What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of investigation include Mobile phone, Data mining, Big data, Land use and Distance decay.
His biological study spans a wide range of topics, including Cartography, Perspective and Travel behavior.
His work in Data mining addresses issues such as Remote sensing, which are connected to fields such as Leverage, Analytics, Representativeness heuristic and Geospatial analysis.
He combines subjects such as Transportation planning, Travel survey, Small data and Data science with his study of Big data.
His study in the fields of Urban land under the domain of Land use overlaps with other disciplines such as Location aware.
His Urban land study combines topics from a wide range of disciplines, such as Environmental resource management, Recreation, Global Positioning System and Urban form.
His most cited work include:
- Social Sensing: A New Approach to Understanding Our Socioeconomic Environments (342 citations)
- Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data (274 citations)
- Urban land uses and traffic 'source-sink areas': Evidence from GPS-enabled taxi data in Shanghai (240 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Data mining, Artificial intelligence, Big data, Beijing and Mobile phone.
His research in Data mining intersects with topics in Spatial analysis, Geospatial analysis, Cluster analysis, Field and Geographic information system.
His work deals with themes such as Computer vision and Pattern recognition, which intersect with Artificial intelligence.
He has included themes like Point of interest, Perspective and Data science in his Big data study.
The various areas that Yu Liu examines in his Data science study include Distance decay and Social media.
His Mobile phone research is multidisciplinary, incorporating elements of Cartography and Built environment.
He most often published in these fields:
- Data mining (17.82%)
- Artificial intelligence (15.52%)
- Big data (12.07%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (15.52%)
- Social media (7.47%)
- Beijing (11.49%)
In recent papers he was focusing on the following fields of study:
Yu Liu mostly deals with Artificial intelligence, Social media, Beijing, Data mining and Mobile phone.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Big data, Anomaly and Pattern recognition.
As a part of the same scientific family, he mostly works in the field of Social media, focusing on Location prediction and, on occasion, Control and Travel behavior.
His Travel behavior study integrates concerns from other disciplines, such as Medical emergency, Land use and Traffic congestion.
His studies in Data mining integrate themes in fields like Kalman filter, Graph, Urban road and Spatial dependence.
Yu Liu focuses mostly in the field of Mobile phone, narrowing it down to topics relating to Point of interest and, in certain cases, Self-organizing map, Measure, Data science, Spatial ecology and Recreation.
Between 2019 and 2021, his most popular works were:
- T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction (131 citations)
- Spatial Lifecourse Epidemiology Reporting Standards (ISLE-ReSt) statement. (26 citations)
- Spatial interpolation using conditional generative adversarial neural networks (17 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Yu Liu mainly investigates Artificial neural network, Graph, Data mining, Urban planning and Econometrics.
His Artificial neural network research is within the category of Artificial intelligence.
His studies deal with areas such as Sustainable development, Industrial data processing and Big data as well as Artificial intelligence.
The study incorporates disciplines such as Traffic system, Urban road and Spatial dependence in addition to Data mining.
His research integrates issues of Transportation planning, Smart card, Trip distribution and Land use in his study of Urban planning.
His Econometrics research includes elements of Range, Scale and Scaling.
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