Optimization of an Urban Microgreen Space Distribution Based on the PS-ACO Algorithm: A Case Study of Shenyang, China

Xi Chen, Yukuan Dong (), Xiaoshi Wang and Qiushi Wang
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Xi Chen: School of Architecture and Urban Planning, Shenyang Jianzhu University, Shenyang 110168, China
Yukuan Dong: School of Architecture and Urban Planning, Shenyang Jianzhu University, Shenyang 110168, China
Xiaoshi Wang: Material Procurement Department, China United Network Communications Limited, Liaoning Branch, Shenyang 110002, China
Qiushi Wang: School of Architecture and Urban Planning, Shenyang Jianzhu University, Shenyang 110168, China

Land, 2024, vol. 13, issue 10, 1-22

Abstract: In this study, optimization of the microgreen space distribution through multistage regulation is investigated, with the goal of alleviating the imbalance between the supply and demand of green resources in the central urban area of Shenyang. An optimized evaluation model of green space supply and demand is employed to calculate the green space accessibility index at a 100-m grid scale and identify different levels of green space resource supply and demand. Priority is given to supplementing resources for the elderly population by balancing the green space supply in vulnerable areas. Particle swarm—ant colony optimization (PS-ACO) is used to select microgreen space sites within each priority level. On the basis of the “important-urgent” quadrant analysis, S1-priority residential areas account for 8.12% of the grid, S2-priority areas account for 67.01%, and S3-priority areas account for 24.87%. The PS-ACO algorithm outputs potential microgreen space sites within each priority level to accurately regulate the green space distribution in residential areas with different supply pressures and limited land availability. A spatial correlation analysis of the new sites reveals good spatial dispersion within service units, effectively alleviating demand pressures, and good aggregation at a regional scale to address imbalances in the supply of green space in a targeted manner. Thus, the optimized results of the PS-ACO algorithm are effective, providing reliable site-selection references for subsequent urban microgreen space distributions.

Keywords: park; site selection; elderly population; green space equity; accessibility; particle swarm optimization; ant colony optimization (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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