The Effect of Observation Scale on Urban Growth Simulation Using Particle Swarm Optimization-Based CA Models

Yongjiu Feng, Jiafeng Wang, Xiaohua Tong, Yang Liu, Zhenkun Lei, Chen Gao and Shurui Chen
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Yongjiu Feng: College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
Jiafeng Wang: College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
Xiaohua Tong: College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
Yang Liu: College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
Zhenkun Lei: College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
Chen Gao: College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
Shurui Chen: College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China

Sustainability, 2018, vol. 10, issue 11, 1-20

Abstract: Cellular automata (CA) is a bottom-up self-organizing modeling tool for simulating contagion-like phenomena such as complex land-use change and urban growth. It is not known how CA modeling responds to changes in spatial observation scale when a larger-scale study area is partitioned into subregions, each with its own CA model. We examined the impact of changing observation scale on a model of urban growth at UA-Shanghai (a region within a one-hour high-speed rail distance from Shanghai) using particle swarm optimization-based CA (PSO-CA) modeling. Our models were calibrated with data from 1995 to 2005 and validated with data from 2005 to 2015 on spatial scales: (1) Regional-scale: UA-Shanghai was considered as a single study area; (2) meso-scale: UA-Shanghai was partitioned into three terrain-based subregions; and (3) city-scale: UA-Shanghai was partitioned into six cities based on administrative boundaries. All three scales yielded simulations averaging about 87% accuracy with an average Figure-of-Merit (FOM) of about 32%. Overall accuracy was reduced from calibration and validation. The regional-scale model yielded less accurate simulations as compared with the meso- and city-scales for both calibration and validation. Simulation success in different subregions is independent at the city-scale, when compared with regional- and meso-scale. Our observations indicate that observation scale is important in CA modeling and that smaller scales probably lead to more accurate simulations. We suggest smaller partitions, smaller observation scales and the construction of one CA model for each subregion to better reflect spatial variability and to produce more reliable simulations. This approach should be especially useful for large-scale areas such as huge urban agglomerations and entire nations.

Keywords: PSO-CA; urban growth modeling; observation scale; simulation accuracy; Figure-of-Merit; UA-Shanghai (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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