Cellular automata models for the simulation of real-world urban processes: A review and analysis

Santé, Inés; García, A.; Miranda, David; Crecente, Rafael

doi:10.1016/j.landurbplan.2010.03.001

Cited by 615 publications

(337 citation statements)

References 102 publications

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“…Calibration can be understood as adjusting the parameters to improve the model's goodness of fit (Rykiel Jr., 1996;Petrov et al, 2009;van Vliet et al, 2011). In the case of the model employed here, the aim of calibration was to obtain the most suitable values for the transition rule parameters in order to reproduce the land use change processes that had occurred in the past (Santé et al, 2010).…”

Section: Implementing Neighbourhood Effect Functions In the Simulatiomentioning

confidence: 99%

“…Cellular automata (CA) have become one of the most widely used modelling frameworks in recent decades and have been employed to simulate phenomena such as land use change or urban growth (Barredo et al, 2003;Batty, 2007;Santé et al, 2010;Triantakonstantis and Mountrakis, 2012). Their success can be attributed to their capacity to reproduce the complex behaviour of dynamic systems such as cities, including aspects such as emergence, self-organisation, selfsimilarity and non-linear behaviour (Portugali 2000).…”

Section: Introductionmentioning

confidence: 99%

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Configuring the neighbourhood effect in irregular cellular automata based models

González

Barros

2016

International Journal of Geographical Information Science

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Abstract:Cellular automata (CA) models have been widely employed to simulate urban growth and land use change. In order to represent urban space more realistically, new approaches to CA models have explored the use of vector data instead of traditional regular grids. However, the use of irregular CA-based models brings new challenges as well as opportunities. The most strongly affected factor when using an irregular space is neighbourhood. Although neighbourhood definition in an irregular environment has been reported in the literature, the question of how to model the neighbourhood effect remains largely unexplored.In order to shed light on this question, this paper proposed the use of spatial metrics to characterise and measure the neighbourhood effect in irregular CA-based models. These metrics, originally developed for raster environments, namely the enrichment factor and the neighbourhood index, were adapted and applied in the irregular space employed by the model. Using the results of these metrics, distance-decay functions were calculated to reproduce the push-and-pull effect between the simulated land uses. The outcomes of a total of 55 simulations (five sets of different distance functions and eleven different neighbourhood definition distances) were compared with observed changes in the study area during the calibration period. Our results demonstrate that the proposed methodology improves the outcomes of the urban growth simulation model tested and could be applied to other irregular CA-based models.

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Section: Implementing Neighbourhood Effect Functions In the Simulatiomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Configuring the neighbourhood effect in irregular cellular automata based models

González

Barros

2016

International Journal of Geographical Information Science

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“…First, CA models are advantageous over other counterparts due to their spatial explicitness, flexible transitional rules, compatibility with large data sets (Wagner, 1997), and easy integration with ArcGIS ® (Santé et al, 2010). Second, SLEUTH only relies on five inputs: urban, transportation, slope, hillshade, and exclusion.…”

Section: Rationale For Model Selectionmentioning

confidence: 99%

“…Numerous software packages have developed since the introduction of CA. Santé et al (2010) evaluated 33 CA models and concluded that the SLEUTH gained more popularity than the other alternatives. The SLEUTH has been adopted by urban researchers all over the world since 2000 (Project Gigalopolis, 2016).…”

Section: Introductionmentioning

confidence: 99%

An examination of land use impacts of flooding induced by sea level rise

Song¹,

Fu²,

Gu³

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. Coastal regions become unprecedentedly vulnerable to coastal hazards that are associated with sea level rise. The purpose of this paper is therefore to simulate prospective urban exposure to changing sea levels. This article first applied the cellular-automaton-based SLEUTH model (Project Gigalopolis, 2016) to calibrate historical urban dynamics in Bay County, Florida (USA) -a region that is greatly threatened by rising sea levels. This paper estimated five urban growth parameters by multiple-calibration procedures that used different Monte Carlo iterations to account for modeling uncertainties. It then employed the calibrated model to predict three scenarios of urban growth up to 2080 -historical trend, urban sprawl, and compact development. We also assessed land use impacts of four policies: no regulations; flood mitigation plans based on the whole study region and on those areas that are prone to experience growth; and the protection of conservational lands. This study lastly overlaid projected urban areas in 2030 and 2080 with 500-year flooding maps that were developed under 0, 0.2, and 0.9 m sea level rise. The calibration results that a substantial number of built-up regions extend from established coastal settlements. The predictions suggest that total flooded area of new urbanized regions in 2080 would be more than 25 times that under the flood mitigation policy, if the urbanization progresses with few policy interventions. The joint model generates new knowledge in the domain between land use modeling and sea level rise. It contributes to coastal spatial planning by helping develop hazard mitigation schemes and can be employed in other international communities that face combined pressure of urban growth and climate change.

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“…Typically, a CA model consists of four components: suitability surfaces, neighborhood configuration, simulation time, and stochastic perturbation (Yeh and Li 2006;Santé et al, 2010). They work in a collective way to form decisions on cell states.…”

Section: Introductionmentioning

confidence: 99%

Integrating remote sensing, GIS and dynamic models for landscape-level simulation of forest insect disturbance

Huang

et al. 2017

Ecological Modelling

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Huabing, "Integrating remote sensing, GIS and dynamic models for landscape-level simulation of forest insect disturbance" (2017 Cellular automata (CA) is a powerful tool for modeling the evolution of macroscopic scale phenomena as it couples time, space, and variables together while remaining in a simplified form. However, such application has remained challenging in forest insect epidemics due to the highly dynamic nature of insect behavior. Recent advances in temporal trajectory-based image analysis offer an alternative way to obtain high-frequency model calibration data. In this study, we propose an insect-CA modeling framework that integrates cellular automata, remote sensing, and Geographic Information System to understand the insect ecological processes, and tested it with measured data of mountain pine beetle (MPB) in the Rocky Mountains. The overall accuracy of the predicted MPB mortality pattern in the test years ranged from 88% to 94%, which illuminates its effectiveness in modeling forest insect dynamics. We further conducted sensitivity analysis to examine responses of model performance to various parameter settings. In our case, the ensemble random forest algorithm outperforms the traditional linear regression in constructing the suitability surface. Small neighborhood size is more effective in simulating the MPB movement behavior, indicating that short-distance is the dominating dispersal mode of MPB. The introduction of a stochastic perturbation component did not improve the model performance after testing a broad range of randomness degree, reflecting a relative compact dispersal pattern rather than isolated outbreaks. We conclude that CA with remote sensing observation is useful for landscape insect movement analyses; however, consideration of several key parameters is critical in the modeling process and should be more thoroughly investigated in future work.

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Cellular automata models for the simulation of real-world urban processes: A review and analysis

Cited by 615 publications

References 102 publications

Configuring the neighbourhood effect in irregular cellular automata based models

Configuring the neighbourhood effect in irregular cellular automata based models

An examination of land use impacts of flooding induced by sea level rise

Integrating remote sensing, GIS and dynamic models for landscape-level simulation of forest insect disturbance

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