Prediction for Nonthaburi Urban Parks by Integrated Geo-Informatics Techniques

Teerawate Limgomonvilas
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Teerawate Limgomonvilas: Faculty of Social Sciences, Srinakharinwirot University, Bangkok, Thailand

International Journal of Technology and Engineering Studies, 2017, vol. 3, issue 1, 20-28

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.

Keywords: Geographic Information System (GIS); Multi-Criteria Analysis (MCA); Cellular Automata; Markov Model (CA-Markov); Urban Park; Land Use Prediction (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:apa:ijtess:2017:p:20-28

DOI: 10.20469/ijtes.3.40003-1

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