Sage Journals: Discover world-class research

Abstract

Cellular automata have found extensive applications in the modelling of urban systems. Calculations in cellular automata models are based on cell centroids and therefore cellular automata models are sensitive to the choices of cell size and shape. While the effect of cell size for urban simulation has been studied, discussions on the effect of cell shape on urban cellular automata models have been limited. Applications in other fields suggest there are advantages of using hexagonal cells over square cells, yet most urban cellular automata models use square cells. Using connectivity indices from graph theory, experiments in this study compared models based on hexagonal and square cells to examine the potential advantage of hexagonal cells in urban cellular automata models. This paper finds that simulation results from the model with hexagonal cells are more consistent and concludes that hexagonal cells would increase the robustness of model simulation.

Keywords

Cellular automata urban simulation hexagonal cells graph theory

Get full access to this article

View all access options for this article.

References

Adamatzky

Wuensche

De Lacy Costello

(2006) Glider-based computing in reaction-diffusion hexagonal cellular automata. Chaos, Solitons & Fractals 27(2): 287–295.

Aljoufie

Zuidgeest

Brussel

, et al. (2013) A cellular automata-based land use and transport interaction model applied to Jeddah, Saudi Arabia. Landscape and Urban Planning 112: 89–99.

Batty

(1997) Cellular automata and urban form: A primer. Journal of the American Planning Association 63(2): 266–274.

Batty

(2007) Cities and Complexity. Cambridge: The MIT Press.

Batty

Couclelis

Eichen

(1997) Urban systems as cellular automata. Environment and Planning B: Planning and Design 24: 159–164.

Batty

Xie

(1994) From cells to cities. Environment and Planning B: Planning and Design 21: s31–s48.

Brown

Page

Riolo

, et al. (2005) Path dependency and the validation of agent-based spatial models of land use. International Journal of Geographical Information Science 19(2): 153–174.

Bura

Guérin-Pace

Mathian

, et al. (1996) Multiagent systems and the dynamics of a settlement system. Geographical Analysis 28(2): 161–178.

Couclelis

(1985) Cellular worlds: A framework for modeling micro–macro dynamics. Environment and Planning A: Economy and Space 17: 585–596.

10.

Couclelis

(1997) From cellular automata to urban models: New principles for model development and implementation. Environment and Planning B: Planning and Design 24: 165–174.

11.

D’Ambrosio

Di Gregorio

Iovine

(2003) Simulating debris flows through a hexagonal cellular automata model: SCIDDICA S_3-hex. Natural Hazards and Earth System Science 3: 545–559.

12.

Horowitz

(2004) Lowry-type land use models. In: Hensher DA, Button KJ, Haynes KE, et al. (eds) Handbook of Transport Geography and Spatial Systems (Handbooks in Transport, Vol. 5). Bingley: Emerald Group Publishing Limited, pp.167–183.

13.

Iovine

D’Ambrosio

Di Gregorio

(2005) Applying genetic algorithms for calibrating a hexagonal cellular automata model for the simulation of debris flows characterised by strong inertial effects. Geomorphology 66: 287–303.

14.

Ménard

Marceau

(2005) Exploration of spatial scale sensitivity in geographic cellular automata. Environment and Planning B: Planning and Design 32: 693–714.

15.

RIKS (2010) Metronamica Documentation. Maastricht: RIKS BV.

16.

Samat

(2006) Characterizing the scale sensitivity of the cellular automata simulated urban growth: A case study of the Seberang Perai Region, Penang State, Malaysia. Computer, Environment, and Urban Systems 30: 906–920.

17.

Sanders

Pumain

Mathian

, et al. (1997) SIMPOP: A multiagent system for the study of urbanism. Environment and Planning B: Planning and Design 24: 287–305.

18.

Santé

García

Miranda

, et al. (2010) Cellular automata models for the simulation of real-world urban processes: A review and analysis. Landscape and Urban Planning 96: 108–122.

19.

Torrens

Benenson

(2005) Geographic automata systems. International Journal of Geographical Information Science 19(4): 385–412.

20.

Torrens

O’Sullivan

(2001) Cellular automaton and urban simulation: Where do we go from here? Environment and Planning B: Planning and Design 28: 163–168.

21.

Trunfio

(2004) Predicting wildfire spreading through a hexagonal cellular automata model. In: Sloot

PMA

Chopard

Hoekstra

(eds) Cellular Automata. ACRI 2004. Lecture Notes in Computer Science. Vol. 3305. Berlin, Heidelberg: Springer.

22.

Van Vliet

Hurkens

White

, et al. (2012) An activity based cellular automaton model to simulate land-use changes. Environment and Planning B: Planning and Design 39: 198–212.

23.

White

Engelen

(1993) Cellular automata and fractal urban form: A cellular modelling approach to the evolution of urban land use patterns. Environment and Planning A: Economy and Space 25: 1175–1199.

On the consistency of urban cellular automata models based on hexagonal and square cells

Abstract

Keywords

Get full access to this article

References