An improved urban cellular automata model by using the trend-adjusted neighborhood
An improved urban cellular automata model by using the trend-adjusted neighborhood
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DOI：
10.1186/s13717-020-00234-9
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发表时间：
2020-05-29
影响因子：
4.8
通讯作者：
Chen, Wei
中科院分区：
文献类型：
--
作者：
Li, Xuecao;Zhou, Yuyu;Chen, Wei
背景基于元胞自动机(CA)的模型已被广泛应用于城市蔓延建模。目前，大多数研究集中于模型中空间表示的改进，而对考虑城市蔓延的时间背景的研究很少。本文提出了一种基于历史城市扩展信息的趋势调整邻域权重因子，并将其融入到常用的Logistic-CA模型中，提出了一种Logistic-Trend-CA模型。结果表明，Logistic-Trend-CA模型的建模效果明显优于传统的Logistic-CA模型，在中等分辨率(1 Km)和精细分辨率(30m)下，分别比传统的Logistic-CA模型提高了18%和14%。与传统的Logistic-CA模型相比，提出的Logistic-Trend-CA模型更适合于长时间间隔的城市蔓延建模。此外，该模型对不同时期和不同空间的适宜面不敏感，其性能随着邻域规模的增加而降低。结论该模型具有在区域和全球尺度上模拟未来较长时期的城市蔓延的潜力。
BackgroundCellular automata (CA)-based models have been extensively used in urban sprawl modeling. Presently, most studies focused on the improvement of spatial representation in the modeling, with limited efforts for considering the temporal context of urban sprawl. In this paper, we developed a Logistic-Trend-CA model by proposing a trend-adjusted neighborhood as a weighting factor using the information of historical urban sprawl and integrating this factor in the commonly used Logistic-CA model. We applied the developed model in the Beijing-Tianjin-Hebei region of China and analyzed the model performance to the start year, the suitability surface, and the neighborhood size.ResultsOur results indicate the proposed Logistic-Trend-CA model outperforms the traditional Logistic-CA model significantly, resulting in about 18% and 14% improvements in modeling urban sprawl at medium (1km) and fine (30m) resolutions, respectively. The proposed Logistic-Trend-CA model is more suitable for urban sprawl modeling over a long temporal interval than the traditional Logistic-CA model. In addition, this new model is not sensitive to the suitability surface calibrated from different periods and spaces, and its performance decreases with the increase of the neighborhood size.ConclusionThe proposed model shows potential for modeling future urban sprawl spanning a long period at regional and global scales.