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GIS-based multi-criteria analysis modeling used to locate suitable sites for industries in suburban areas in Bangladesh to ensure the sustainability of agricultural lands

Author

Listed:
  • Nazia Muhsin

    (University of Tsukuba)

  • Tofael Ahamed

    (University of Tsukuba)

  • Ryozo Noguchi

    (University of Tsukuba)

Abstract

Land use changes significantly affect the sustainability of food security, environmental quality and the ecological balance in developing countries. Bangladesh is one such country, and it faces challenges caused by its limited resources of arable land. The urbanization of agricultural lands and urban developments in suburban areas add to these challenges. Therefore, the aim of this study is to characterize the land use changes that occur over time in suburban areas that have potential for industrial growth. This study also assesses potential locations for industrial development by using land suitability analysis (LSA) to emphasize both agricultural and industrial development in terms of sustainable growth. A geographical information system (GIS)-based multi-criteria analysis (MCA) model is developed for the LSA to distinguish compact parcels that are suitable for industrial economic zones. Nine criteria, including seven constraints and 23 factors, are evaluated using the spatial analysis tools in ArcGIS®. An analytical hierarchy process (AHP) is applied to prioritize these criteria based on experts opinion for the decision-making process of LSA. This study finds that the construction of densely clustered industrial areas has decreased the area of agricultural land by more than 10% in the last two decades. Furthermore, the results of the LSA show that only 4% of the land was highly suitable for industrial sites. On the other hand, four compact parcels that contain 16–10 ha of land and suitable for small industrial zones exist within the study area. Thus, the integrated GIS-MCA model can serve as a policy and planning tool for locating industrial economic zones while preserving agricultural lands and retaining environmental protections.

Suggested Citation

  • Nazia Muhsin & Tofael Ahamed & Ryozo Noguchi, 2018. "GIS-based multi-criteria analysis modeling used to locate suitable sites for industries in suburban areas in Bangladesh to ensure the sustainability of agricultural lands," Asia-Pacific Journal of Regional Science, Springer, vol. 2(1), pages 35-64, April.
    • Handle: RePEc:spr:apjors:v:2:y:2018:i:1:d:10.1007_s41685-017-0046-0
    DOI: 10.1007/s41685-017-0046-0
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    References listed on IDEAS

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    1. Valentina Ferretti & Silvia Pomarico, 2012. "Integrated sustainability assessments: a spatial multicriteria evaluation for siting a waste incinerator plant in the Province of Torino (Italy)," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 14(5), pages 843-867, October.
    2. Saaty, Thomas L., 1990. "How to make a decision: The analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 48(1), pages 9-26, September.
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    Cited by:

    1. Majid Ebrahimi & Hamid Nejadsoleymani & Mohammad Reza Mansouri Daneshvar, 2019. "Land suitability map and ecological carrying capacity for the recognition of touristic zones in the Kalat region, Iran: a multi-criteria analysis based on AHP and GIS," Asia-Pacific Journal of Regional Science, Springer, vol. 3(3), pages 697-718, October.
    2. Billal Hossen & Helmut Yabar & Takeshi Mizunoya, 2021. "Land Suitability Assessment for Pulse (Green Gram) Production through Remote Sensing, GIS and Multicriteria Analysis in the Coastal Region of Bangladesh," Sustainability, MDPI, vol. 13(22), pages 1-24, November.
    3. Kazi Faiz Alam & Tofael Ahamed, 2023. "Erosion vulnerable area assessment of Jamuna River system in Bangladesh using a multi-criteria-based geospatial fuzzy expert system and remote sensing," Asia-Pacific Journal of Regional Science, Springer, vol. 7(2), pages 433-454, June.
    4. Aung Myint Htun & Md. Shamsuzzoha & Tofael Ahamed, 2023. "Rice yield prediction model using normalized vegetation and water indices from Sentinel-2A satellite imagery datasets," Asia-Pacific Journal of Regional Science, Springer, vol. 7(2), pages 491-519, June.

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