TY - GEN
T1 - Particle swarm optimization based spatial location allocation of urban parks - A case study in Baoshan District, Shanghai, China
AU - Yu, Jia
AU - Chen, Yun
AU - Wu, Jianping
AU - Liu, Rui
AU - Xu, Hui
AU - Yao, Dongjing
AU - Fu, Jing
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/9/25
Y1 - 2014/9/25
N2 - This paper introduces a spatial location allocation (SLA) method for urban parks based on Particle Swarm Optimization (PSO). PSO is an effective optimization method on the basis of swarm intelligence. The algorithms of it are population based random search algorithms inspired by the social behavior of bird flocks. Compared with the other artificial intelligence (AI) algorithms, PSO is simple, easy to implement, needs fewer parameters. In the problem of SLA for urban park, three factors: population density, accessibility and competitiveness, were considered to configure a specified number of parks in this study. To find the locations of parks which satisfy these requirements, the calculation of SLA using the traditional overlaying method is with high complexity. The PSO method can decrease the complexity of computation and locate a set of parks in reasonable time. A case study in Baoshan District of Shanghai, China was proposed. The service area analysis of the simulation result of urban parks convinced that the result can confirm the fairness of public green-space service and the PSO method is a practicable and efficient approach in SLA problem. The method can easily be extended for other service facilities, for instance, the location allocation of water-saving irrigation systems, agriculture service centers, hospitals, supermarkets and cinemas, etc.
AB - This paper introduces a spatial location allocation (SLA) method for urban parks based on Particle Swarm Optimization (PSO). PSO is an effective optimization method on the basis of swarm intelligence. The algorithms of it are population based random search algorithms inspired by the social behavior of bird flocks. Compared with the other artificial intelligence (AI) algorithms, PSO is simple, easy to implement, needs fewer parameters. In the problem of SLA for urban park, three factors: population density, accessibility and competitiveness, were considered to configure a specified number of parks in this study. To find the locations of parks which satisfy these requirements, the calculation of SLA using the traditional overlaying method is with high complexity. The PSO method can decrease the complexity of computation and locate a set of parks in reasonable time. A case study in Baoshan District of Shanghai, China was proposed. The service area analysis of the simulation result of urban parks convinced that the result can confirm the fairness of public green-space service and the PSO method is a practicable and efficient approach in SLA problem. The method can easily be extended for other service facilities, for instance, the location allocation of water-saving irrigation systems, agriculture service centers, hospitals, supermarkets and cinemas, etc.
KW - artificial intelligence (AI)
KW - GIS
KW - location allocation
KW - network distance
KW - particle swarm optimization (PSO)
UR - http://www.scopus.com/inward/record.url?scp=84909993405&partnerID=8YFLogxK
U2 - 10.1109/Agro-Geoinformatics.2014.6910575
DO - 10.1109/Agro-Geoinformatics.2014.6910575
M3 - Conference contribution
AN - SCOPUS:84909993405
T3 - 2014 The 3rd International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2014
SP - 1
EP - 6
BT - 2014 The 3rd International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2014
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - United States
T2 - 2014 3rd International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2014
Y2 - 11 August 2014 through 14 August 2014
ER -