深圳大学 Li Mingxiao--Home--Scientific Research

李明晓

Personal information

Highest Degree：Doctoral degree
Professional Title：Assistant Professor

Scientific Research

Home - Scientific Research

Vertical Project

[1] 国家自然科学基金面上项目,(4257011613),基于知识迁移的城市人口时空分布预测方法研究,In research.主持
[2] 广东省哲学社会科学规划项目,(GD25YSH14),人群流动视角下的粤港澳大湾区空间协同发展态势研判与促进策略研究,In research.主持
[3] 国家自然科学基金青年项目,(42101463),基于稀疏手机定位数据的城市人群分布近实时预测,Completed.主持.
[4] 中国博士后科学基金,(2021M692164),基于海量稀疏手机数据的城市高时空分辨率人口分布预测,Completed.主持.
[5] 资源与环境信息国家重点实验室开放基金,城市人群移动动态观测与时空模拟,Completed.主持.
[6] 广东省基础与应用基础研究基金区域联合基金,(2020A1515111166),基于多源稀疏轨迹大数据的城市精细时空粒度人口分布预测,Completed.主持.

Journal Paper

[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,.

Patent Authorization

[1] Invent，基于物理空间与社交网络空间交互的人口分布预测方法，ZL 2020 1 129580.0
[2] Invent，一种人群分布预测方法、装置、设备及存储介质，ZL 2021 1 0975975.6