Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change

Hao Wu, Zhen Li, Keith C. Clarke, Wenzhong Shi, Linchuan Fang, Anqi Lin, Jie Zhou

Research output: Journal article publicationJournal articleAcademic researchpeer-review

102 Citations (Scopus)


Understanding the spatial scale sensitivity of cellular automata is crucial for improving the accuracy of land use change simulation. We propose a framework based on a response surface method to comprehensively explore spatial scale sensitivity of the cellular automata Markov chain (CA-Markov) model, and present a hybrid evaluation model for expressing simulation accuracy that merges the strengths of the Kappa coefficient and of Contagion index. Three Landsat-Thematic Mapper remote sensing images of Wuhan in 1987, 1996, and 2005 were used to extract land use information. The results demonstrate that the spatial scale sensitivity of the CA-Markov model resulting from individual components and their combinations are both worthy of attention. The utility of our proposed hybrid evaluation model and response surface method to investigate the sensitivity has proven to be more accurate than the single Kappa coefficient method and more efficient than traditional methods. The findings also show that the CA-Markov model is more sensitive to neighborhood size than to cell size or neighborhood type considering individual component effects. Particularly, the bilateral and trilateral interactions between neighborhood and cell size result in a more remarkable scale effect than that of a single cell size.

Original languageEnglish
Pages (from-to)1040-1061
Number of pages22
JournalInternational Journal of Geographical Information Science
Issue number5
Publication statusPublished - 4 May 2019


  • cellular automata
  • Land use change simulation
  • Markov chain
  • response surface method
  • spatial scale sensitivity

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development
  • Library and Information Sciences


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