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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
1秒前
乌龙茶干发布了新的文献求助10
1秒前
邢夏之完成签到,获得积分10
1秒前
脑洞疼应助略略略采纳,获得10
1秒前
十二发布了新的文献求助10
1秒前
wlj发布了新的文献求助10
2秒前
为科研奋斗完成签到,获得积分10
2秒前
Liooo完成签到 ,获得积分20
2秒前
xiao刘完成签到,获得积分10
2秒前
飞云发布了新的文献求助30
3秒前
繁多星完成签到,获得积分10
3秒前
璟晔发布了新的文献求助10
4秒前
pups发布了新的文献求助10
4秒前
4秒前
5秒前
Shirly完成签到,获得积分10
5秒前
123456hhh完成签到,获得积分10
5秒前
Sjingjia完成签到,获得积分10
5秒前
嘿帕王教官完成签到,获得积分10
5秒前
coco完成签到,获得积分10
5秒前
Hong完成签到,获得积分10
6秒前
我爱学习完成签到 ,获得积分10
6秒前
zzz完成签到,获得积分10
7秒前
7秒前
田様应助亚秋采纳,获得10
7秒前
动听的笑南完成签到,获得积分10
7秒前
儒雅的冷松完成签到,获得积分10
7秒前
Liooo发布了新的文献求助30
7秒前
8秒前
8秒前
8秒前
9秒前
专一的善愁完成签到 ,获得积分10
9秒前
乌龙茶干完成签到,获得积分10
9秒前
视野胤发布了新的文献求助10
9秒前
糊涂的雁易完成签到,获得积分0
10秒前
QXH完成签到,获得积分10
10秒前
王毅完成签到 ,获得积分10
10秒前
香蕉觅云应助ZeSir采纳,获得10
10秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3968964
求助须知:如何正确求助?哪些是违规求助? 3513877
关于积分的说明 11170569
捐赠科研通 3249201
什么是DOI,文献DOI怎么找? 1794692
邀请新用户注册赠送积分活动 875297
科研通“疑难数据库(出版商)”最低求助积分说明 804755