Multiple intra-urban land use simulations and driving factors analysis: a case study in Huicheng, China
Multiple intra-urban land use simulations and driving factors analysis: a case study in Huicheng, China
复制标题
多重城市内土地利用模拟及驱动因素分析：以中国惠城为例
DOI：
10.1080/15481603.2018.1507074
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发表时间：
2019-02-17
影响因子：
6.7
通讯作者：
Chen, Yimin
中科院分区：
文献类型：
--
作者：
Zhang, Dachuan;Liu, Xiaoping;Chen, Yimin
城市内部土地利用变化的模拟逐渐引起更多的注意，因为这些方法在决策和政策制定方面非常有帮助。虽然以前的研究主要集中在开发城市内部水平模拟的方法上，但很少有研究解释城市内部土地利用变化的驱动因素。城市规划者高度关注城市内部结构的形成及其功能。为了模拟多种城市内部土地利用变化，并确定不同驱动因素的贡献，我们开发了一个基于随机森林（RF）算法的元胞自动机（CA）模拟模型。该模型将交通区位因素、环境因素、公共服务和人口密度等不同类型的空间变量作为驱动因素，增强了我们对城市内部土地利用动态的理解。利用广东省惠州市惠城区的数据对CA模型进行了检验。该模型使用2000年至2010年的实际历史土地利用数据进行验证。应用该模型，对2015年多幅城市内部土地利用图进行了模拟。同时，利用射频算法的袋外误差估计方法计算空间变量重要性测度（VIMs）。在此基础上，对各城市内部土地利用驱动因素的重要性进行了评价和分析。该研究为城市规划者和相关学者提供了详细而有针对性的信息，有助于制定针对不同城市内部土地利用的具体规划策略，并支持该地区的未来发展。
Simulations of intra-urban land use changes have gradually attracted more attention as these approaches are extremely helpful in regard to decision making and policy formulation. While prior studies mostly focused on methods of developing intra-urban level simulations, very little research has been conducted explain the factors driving intra-urban land use change. Urban planners are highly concerned with how inner-city structures are formed and how they function. Here, to simulate multiple intra-urban land use changes and to identify the contribution of different driving factors, we developed a random forests (RF) algorithm-based cellular automata (CA) simulation model. In this study, the model applied diverse categories of spatial variables, including traffic location factors, environmental factors, public services, and population density, as the driving factors to enhance our understanding of the dynamics of internal urban land use. The CA model was tested using data from the Huicheng district of Huizhou city in the Guangdong province of China. The Model was validated using actual historical land use data from 2000 to 2010. By applying the validated model, multiple intra-urban land use maps were simulated for 2015. Simultaneously, spatial variable importance measures (VIMs) were calculated by using the out-of-bag (OOB) error estimation approach of the RF algorithm. Based on the calculation results, we assessed and analysed the significance of each intra-urban land use driver for this region. This study provides urban planners and relevant scholars with detailed and targeted information that can aid in the formulation of specific planning strategies for different intra-urban land uses and support the future evolution of this area.