机译:Fine-grained crowd distribution forecasting with multi-order spatial interactions using mobile phone data
Guangdong Key Laboratory of Urban Informatics, Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services, and Research Institute of Smart Cities, School of Architeture & Urban Planning, Shenzhen University, Shenzhen 518060, China, Geospati;
Geospatial Data Science Lab, Department of Geography, University of Wisconsin, Madison, WI 53706, USA;
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100201, China, College of Surveying and Geo-Informatics, Shandong Jianzhu UniversGuangdong Key Laboratory of Urban Informatics, Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services, and Research Institute of Smart Cities, School of Architeture & Urban Planning, Shenzhen University, Shenzhen 518060, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100201, China, University of Chinese Academy of Sciences, Beijing 100049, China,;
Crowd distribution forecasting; Multi-order spatial interaction; Embedding learning; Trajectory enhancement; Human mobility;