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Prediction for Nonthaburi Urban Parks by Integrated Geo-Informatics Techniques

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  • TEERAWATE LIMGOMONVILAS

    (Faculty of Social Sciences, Srinakharinwirot University, Bangkok, Thailand)

Abstract

Lack of suitable area for urban parks is the big problem in Thailand. The goal of this research is to find a suitable area for urban parks in Nonthaburi province, Thailand and to predict future land use (2019 and 2024) that will support urban parks. Data analysis was under integrated Remote Sensing (RS) to classify existing land use, Geographic Information System (GIS) with Multi-Criteria Analysis (MCA) to find the suitable area and CA-Markov model to predict the future land use. Finally, we integrated MCA with CA-Markov to improve future land use for urban parks. Then we compared the alternative of letting urban expansion without direction with an alternative of a prepared suitable land use for urban parks in the future. The study concludes that integrating Geo-Informatics modeling can increase suitable urban park areas from urban expansion up to 7.58 and 12.56 sq.km. in 2019 and 2024 respectively. The increase of the urban park areas in 2019 can increase about 6.41 sq.m. per person, which is higher than the Thailand standard (4 sq.m. per person) and is close to the world standard.

Suggested Citation

  • Teerawate Limgomonvilas, 2017. "Prediction for Nonthaburi Urban Parks by Integrated Geo-Informatics Techniques," International Journal of Technology and Engineering Studies, PROF.IR.DR.Mohid Jailani Mohd Nor, vol. 3(1), pages 20-28.
    • Handle: RePEc:apa:ijtess:2017:p:20-28
    DOI: 10.20469/ijtes.3.40003-1
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    References listed on IDEAS

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    1. Yang, Xin & Zheng, Xin-Qi & Chen, Rui, 2014. "A land use change model: Integrating landscape pattern indexes and Markov-CA," Ecological Modelling, Elsevier, vol. 283(C), pages 1-7.
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    Cited by:

    1. Menor Dawn Bernadette Ortillo, 2019. "A configurable data management solution For Small-Medium Enterprises (SMEs)," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 5(3), pages 101-115.
    2. Pornthip Liewtrakul & Katawut Kaewbanjong, 2017. "A Semantic Web Service Retrieval Approach that Combines Semantic Matching With Quality of Service Matching," Journal of ICT, Design, Engineering and Technological Science, Juhriyansyah Dalle, vol. 1(2), pages 42-47.
    3. Niang Sian Lun & Siddharth Chaudhary & Sarawut Ninsawat, 2023. "Assessment of Machine Learning Methods for Urban Types Classification Using Integrated SAR and Optical Images in Nonthaburi, Thailand," Sustainability, MDPI, vol. 15(2), pages 1-17, January.
    4. Felix Constantin Adochiei & Radu Ciucu & Ioana Raluca Adochiei & Florin Ciprian Argatu & CiprianMarius Larco & Miron Casian, 2019. "A Data Analysis platform for high altitude bio monitoring using DEVOPS principles and time series machine learning," Journal of Advances in Technology and Engineering Research, A/Professor Akbar A. Khatibi, vol. 5(1), pages 18-24.

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