A graph spatial-temporal model for predicting population density of key areas.

Xu, Zhihao; Li, Jianbo; Lv, Zhiqiang; Wang, Yue; Fu, Liping; Wang, Xinghao

Xu, Zhihao; Li, Jianbo; Lv, Zhiqiang; Wang, Yue; Fu, Liping; Wang, Xinghao.

Afiliación

Xu Z; School of Computer Science and Technology, Qingdao University, Qingdao 266000, Shandong, China.
Li J; School of Computer Science and Technology, Qingdao University, Qingdao 266000, Shandong, China.
Lv Z; Institute of Ubiquitous Networks and Urban Computing, Qingdao University, Qingdao 266001, Shandong, China.
Wang Y; School of Computer Science and Technology, Qingdao University, Qingdao 266000, Shandong, China.
Fu L; Institute of Ubiquitous Networks and Urban Computing, Qingdao University, Qingdao 266001, Shandong, China.
Wang X; Institute of Ubiquitous Networks and Urban Computing, Qingdao University, Qingdao 266001, Shandong, China.

Comput Electr Eng ; 93: 107235, 2021 Jul.

Article en En | MEDLINE | ID: mdl-34642506

Palabras clave

Feature component; Key areas; Population density; WE-STGCN

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Comput Electr Eng Año: 2021 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos

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