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Identification of Population Growth and Distribution, Based on Urban Zone Functions

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  • Ida Bagus Ilham Malik

    (Graduate School of Environmental Engineering, The University of Kitakyushu, Kitakyushu 808-0135, Japan
    Engineering Faculty, the University of Bandar Lampung, Bandar Lampung 35142, Indonesia)

  • Bart Julien Dewancker

    (Department of Architecture, The University of Kitakyushu, Kitakyushu 808-0135, Japan)

Abstract

Population growth and distribution are still widely seen from the perspective of urban areas. Though the city has many zones with various functions, so comes the question, what zone will have population growth and hold the highest population growth distribution? And why would residents choose that zone? The study was conducted in Bandar Lampung, Indonesia, using population data from 2004 to 2011 (8 years) and urban zoning data from government documents. The results show that the conservation zone has the highest population growth and receives the highest population growth distribution. This result is confirmed by a cross section survey of respondents who live in the conservation zone. The survey results show that purchase, wide land, environment, and native inhabitants are variables affecting the population’s choice of the conservation zone as their residential location. The study also shows that the zones with the highest population do not automatically have the highest population growth and accommodate the highest population increase. Population analysis using the zonation system can help us better understand population growth and population growth distribution in urban areas.

Suggested Citation

  • Ida Bagus Ilham Malik & Bart Julien Dewancker, 2018. "Identification of Population Growth and Distribution, Based on Urban Zone Functions," Sustainability, MDPI, vol. 10(4), pages 1-13, March.
  • Handle: RePEc:gam:jsusta:v:10:y:2018:i:4:p:930-:d:137671
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    References listed on IDEAS

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    Cited by:

    1. Yuewen Yang & Dongyan Wang & Zhuoran Yan & Shuwen Zhang, 2021. "Delineating Urban Functional Zones Using U-Net Deep Learning: Case Study of Kuancheng District, Changchun, China," Land, MDPI, vol. 10(11), pages 1-21, November.

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