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The Use of Particle Swarm Optimization for a Vector Cellular Automata Model of Land Use Change (Short Paper)

Authors Yi Lu , Shawn Laffan

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Subject Classification

ACM Subject Classification
  • Computing methodologies → Modeling methodologies
Keywords
  • Vector cellular automata (CA)
  • Particle swarm optimization (PSO)
  • Land use simulation
  • Ipswich

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Abstract

Cellular automata (CA) is an important area of research in GIScience, with recent research developing vector-based models in addition to the traditional raster data formats. One active area of research is the calibration of transition rules, particularly when applied to vector CA. Here we evaluate a particle swarm optimization (PSO) process to calibrate a vector CA model of land use change for a sub-region of Ipswich in Queensland, Australia, for the period 1999-2016. We compare the results with those for a raster CA of the same dataset. The spatial indices of the vector PSO-CA model exceed that of the raster model, with spatial accuracies being 82.45% and 76.47%, respectively. In addition, the vector PSO-CA model achieved a higher kappa coefficient. Vector-based PSO-CA model can be used for the exploration of urbanization process and provide a better understanding of land use change.

Cite As Get BibTex

Yi Lu and Shawn Laffan. The Use of Particle Swarm Optimization for a Vector Cellular Automata Model of Land Use Change (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 42:1-42:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018) https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.42

Author Details

Yi Lu
  • University of New South Wales, Sydney, Australia
Shawn Laffan
  • University of New South Wales, Sydney, Australia

References

  1. Robert Barlovic, Ludger Santen, Andreas Schadschneider, and Michael Schreckenberg. Metastable states in cellular automata for traffic flow. The European Physical Journal B-Condensed Matter and Complex Systems, 5(3):793-800, 1998. Google Scholar
  2. Michael Batty and Yichun Xie. From cells to cities. Environment and Planning B: Planning and Design, 21(7):S31-S48, 1994. Google Scholar
  3. Min Cao, Guo’an Tang, Quanfei Shen, and Yanxia Wang. A new discovery of transition rules for cellular automata by using cuckoo search algorithm. International Journal of Geographical Information Science, 29(5):806-824, 2015. Google Scholar
  4. Jacob Cohen. Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. Psychological Bulletin, 70(4):213, 1968. Google Scholar
  5. Eric de Noronha Vaz, Peter Nijkamp, Marco Painho, and Mário Caetano. A multi-scenario forecast of urban change: A study on urban growth in the Algarve. Landscape and Urban Planning, 104(2):201-211, 2012. Google Scholar
  6. Ruth J Doran and Shawn W Laffan. Simulating the spatial dynamics of foot and mouth disease outbreaks in feral pigs and livestock in Queensland, Australia, using a susceptible-infected-recovered cellular automata model. Preventive Veterinary Medicine, 70(1-2):133-152, 2005. Google Scholar
  7. Russell Eberhart and James Kennedy. A new optimizer using particle swarm theory. In Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on, pages 39-43. IEEE, 1995. Google Scholar
  8. L Hernández Encinas, S Hoya White, A Martín Del Rey, and G Rodríguez Sánchez. Modelling forest fire spread using hexagonal cellular automata. Applied Mathematical Modelling, 31(6):1213-1227, 2007. Google Scholar
  9. Yongjiu Feng and Yan Liu. A cellular automata model based on nonlinear kernel principal component analysis for urban growth simulation. Environment and Planning B: Planning and Design, 40(1):117-134, 2013. Google Scholar
  10. Yongjiu Feng, Yan Liu, Xiaohua Tong, Miaolong Liu, and Susu Deng. Modeling dynamic urban growth using cellular automata and particle swarm optimization rules. Landscape and Urban Planning, 102(3):188-196, 2011. Google Scholar
  11. Queensland Government. South East Queensland Regional Plan 2009–2031, June 2009. Google Scholar
  12. Richard E Klosterman. The what if? Collaborative planning support system. Environment and Planning B: Planning and Design, 26(3):393-408, 1999. Google Scholar
  13. Verda Kocabas and Suzana Dragicevic. Assessing cellular automata model behaviour using a sensitivity analysis approach. Computers, Environment and Urban Systems, 30(6):921-953, 2006. Google Scholar
  14. Tiebei Li, Jonathan Corcoran, David Pullar, Alistair Robson, and Robert Stimson. A geographically weighted regression method to spatially disaggregate regional employment forecasts for South East Queensland. Applied Spatial Analysis and Policy, 2(2):147-175, 2009. Google Scholar
  15. Xia Li and Anthony Gar-On Yeh. Neural-network-based cellular automata for simulating multiple land use changes using gis. International Journal of Geographical Information Science, 16(4):323-343, 2002. Google Scholar
  16. Xiaoping Liu, Xia Li, Lin Liu, Jinqiang He, and Bin Ai. A bottom-up approach to discover transition rules of cellular automata using ant intelligence. International Journal of Geographical Information Science, 22(11-12):1247-1269, 2008. Google Scholar
  17. Yan Liu, Yongjiu Feng, and Robert Gilmore Pontius. Spatially-explicit simulation of urban growth through self-adaptive genetic algorithm and cellular automata modelling. Land, 3(3):719-738, 2014. Google Scholar
  18. Niandry Moreno, Fang Wang, and Danielle J Marceau. Implementation of a dynamic neighborhood in a land-use vector-based cellular automata model. Computers, Environment and Urban Systems, 33(1):44-54, 2009. Google Scholar
  19. Yuhui Shi and Russell Eberhart. A modified particle swarm optimizer. In Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on, pages 69-73. IEEE, 1998. Google Scholar
  20. Fang Wang, Jean-Gabriel Hasbani, Xin Wang, and Danielle J Marceau. Identifying dominant factors for the calibration of a land-use cellular automata model using rough set theory. Computers, Environment and Urban Systems, 35(2):116-125, 2011. Google Scholar
  21. Fang Wang and Danielle J Marceau. A patch-based cellular automaton for simulating land-use changes at fine spatial resolution. Transactions in GIS, 17(6):828-846, 2013. Google Scholar
  22. Douglas Ward, Stuart R Phinn, and Alan T Murray. Monitoring growth in rapidly urbanizing areas using remotely sensed data. The Professional Geographer, 52(3):371-386, 2000. Google Scholar
  23. Yongke Yang, Pengfeng Xiao, Xuezhi Feng, and Haixing Li. Accuracy assessment of seven global land cover datasets over China. ISPRS Journal of Photogrammetry and Remote Sensing, 125:156-173, 2017. Google Scholar
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