Simulating urban expansion by incorporating an integrated gravitational field model into a demand-driven random forest-cellular automata model
Simulating urban expansion by incorporating an integrated gravitational field model into a demand-driven random forest-cellular automata model
复制标题
通过将集成引力场模型纳入需求驱动的随机森林细胞自动机模型来模拟城市扩张
DOI：
10.1016/j.cities.2020.103044
复制
发表时间：
2021-02-01
期刊：
影响因子：
6.7
通讯作者：
Guan, Qingfeng
中科院分区：
文献类型：
--
作者：
Lv, Jianjun;Wang, Yifan;Guan, Qingfeng
在城市群中，城市间的互动在推动城市扩张方面发挥着越来越重要的作用。许多研究将引力场模型（GFM）与元胞自动机（CA）模型相结合来分析城市空间相互作用对城市群的影响。然而，以往的研究都使用基于阈值的CA模型，这不能确保所提出的CA模型和对比CA模型的模拟城市土地（一个重要的控制变量）的数量是一致的实验过程中。此外，以往的研究仅采用一两个指标来表征城市间的空间相互作用，不能全面反映城市群内部的城市空间场强度水平。此外，以前的研究往往采用简单的采矿方法（例如，logistic回归）来挖掘CA模型的转换规则。这些方法无法探索城市增长与驱动因素（包括城市空间场强度）之间的复杂关系。本文提出了一个综合经济指标、时间成本距离和信息流强度的综合引力场模型（IGFM），对城市空间场强度进行量化。随机森林（RF）算法是一种具有很强拟合能力的机器学习方法，被用来挖掘需求驱动的CA模型的复杂转换规则，这是以前开发的简单挖掘方法无法完成的。需求驱动的CA的使用确保了对比CA之间的城市需求的一致性，这可以帮助生成更严格的结果。将所提出的IGFM-RF-CA模型应用于京津冀城市群的城市增长模拟。IGFM-RF-CA不仅可以对整个区域，而且可以对大多数小区，特别是在相对发达的城市，实现高精度的模拟。我们还发现，在模拟中的信息流的强度可以显着提高CA模型的性能，特别是在小城市位于沿着周边的BTH，其特点是相对较低的经济发展，但高互联网普及。我们建议一些小城市（例如，张家口、承德）可以通过提高互联网普及度来促进其发展。
Interactions among cities are playing an increasingly significant role in driving urban expansion in urban agglomerations. Many studies have combined the gravitational field model (GFM) with the cellular automata (CA) model to analyze the impact of urban spatial interaction on urban agglomerations. However, previous studies have used threshold-based CA models, which cannot ensure that the amount of simulated urban land (an important control variable) of the proposed CA model and contrasting CA models are consistent during experiments. In addition, previous studies have applied only one or two indicators to represent spatial interactions among cities, which cannot fully reflect the urban spatial field intensity levels within city clusters. Furthermore, previous studies have tended to apply simple mining methods (e.g., logistic regression) to mine the transition rules of CA models. These methods cannot explore the complex relationships between urban growth and driving factors (including urban spatial field intensity). This study proposes an integrated gravitational field model (IGFM) by combining comprehensive economic indicators, time-cost distance and information flow intensity to quantify urban spatial field intensity. The random forest (RF) algorithm, a machine learning method with a strong fitting ability, is adopted to mine the complex transition rules of a demand-driven CA model, which the previously developed simple mining methods are unable to accomplish. The use of demand-driven CA ensures the consistency of urban demand between contrasting CAs, which can help generate more rigorous results. The proposed IGFM-RF-CA is applied to simulate urban growth in the Beijing-Tianjin-Hebei urban agglomeration (BTH). The IGFM-RF-CA can achieve high simulation accuracy not only for a whole area but also for most subdistricts, especially in relatively developed cities. We also find that the intensity of information flow in the simulation can significantly improve the performance of the CA model, particularly in small cities located along the periphery of the BTH, which are characterized by relatively low economic development but high Internet popularity. We suggest that some small cities (e.g., Zhangjiakou and Chengde) can promote their development by increasing their Internet popularity.