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Land Use Change Model Comparison: Mae Sot Special Economic Zone

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Sustainable Human–Nature Relations

Part of the book series: Advances in 21st Century Human Settlements ((ACHS))

Abstract

Development of the Mae Sot Special Economic Zone (SEZ), Tak province, connects Thailand’s economy through the city of Myawaddy, Karen State, Myanmar with Mawlamyine, Yangon, Myanmar, India, and the south of China. Support for several basic infrastructure-related projects and public sector mega department stores are under construction. To date, these investments had not appeared in Tak province. As a result, land use change plays an important part in influencing Mae Sot SEZ. This chapter is a case study on land use change and prediction modeling over the next 20 years (i.e., 2028 and 2038) utilizing the cellular automata (CA)-Markov model and Land Change Modeler (LCM) methods. Predictive results show similar findings from both methods. Results indicate the forest areas and water bodies will change into agricultural and community areas, while the agricultural areas will change to community areas. These methods can assist in proper administrative safe measures to monitor impact on society, environment, security, and public health.

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References

  1. Tsertseil J (2015) The clusters and special economic zone: the improvement in the development of the region. OMICS International

    Google Scholar 

  2. Frick SA, Rodríguez-Pose A, Wong MD (2019) Toward economically dynamic special economic zones in emerging countries. Econ Geogr 95:30–64. https://doi.org/10.1080/00130095.2018.1467732

    Article  Google Scholar 

  3. Crane B, Albrecht C, Duffin KM, Albrecht C (2018) China’s special economic zones: an analysis of policy to reduce regional disparities. Reg Stud Reg Sci 5:98–107. https://doi.org/10.1080/21681376.2018.1430612

    Article  Google Scholar 

  4. Babita M (2017) Output and input efficiency of special economic zones (SEZs) in India. Indian Econ J 65:107–118. https://doi.org/10.1177/0019466217727881

    Article  Google Scholar 

  5. Wang J (2013) The economic impact of special economic zones: evidence from Chinese municipalities. J Dev Econ 101:133–147

    Article  Google Scholar 

  6. Sinenko O, Mayburov I (2017) Comparative analysis of the effectiveness of special economic zones and their influence on the development of territories. Int J Econ Financ Issues 7:115–122

    Google Scholar 

  7. Cardinale M, Brusetti L, Quatrini P et al (2004) Comparison of different primer sets for use in automated ribosomal intergenic spacer analysis of complex bacterial communities. Appl Environ Microbiol 70:6147–6156

    Article  CAS  Google Scholar 

  8. Chavanavesskul S (2018) The impact of Mae Sot’s special economic zone on climate change. In: 2018 Asia Global land programme conference: Transitioning to sustainable development of land systems through teleconections and telecoupling, 3–5 Sept 2018. Taipei, Taiwan

    Google Scholar 

  9. BOI (2018) A guide to investment in the special economic development zones (SEZ). Office of the Board of Investment, Bangkok

    Google Scholar 

  10. Ishida M (2009) Special economic zones and economic corridors. In: Kuchiki A, Uchikawa S (eds) Research on development strategies for CLMV countries. ERIA, Jakarta, pp 33–52

    Google Scholar 

  11. Government of Thailand (2014) Results of the special economic development board (NRP) meeting. Government House, Bangkok, Thailand

    Google Scholar 

  12. Thitawadee S, Yoshihisa M (2018) Urban growth prediction of special economic development zone in Mae Sot District, Thailand. Eng J 22:269–277. https://doi.org/10.4186/ej.2018.22.3.269

    Article  Google Scholar 

  13. Tawilpipatkul D (1996) The process of urbanisation and social change. Chulalongkorn University, Bangkok, Thailand

    Google Scholar 

  14. Labs Clark (2013) IDRISI spotlight: the land change modeler. Clark University, Massachusetts

    Google Scholar 

  15. Lee N (2019) Inclusive growth in cities: a sympathetic critique. Reg Stud 53:424–434. https://doi.org/10.1080/00343404.2018.1476753

    Article  Google Scholar 

  16. McNevin A (2007) Irregular migrants, neoliberal geographies and spatial frontiers of ‘the political’. Rev Int Stud 33:655–674. https://doi.org/10.1017/S0260210507007711

    Article  Google Scholar 

  17. Intarat P (2018) From SEZs to Thailand 4.0: geopolitics of borderlands in the Thai state’s vision. For Soc 2:65. https://doi.org/10.24259/fs.v2i1.3600

  18. Joshi R (2017) Assessing the impact of income inequality on economic growth. Indian Econ J 65:1–26. https://doi.org/10.1177/0019466217727811

    Article  Google Scholar 

  19. De Jong M, Joss S, Schraven D et al (2015) Sustainable-smart-resilient-low carbon-eco-knowledge cities; making sense of a multitude of concepts promoting sustainable urbanization. J Clean Prod 109:25–38. https://doi.org/10.1016/j.jclepro.2015.02.004

    Article  Google Scholar 

  20. UN (2018) 2018 revision of world urbanization prospects. New York

    Google Scholar 

  21. Russo A, Cirella G (2018) Modern compact cities: how much greenery do we need? Int J Environ Res Public Health 15:2180. https://doi.org/10.3390/ijerph15102180

    Article  Google Scholar 

  22. Long H, Heilig GK, Li X, Zhang M (2007) Socio-economic development and land-use change: analysis of rural housing land transition in the transect of the Yangtse River, China. Land Use Policy 24:141–153. https://doi.org/10.1016/j.landusepol.2005.11.003

    Article  Google Scholar 

  23. International Institute for Trade and Development (2014) Guidelines and measures for the development of special economic zones in Thai border. Policy briefs on research projects: IITD, Bangkok, Thailand

    Google Scholar 

  24. Losiri C (2017) Land use change model and urban area prediction in the future. J Soc Sci 19:340–357

    Google Scholar 

  25. Oueslati W, Salanié J, Wu J (2019) Urbanization and agricultural productivity: some lessons from European cities. J Econ Geogr 19:225–249. https://doi.org/10.1093/jeg/lby001

    Article  Google Scholar 

  26. Mosammam HM, Nia JT, Khani H et al (2017) Monitoring land use change and measuring urban sprawl based on its spatial forms: the case of Qom city. Egypt J Remote Sens Sp Sci 20:103–116. https://doi.org/10.1016/j.ejrs.2016.08.002

    Article  Google Scholar 

  27. Deep S, Saklani A (2014) Urban sprawl modeling using cellular automata. Egypt J Remote Sens Sp Sci 17:179–187. https://doi.org/10.1016/j.ejrs.2014.07.001

    Article  Google Scholar 

  28. Fuglsang M, Münier B, Hansen HS (2013) Modelling land-use effects of future urbanization using cellular automata: an Eastern Danish case. Environ Model Softw 50:1–11. https://doi.org/10.1016/J.ENVSOFT.2013.08.003

    Article  Google Scholar 

  29. Thorp KR, Bronson KF (2013) A model-independent open-source geospatial tool for managing point-based environmental model simulations at multiple spatial locations. Environ Model Softw 50:25–36. https://doi.org/10.1016/j.envsoft.2013.09.002

    Article  Google Scholar 

  30. Patra S, Sahoo S, Mishra P, Mahapatra SC (2018) Impacts of urbanization on land use cover changes and its probable implications on local climate and groundwater level. J Urban Manag 7:70–84. https://doi.org/10.1016/J.JUM.2018.04.006

    Article  Google Scholar 

  31. Verburg PH, Schot PP, Dijst MJ, Veldkamp A (2004) Land use change modelling: current practice and research priorities. GeoJournal 61:309–324. https://doi.org/10.1007/s10708-004-4946-y

    Article  Google Scholar 

  32. Terama E, Clarke E, Rounsevell MDA et al (2019) Modelling population structure in the context of urban land use change in Europe. Reg Environ Chang 19:667–677. https://doi.org/10.1007/s10113-017-1194-5

    Article  Google Scholar 

  33. Cao Q, Yu D, Georgescu M et al (2018) Impacts of future urban expansion on summer climate and heat-related human health in eastern China. Environ Int 112:134–146. https://doi.org/10.1016/j.envint.2017.12.027

    Article  Google Scholar 

  34. Yang B, Yang X, Leung LR et al (2019) Modeling the impacts of urbanization on summer thermal comfort: the role of urban land use and anthropogenic heat. J Geophys Res Atmos 124:2018JD029829. https://doi.org/10.1029/2018JD029829

  35. Buzan JR, Oleson K, Huber M (2015) Implementation and comparison of a suite of heat stress metrics within the community land model version 4.5. Geosci Model Dev 8:151–170. https://doi.org/10.5194/gmd-8-151-2015

    Article  Google Scholar 

  36. Long H, Wu X, Wang W, Dong G (2008) Analysis of urban-rural land-use change during 1995–2006 and its policy dimensional driving forces in Chongqing, China. Sensors 8:681–699. https://doi.org/10.3390/s8020681

    Article  Google Scholar 

  37. Yao X, Wang Z, Wang H (2015) Impact of urbanization and land-use change on surface climate in middle and lower reaches of the Yangtze River, 1988–2008. Adv Meteorol 2015:1–10. https://doi.org/10.1155/2015/395094

    Article  CAS  Google Scholar 

  38. Wang SQ, Zheng XQ, Zang XB (2012) Accuracy assessments of land use change simulation based on Markov-cellular automata model. Procedia Environ Sci 13:1238–1245. https://doi.org/10.1016/j.proenv.2012.01.117

    Article  Google Scholar 

  39. Halmy MWA, Gessler PE, Hicke JA, Salem BB (2015) Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Appl Geogr 63:101–112. https://doi.org/10.1016/J.APGEOG.2015.06.015

    Article  Google Scholar 

  40. Noszczyk T (2019) A review of approaches to land use changes modeling. Hum Ecol Risk Assess An Int J 25:1377–1405. https://doi.org/10.1080/10807039.2018.1468994

    Article  CAS  Google Scholar 

  41. Mishra V, Rai P, Mohan K (2014) Prediction of land use changes based on land change modeler (LCM) using remote sensing: a case study of Muzaffarpur (Bihar), India. J Geogr Inst Jovan Cvijic, SASA 64:111–127. https://doi.org/10.2298/IJGI1401111M

    Article  Google Scholar 

  42. Roshanbakhsh S, Modaresi SA, Karami J (2017) Land use changes using multi-layer perception and change modeler. Int J Urban Manag Energy Sustain 1:79–84. https://doi.org/10.22034/IJUMES.2017.01.01.008

  43. Hyandye C, Martz LW (2017) A Markovian and cellular automata land-use change predictive model of the Usangu Catchment. Int J Remote Sens 38:64–81. https://doi.org/10.1080/01431161.2016.1259675

    Article  Google Scholar 

  44. Ghosh P, Mukhopadhyay A, Chanda A et al (2017) Application of cellular automata and Markov-chain model in geospatial environmental modeling—a review. Remote Sens Appl Soc Environ 5:64–77. https://doi.org/10.1016/J.RSASE.2017.01.005

    Article  Google Scholar 

  45. Kumar S, Radhakrishnan N, Mathew S (2014) Land use change modelling using a Markov model and remote sensing. Geomatics, Nat Hazards Risk 5:145–156. https://doi.org/10.1080/19475705.2013.795502

    Article  Google Scholar 

  46. Mondal MS, Sharma N, Garg PK, Kappas M (2016) Statistical independence test and validation of CA Markov land use land cover (LULC) prediction results. Egypt J Remote Sens Sp Sci 19:259–272. https://doi.org/10.1016/J.EJRS.2016.08.001

    Article  Google Scholar 

  47. Hishe S, Bewket W, Nyssen J, Lyimo J (2019) Analysing past land use land cover change and CA-Markov-based future modelling in the Middle Suluh Valley, Northern Ethiopia. Geocarto Int 1–31. https://doi.org/10.1080/10106049.2018.1516241

  48. Liping C, Yujun S, Saeed S (2018) Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques-a case study of a hilly area, Jiangle, China. PLoS One 13:e0200493. https://doi.org/10.1371/journal.pone.0200493

    Article  CAS  Google Scholar 

  49. Nouri J, Gharagozlou A, Arjmandi R et al (2014) Predicting urban land use changes using a CA–Markov model. Arab J Sci Eng 39:5565–5573. https://doi.org/10.1007/s13369-014-1119-2

    Article  Google Scholar 

  50. Hamad R, Balzter H, Kolo K (2018) Predicting land use-land cover changes using a CA-Markov model under two different scenarios. Sustainability 10:3421. https://doi.org/10.3390/su10103421

    Article  Google Scholar 

  51. Russo P (2017) Usability of planning support systems: analysing adoption and use in planning practice. University of Melbourne

    Google Scholar 

  52. Joorabian Shooshtari S, Shayesteh K, Gholamalifard M et al (2017) Impacts of future land cover and climate change on the water balance in northern Iran. Hydrol Sci J 62:2655–2673. https://doi.org/10.1080/02626667.2017.1403028

    Article  Google Scholar 

  53. Widyasamratri H, Aswad A (2017) A preliminary study: an agent-based spatial simulation of human-coastal environment interaction. In: The third international conference on coastal and delta areas, pp 593–601

    Google Scholar 

  54. Maestripieri N (2012) Dynamiques spatio-temporelles des plantationsforestières industrielles dans le sud chilien: de l’analysediachronique à la modélisation prospective [in French]. Université Toulousele Mirail, Toulouse II

    Google Scholar 

  55. Clark Labs (2019) Clark labs. https://clarklabs.org/. Accessed 26 May 2019

  56. Nadnicha P, Onanong P, Kasem C et al (2016) The effect of land use changes on landslide in the high slope area at Surat Thani Province. KKU Sci J 44:212–221

    Google Scholar 

  57. Osman TMK (2016) Driving Forces, and future directions of informal urban expansion in greater Cairo Metropolitan region. Kyushu University

    Google Scholar 

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Acknowledgements

Gratitude and funding are given to Srinakharinwirot University, Bangkok, Thailand.

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Authors and Affiliations

  1. Department of Geography, Srinakharinwirot University, Bangkok, Thailand

    Sutatip Chavanavesskul

  2. Faculty of Economics, University of Gdansk, Sopot, Poland

    Giuseppe T. Cirella

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  1. Sutatip Chavanavesskul
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  2. Giuseppe T. Cirella
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Corresponding author

Correspondence to Sutatip Chavanavesskul .

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Editors and Affiliations

  1. Faculty of Economics, University of Gdansk, Sopot, Poland

    Giuseppe T. Cirella

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Chavanavesskul, S., Cirella, G.T. (2020). Land Use Change Model Comparison: Mae Sot Special Economic Zone. In: Cirella, G. (eds) Sustainable Human–Nature Relations. Advances in 21st Century Human Settlements. Springer, Singapore. https://doi.org/10.1007/978-981-15-3049-4_7

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