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A Swarm Optimization Based Method for Urban Growth Modelling

机译：基于群体优化的城市增长建模方法

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Land use activity is a major issue and challenge for town and country planners. Urban planners must be able to allocate urban land area to different applications with a special focus on the role and function of the city, its economy, and the ability to simulate the effect of user interaction with each other. Continuing migration of rural population to cities and population increases has caused many problems of today's cities including the expansion of urban areas, lack of infrastructure and urban services as well as environmental pollution. Local governments that implement urban growth boundaries need to estimate the amount of urban land required in the future given anticipated growth of housing, business, recreation and other urban activities. Urban growth is a complex process that encounters a number of sophisticated parameters that interact to produce the urban growth pattern. Urban growth modelling aims to understand the dynamic processes. Therefore, interpretability of models is becoming increasingly important. Different approaches have been applied in spatial modelling. In this study, Particle Swarm Optimization (PSO) has been used for modelling of urban growth in Qazvin city area (Iran) during 2005 to 2011. Landsat imageries, taken in 2005 and 2011 have been used in the study. Main parameters in this study are distance to residential area, distance to industrial area, slope, accessibility, land price and number of urban cell in a 3*3 neighbourhood. Figure of Merit and Kappa statistics have been used for estimating accuracy of the proposed model. DOI: http://dx.doi.org/10.5755/j01.erem.69.3.6653
机译：土地使用活动是城乡规划者面临的主要问题和挑战。城市规划人员必须能够将城市土地面积分配给不同的应用程序，并特别关注城市的作用和功能，其经济状况以及模拟用户彼此交互作用的能力。农村人口不断向城市迁移和人口增加已引起当今城市的许多问题，包括城市面积的扩大，基础设施和城市服务的缺乏以及环境污染。考虑到住房，商业，娱乐和其他城市活动的预期增长，实施城市增长边界的地方政府需要估计未来所需的城市土地数量。城市增长是一个复杂的过程，遇到许多复杂的参数，这些参数相互作用以产生城市增长模式。城市增长模型旨在了解动态过程。因此，模型的可解释性变得越来越重要。在空间建模中已应用了不同的方法。在这项研究中，粒子群优化（PSO）已用于模拟2005年至2011年Qazvin市区（伊朗）的城市增长。该研究使用了2005年和2011年拍摄的Landsat影像。本研究的主要参数是到3 * 3社区中与居民区的距离，与工业区的距离，坡度，可及性，土地价格和城市单元数量。品质因数和Kappa统计图已用于估计所提出模型的准确性。 DOI：http：//dx.doi.org/10.5755/j01.erem.69.3.6653

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