深圳大学李明晓--中文主页--期刊论文

李明晓

个人信息

Personal information

学位：博士
职称：助理教授

期刊论文

中文主页 - 科研工作 - 期刊论文

[1] Zhu Jiasong, Chen Jingbiao, Li Mingxiao*, et al. How spatial scales enhance prediction: an interpretable multi-scale framework for bike-sharing demand prediction,Geo-spatial Information Science, 2025: 1-15.,.
[2] Wang Nan, Li Mingxiao*, Cheng Weiming, et al., Susceptibility modeling of hydro-morphological processes considered river topology,Geo-spatial Information Science, 2024: 1-20,.
[3] Fang Bichen, Li Mingxiao*, Huang Zhengdong, et al. Revealing multi-scale spatial synergy of mega-city region from a human mobility perspective,Geo-spatial Information Science, 2024: 1-16,.
[4] Tu Wei, Gao Wei, Li Mingxiao*, et al. Spatial cooperative simulation of land use-population-economy in the Greater Bay Area, China,International Journal of Geographical Information Science, 2024, 38(2): 381-406,.
[5] Li Mingxiao, Tu Wei, Tong Huan, et al. Quantifying the nighttime economy–housing separation from a human activity standpoint: A case study in Shenzhen, China,Cities, 2024, 148: 104894,.
[6] Li Mingxiao, Gao Song, Qiu Peiyuan, et al. Fine-grained crowd distribution forecasting with multi-order spatial interactions using mobile phone data,Transportation research part C: Emerging technologies, 2022, 144: 103908,.
[7] Li Mingxiao, Tu Wei, Lu Feng. Sensing the Nighttime Economy–Housing Imbalance from a Mobile Phone Data Perspective: A Case Study in Shanghai,Remote Sensing, 2022, 14(12):2738,.
[8] Li Mingxiao, Gao Song, Lu Feng, et al. Prediction of human activity intensity using the interactions in physical and social spaces through graph convolutional networks,International Journal of Geographical Information Science, 2021, 35:12, 2489-2516,.
[9] Li Mingxiao, Gao Song, Lu Feng, et al. Reconstruction of human movement trajectories from large-scale low-frequency mobile phone data,Computers, Environment and Urban Systems, 2019, 77: 101346,.
[10] Li Mingxiao, Lu Feng, Zhang Hengcai, et al. Predicting future locations of moving objects with deep fuzzy-LSTM networks,Transportmetrica A: Transport Science, 2020, 16(1): 119-136,.

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