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A comparison of proximity and accessibility drivers in simulating dynamic urban growth

机译：在模拟动态城市增长中的接近和可访问性驱动因素比较

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摘要

Dynamic urban growth is affected by various driving factors which are important for building cellular automata (CA) models. Two categories of urban-growth drivers (i.e., proximity and accessibility) have not been well differentiated in simulating urban patterns. We built two CA models (Pro-CA(PSO) and Acc-CA(PSO)) using particle swarm optimization (PSO) incorporating the proximity and accessibility drivers and applied these models to simulate urban growth at Zhuji in China. The results show that although both models accurately reproduced urban growth from 2005 to 2015, differences in the allocation of urban cells are apparent. The proximity-based model yielded a slightly better figure of merit (FOM) of 19.3% during calibration (2005-2010) as compared to the accessibility-based model with FOM of 17.5%. An opposite trend (FOM 17.5% vs. 18.3%) was found for subsequent validation (2010-2015). The simulations by the Acc-CA(PSO) model are closer to the real urban patterns as measured by landscape metrics. The results indicate that accessibility is a relatively robust driver in predicting long-term future urban growth. For specific regions, urban future scenarios produced by the two models show opposing patterns; in the same areas, when Pro-CA(PSO) predicted more new urban cells, Acc-CA(PSO) predicted fewer, and vice versa. Our methods are useful for modelers in selecting appropriate factors between proximity and accessibility and arrange the combination to represent human disturbance when simulating urban growth.
机译：动态城市增长受到各种驱动因素的影响，这些因素对于建立细胞自动机（CA）模型非常重要。在模拟城市模式时，两类城市增长驱动因素（即邻近性和可达性）没有得到很好的区分。我们使用粒子群优化算法（PSO）建立了两个CA模型（Pro-CA（PSO）和Acc-CA（PSO）），将邻近性和可达性驱动因素结合起来，并应用这些模型模拟了中国诸暨的城市增长。结果表明，尽管这两个模型都准确地再现了2005年至2015年的城市增长，但城市细胞的分配存在明显差异。在校准（2005-2010年）期间，基于接近度的模型产生了19.3%的略好的优值（FOM），与基于可访问性的模型（FOM为17.5%）相比。随后的验证（2010-2015年）发现了相反的趋势（FOM 17.5%对18.3%）。Acc-CA（PSO）模型的模拟结果更接近于用景观指标衡量的真实城市格局。结果表明，可达性是预测未来城市长期增长的一个相对强劲的驱动力。对于特定区域，两个模型产生的城市未来情景显示出相反的模式；在同一地区，当Pro-CA（PSO）预测更多新的城市细胞时，Acc-CA（PSO）预测更少，反之亦然。我们的方法有助于建模人员在接近度和可达性之间选择合适的因素，并在模拟城市增长时安排组合来表示人为干扰。

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  • 来源
    《Transactions in GIS: TG》 |2021年第2期|共25页
  • 作者

    Wang Rong; Feng Yongjiu; Wei Yongliang; Tong Xiaohua; Zhai Shuting; Zhou Yilun; Wu Peiqi;

  • 作者单位

    Shanghai Ocean Univ Coll Marine Sci Shanghai 201306 Peoples R China;

    Tongji Univ Coll Surveying &

    Geoinformat Shanghai 200092 Peoples R China;

    Shanghai Ocean Univ Coll Marine Sci Shanghai 201306 Peoples R China;

    Tongji Univ Coll Surveying &

    Geoinformat Shanghai 200092 Peoples R China;

    Shanghai Ocean Univ Coll Marine Sci Shanghai 201306 Peoples R China;

    Tongji Univ Coll Surveying &

    Geoinformat Shanghai 200092 Peoples R China;

    Tongji Univ Coll Surveying &

    Geoinformat Shanghai 200092 Peoples R China;

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  • 正文语种 eng
  • 中图分类 测绘数据库与信息系统;
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