A Markov Chain Model of Land Use Change

Authors

  • Michael Iacono Department of Civil, Environmental, and Geo-Engineering University of Minnesota (USA)
  • David Levinson Department of Civil, Environmental, and Geo-Engineering University of Minnesota
  • Ahmed El-Geneidy School of Urban Planning McGill University
  • Rania Wasfi Department of Geography McGill University

DOI:

https://doi.org/10.6092/1970-9870/2985

Keywords:

Land use, Twin Cities, Statistical models, Markov chain, State dependence.

Abstract

The set of models available to predict land use change in urban regions has become increasingly complex in recent years. Despite their complexity, the predictive power of these models remains relatively weak. This paper presents an example of an alternative modeling framework based on the concept of a Markov chain. The model assumes that land use at any given time, which is viewed as a discrete state, can be considered a function of only its previous state. The probability of transition between each pair of states is recorded as an element of a transition probability matrix.  Assuming that this matrix is stationary over time, it can be used to predict future land use distributions from current data. To illustrate this process, a Markov chain model is estimated for the Minneapolis-St. Paul, MN, USA (Twin Cities) metropolitan region.  Using a unique set of historical land use data covering several years between 1958 and 2005, the model is tested using historical data to predict recent conditions, and is then used to forecast the future distribution of land use decades into the future. We also use the cell-level data set to estimate the fraction of regional land use devoted to transportation facilities, including major highways, airports, and railways. The paper concludes with some comments on the strengths and weaknesses of Markov chains as a land use modeling framework, and suggests some possible extensions of the model.

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Author Biographies

Michael Iacono, Department of Civil, Environmental, and Geo-Engineering University of Minnesota (USA)

Research Fellow in the Department of Civil, Environmental, and Geo-Engineering at the University of Minnesota (USA).

David Levinson, Department of Civil, Environmental, and Geo-Engineering University of Minnesota

Professor of Civil, Environmental, and Geo-Engineering at the University of Minnesota (USA).

Ahmed El-Geneidy, School of Urban Planning McGill University

Associated Professor in the School of Urban Planning at McGill University.

Rania Wasfi, Department of Geography McGill University

PhD Candidate in the Department of Geography at McGill University.

References

Anderson, T. and L. Goodman (1957). Statistical inference about Markov chains. Annals of Mathematical Statistics, 28(1), 89–110. Available at http://www.jstor.org/stable/2237025.

Baker, W. (1989). A review of models of landscape change. Landscape Ecology, 2(2), 111–133. doi: http://dx.doi.org/10.1007/BF00137155.

Bell, E. (1974). Markov analysis of land use change: an application of stochastic processes to remotely sensed data. Socio-Economic Planning Sciences, 8(6), 311–316. doi: http://dx.doi.org/10.1016/0038 0121(74)90034-2.

Bell, E. and R. Hinojosa (1977). Markov analysis of land use change: continuous time and stationary processes. Socio-Economic Planning Sciences, 11(1), 13–17. doi: http://dx.doi.org/10.1016/0038-0121(77)90041-6.

Bourne, L. (1971). Physical adjustment processes and land use succession: a conceptual review and central city example. Economic Geography , 47(1), 1–15. doi: http://dx.doi.org/10.2307/143220.

Brown, D. G., Pijanowski, B. C., & Duh, J. D. (2000). Modeling the relationships between land use and land cover on private lands in the Upper Midwest, USA. Journal of Environmental Management, 59(4), 247–263. doi: http://dx.doi.org/10.1006/jema.2000.0369.

Clark, W. (1965). Markov chain analysis in geography: an application to the movement of rental housing areas. Annals of the Association of American Geographers, 55(2), 351–359. doi: http://dx.doi.org/10.1111/j.1467-8306.1965.tb00523.x.

Davis, A. Y., B. C. Pijanowski, K. Robinson, and B. Engel (2010). The environmental and economic costs of sprawling parking lots in the United States. Land Use Policy, 27(2), 255–261. doi: http://dx.doi.org/10.1016/j.landusepol.2009.03.002.

Davis, A. Y., Pijanowski, B. C., Robinson, K. D., & Kidwell, P. B. (2010). Estimating parking lot footprints in the Upper Great Lakes Region of the USA. Landscape and Urban Planning, 96(2), 68–77. doi: http://dx.doi.org/10.1016/j.landurbplan.2010.02.004.

de Almeida, C. M., Batty, M., Monteiro, A. M. V., Camara, G., Soares-Filho, B.S., Cerqueira, G.C., and Pennachin, C. L. (2003). Stochastic cellular automata modeling of urban land use dynamics: empirical development and estimation. Computers, Environment and Urban Systems, 27(5), 481–509. doi: http://dx.doi.org/10.1016/S0198-9715(02)00042-X.

Lever, W. (1972). The intra-urban movement of manufacturing: A Markov approach. Transactions of the Institute of British Geographers, 56, 21–38. doi: http://dx.doi.org/10.2307/621540.

Levinson, D. and Chen, W. (2005). Paving new ground: a Markov chain model of the change in transportation networks and land use. In D. Levinson and K. Krizek (Eds.), Access to Destinations (pp. 243–266). Elsevier Science. ISBN: 9780080446783.

Liu, X. and Andersson, C. (2004). Assessing the impact of temporal dynamics on land-use change modeling. Computers, Environment and Urban Systems, 28(1), 107–124. doi: http://dx.doi.org/10.1016/S0198-9715(02)00045-5.

McMillen, D. and McDonald, J. (1991). A Markov chain model of zoning change. Journal of Urban Economics, 30(2), 257–270. doi: http://dx.doi.org/10.1016/0094-1190(91)90040-E.

Muller, R. and Middleton, J. (1994). A Markov model of land-use change dynamics in the Niagara region, Ontario, Canada. Landscape Ecology, 9(2), 151–157. doi: http://dx.doi.org/10.1007/BF00124382.

Pontius, G. R. and Malanson J. (2005). Comparison of the structure and accuracy of two land change models. International Journal of Geographical Information Science, 19(2), 243–265. doi: http://dx.doi.org/10.1080/13658810410001713434.

Stokey, E. and Zeckhauser, R. (1978). A primer for policy analysis. W.W. Norton. ISBN-13: 978-0393090987.

Turner, M. (1987). Spatial simulation of landscape changes in Georgia: a comparison of three transition models. Landscape Ecology, 1(1), 29–36. doi: http://dx.doi.org/10.1007/BF02275263.

Weng, Q. (2002). Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. Journal of Environmental Management 64(3), 273–284. doi: http://dx.doi.org/10.1006/jema.2001.0509.

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Published

11-12-2015

How to Cite

Iacono, M., Levinson, D., El-Geneidy, A., & Wasfi, R. (2015). A Markov Chain Model of Land Use Change. TeMA - Journal of Land Use, Mobility and Environment, 8(3), 263–276. https://doi.org/10.6092/1970-9870/2985

Issue

Vol. 8 No. 3 (2015): Cities, Energy and Mobility Strategies for consumptions’ reduction

Section

Focus

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