Incorporation of intra-city human mobility into urban growth simulation: A case study in Beijing

北京 城市化 城市规划 背景(考古学) 特大城市 经济地理学 杠杆(统计) 计算机科学 城市密度 地理 运输工程 区域科学 中国 经济增长 土木工程 经济 经济 人工智能 考古 工程类
作者
Siying Wang,Fei Teng,Weifeng Li,Anqi Zhang,Huagui Guo,Yunyan Du
出处
期刊:Journal of Geographical Sciences [Springer Nature]
卷期号:32 (5): 892-912 被引量:8
标识
DOI:10.1007/s11442-022-1977-6
摘要

The effective modeling of urban growth is crucial for urban planning and analyzing the causes of land-use dynamics. As urbanization has slowed down in most megacities, improved urban growth modeling with minor changes has become a crucial open issue for these cities. Most existing models are based on stationary factors and spatial proximity, which are unlikely to depict spatial connectivity between regions. This research attempts to leverage the power of real-world human mobility and consider intra-city spatial interaction as an imperative driver in the context of urban growth simulation. Specifically, the gravity model, which considers both the scale and distance effects of geographical locations within cities, is employed to characterize the connection between land areas using individual trajectory data from a macro perspective. It then becomes possible to integrate human mobility factors into a neural-network-based cellular automata (ANN-CA) for urban growth modeling in Beijing from 2013 to 2016. The results indicate that the proposed model outperforms traditional models in terms of the overall accuracy with a 0.60% improvement in Cohen's Kappa coefficient and a 0.41% improvement in the figure of merit. In addition, the improvements are even more significant in districts with strong relationships with the central area of Beijing. For example, we find that the Kappa coefficients in three districts (Chaoyang, Daxing, and Shunyi) are considerably higher by more than 2.00%, suggesting the possible existence of a positive link between intense human interaction and urban growth. This paper provides valuable insights into how fine-grained human mobility data can be integrated into urban growth simulation, helping us to better understand the human-land relationship.
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