Abstract
This study combines statistical methods and a Markov model to analyze interregional differences in land use in Beijing since 2003 and to predict land use changes for 2015 and 2019. First, the paper proposes a new concept, land use flow, which counts the change in area from the beginning to the end of the period of interest, to analyze changing land use patterns using statistical records from 2003 to 2011. Second, based on land use data between 2003, 2007 and 2011, this paper applied a Markov model to the prediction of Beijing land use in 2015 and 2019. The results show that: (1) the area of arable land decreased significantly across all of Beijing, with the greatest decrease, 6,953 ha, occurring in Tongzhou. The amount of urban land increased significantly, particularly in eastern and southern Beijing, in areas such as Chaoyang, Tongzhou and Daxing. The amount of land use for orchards fluctuated depending on the distance from the city center; (2) Haidian experienced the greatest change in its ratio of land use flow to urban land (100 %), while Tongzhou had the greatest reduction in arable land (−67.6 %); (3) the annual rate of land use change for Beijing as a whole was 0.89 %. Fangshan had the highest rate of annual conversion to grassland (13.2 %), while the Daxing District had the highest rate of change to urban land (5.1 %). Shijingshan experienced the greatest rate of annual change to other uses due to its small base (135.38 %); and (4) the predictions suggest that urban land is increasing and arable land is decreasing in the 14 districts and counties of Beijing. Several conclusions can be drawn from these results. First, the concept of land use flow is useful for analyzing land use change and simplifies previous methods that were based on descriptions of the change orientation. Moreover, an understanding of the sub-flow of land use is improved by the linkage between land use change and the Markov model. Second, the predictions of land use in 2015 and 2019 suggest problems of urban sprawl and diminishing arable land in the patterns of land use that exist in Beijing and that will continue in the future. In terms of sustainability and Beijing’s goal of being a world city, the findings can help local authorities better understand and address a complex urban master plan, and develop improved land use management strategies that can better balance urban expansion and ecological conservation.
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Notes
Data sources come from the Chinese city statistics year book.
The concept of urban land in this introduction is used only to represent cities in the urban land category.
Data sources come from the Chinese city statistics year book.
State Council’s notice on the second national land survey, and the document number: Nation [2006] No.38.
Data come from the Beijing Bureau of Statistics and the Beijing Municipal Bureau of Land Use and Resources.
The concept of urban sprawl in this section is used only to represent the area of cities and towns in urban land category, and the data come from the Beijing Bureau of Statistics and the Beijing Municipal Bureau of Land Use and Resources.
References
Aaviksoo K (1995) Simulating vegetation dynamics and land use in a mire landscape using a Markov model. Landsc Urban Plan 31(1–3):129–142
Benito PR, Cuevas JA, de la Parra RB, Prieto F, del Barrio JMG, Zavala MA (2010) Land use change in a Mediterranean metropolitan region and its periphery: assessment of conservation policies through Corine land cover data and Markov models. For Syst 19(3):315–328
Chen X, Yu SX, Zhang YP (2013) Evaluation of spatiotemporal dynamics of simulated land use/cover in China using a probabilistic cellular automata-Markov Model. Pedosphere 23(2):243–255
Cui XG, Yan TL, Zhu DH, Niu FQ, Zhang XD (2007) Applying a GIS-based model to collect information on agricultural land-use change in Beijing. N Z J Agric Res 50(5):1073–1081
Du JF, Thill JC, Peiser RB, Feng CC (2014) Urban land market and land-use changes in post-reform China: a case study of Beijing. Lands Urban Plan 124:118–128
Guan DJ, Gao WJ, Watari K, Fukahori H (2008) Land use change of Kitakyushu based on landscape ecology and Markov model. J Geog Sci 18(4):455–468
Guan DJ, Li HF, Inohae T, Su WC, Nagaie T, Hokao K (2011) Modeling urban land use change by the integration of cellular automaton and Markov model. Ecol Model 222(20–22):3761–3772
Hou JW (2011) Economic reform of China: Cause and effects. Soc Sci J 48(3):419–434
Hu YC, Zheng YM, Zheng XQ (2013) Simulation of land use scenarios for Beijing using CLUE-S and Markov composite models. Chin Geogr Sci 23(1):92–100
Jin YP (2010) Beijing: becoming a world city-strategy study of Beijing urban development to a world city. Beijing Science and Technology Press, Beijing
Kuang WH (2012) Spatio-temporal patterns of intra-urban land use change in Beijing, China between 1984 and 2008. Chin Geogr Sci 22(2):210–220
Kue-Young Kim WSH, Jina Jeong SC, Eungyu Park NHJ (2013) Non-parametric simulations-based conditional stochastic predictions of geologic heterogeneities and leakage potentials for hypothetical CO2 sequestration sites. Environ Earth Sci 71:2739–2752
Li J (2014) Land sale venue and economic growth path: Evidence from China’s urban land market. Habitat Int 41:307–313
Li Y, Zhu M, Klein R, Kong N (2014) Using a partially observable Markov chain model to assess colonoscopy screening strategies—a cohort study. Eur J Oper Res 238(1):313–326
Sang LL, Zhang C, Yang JY, Zhu DH, Yun WJ (2011) Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Math Comput Model 54(3–4):938–943
Stables A (2013) The unsustainability imperative? Problems with ‘sustainability’ and ‘sustainable development’ as regulative ideals. Environ Educ Res 19(2):177–186
Tovar C, Seijmonsbergen AC, Duivenvoorden JF (2013) Monitoring land use and land cover change in mountain regions: An example in the Jalca grasslands of the Peruvian Andes. Lands Urban Plan 112:40–49
Trubins R (2013) Land use change in southern Sweden: before and after decoupling. Land Use Policy 33:161–169
Wang XL, Bao YH (1999) Study on the methods of land use dynamic change research. Prog Geogr 18(1):81–87 (in Chinese)
Wang X, Coeurjolly D, Flin F (2014) Digital flow for shape decomposition: application to 3-D microtomographic images of snow. Pattern Recognit Lett 45:181–188
Wei YD, Ye XY (2014) Urbanization, urban land expansion and environmental change in China. Stoch Env Res Risk Assess 28:757–765
Wu Q, Li HQ, Wang RS, Paulussen J, He Y, Wang M, Wang BH, Wang Z (2006) Monitoring and predicting land use change in Beijing using remote sensing and GIS. Lands Urban Plan 78(4):322–333
Xie YC, Fang CL, Lin GCS, Gong HM, Qiao B (2007) Tempo-spatial patterns of land use changes and urban development in globalizing China: a study of Beijing. Sensors 7(11):2881–2906
Yang X, Zheng XQ, Lv LN (2012) A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata. Ecol Model 233:11–19
Yang X, Zheng XQ, Chen R (2014) A land use change model: Integrating landscape pattern indexes and Markov-CA. Ecol Model 283:1–7
Yong-Seon Zhang YM, Hi-Soo Moon YSEP (2012) Multivariate statistical analysis and 3D-coupled Markov chain modeling approach for the prediction of subsurface heterogeneity of contaminated soil management in abandoned Guryong Mine Tailings, Korea. Environ Earth Sci 68:1527–1538
Zeng XY (2014) A high-order hybrid finite difference-finite volume approach with application to inviscid compressible flow problems: a preliminary study. Comput Fluids 98:91–110
Zhang YQ, Gong HL, Zhao WJ, Li XJ (2007) Analysis of mechanism of land use change on Beijing from 1990 to 2000. Resour Sci 29(03):206–213 (in Chinese)
Zhang N, Fang LN, Zhou J, Song JP, Jiang J (2010) The study on spatial expansion and its driving forces in the urban fringe of Beijing. Geogr Res 29(3):471–480 (in Chinese)
Zhao RF, Chen YN, Shi PJ, Zhang LH, Pan JH, Zhao HL (2013) Land use and land cover change and driving mechanism in the arid inland river basin: a case study of Tarim River, Xinjiang, China. Environ Earth Sci 68(2):591–604
Zivanic D, Vujic G, Kosec B, Stoic A (2014) Material flow enhancement in production assembly lines under application of zoned order picking systems. Metalurgija 53(4):681–684
Acknowledgments
Our research was supported by the Beijing Natural Science Foundation (8122028). We thank the Beijing Land Surveying and Planning Resource Center for providing the data for the study.
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Huang, Y., Nian, P. & Zhang, W. The prediction of interregional land use differences in Beijing: a Markov model. Environ Earth Sci 73, 4077–4090 (2015). https://doi.org/10.1007/s12665-014-3693-8
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DOI: https://doi.org/10.1007/s12665-014-3693-8