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Multitype Green-Space Modeling for Urban Planning Using GA and GIS

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  • Meher Nigar Neema
  • Akira Ohgai

Abstract

Green spaces can facilitate sustainable urban environment in a number of ways: Purifying air and water, filtering noise, and stabilizing the microclimate. Therefore, city planners have to design optimal sites to provide new green spaces. The present study addresses the genetic-algorithm-based multiobjective modeling of optimal sites for multitype green spaces considering multiple objectives. A new model has been developed and applied to identify the optimum sites for green spaces, particularly parks and open spaces (POSs). We considered six criteria: population, air quality, noise level, air temperature, water quality, and recreational value, including barriers for placing new POSs. The model thus developed was applied to Dhaka as a case study. The spatial functions of GIS are used to quantify, analyze, and represent the six objective criteria of our model. The modeling results show a successful optimization of locations for new POS. In addition, a suitability analysis is performed to find locations of various POSs using GIS. This study provides an indication of how to site multitype green spaces to make a sustainable urban environment.

Suggested Citation

  • Meher Nigar Neema & Akira Ohgai, 2013. "Multitype Green-Space Modeling for Urban Planning Using GA and GIS," Environment and Planning B, , vol. 40(3), pages 447-473, June.
    • Handle: RePEc:sae:envirb:v:40:y:2013:i:3:p:447-473
    DOI: 10.1068/b38003
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    3. Yang, Lili & Jones, Bryan F. & Yang, Shuang-Hua, 2007. "A fuzzy multi-objective programming for optimization of fire station locations through genetic algorithms," European Journal of Operational Research, Elsevier, vol. 181(2), pages 903-915, September.
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    1. Zhanqiang Zhu & Wei Lang & Xiaofang Tao & Jiali Feng & Kai Liu, 2019. "Exploring the Quality of Urban Green Spaces Based on Urban Neighborhood Green Index—A Case Study of Guangzhou City," Sustainability, MDPI, vol. 11(19), pages 1-17, October.

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