven methods such as multiple regression analysis and principal components analysis (see Liu 2009). This allows for a more realistic definition of transition rules in CA by taking into consideration the characteristics of the decision making process (Jiao and Boerboom 2006). The GIS-MCDA approach, integrated with CA, provides an effective and efficient tool for generating different planning scenarios and performing a what-if type of analysis.…”

Section: Cellular Automatamentioning

confidence: 99%

GIS-MCDA for Group Decision Making

Malczewski

Rinner

2015

Advances in Geographic Information Science

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Transition Rule Elicitation Methods for Urban Cellular Automata Models

Abstract: Abstract:Artificial Neural network (ANN), Visual calibration, and Analytical Hierarchy rules in urban CA models.

Cited by 8 publications

References 15 publications

Land use scenarios and projections simulation using an integrated GIS cellular automata algorithms

Land use scenarios and projections simulation using an integrated GIS cellular automata algorithms

A cellular automata model of land cover change to integrate urban growth with open space conservation

GIS-MCDA for Group Decision Making

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