D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Engineering and Technology D-index 31 Citations 4,881 114 World Ranking 5363 National Ranking 613

Overview

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Social Sensing: A New Approach to Understanding Our Socioeconomic Environments

Yu Liu;Xi Liu;Song Gao;Li Gong.
Annals of The Association of American Geographers (2015)

453 Citations

The promises of big data and small data for travel behavior (aka human mobility) analysis

Cynthia Chen;Jingtao Ma;Yusak Susilo;Yu Liu.
Transportation Research Part C-emerging Technologies (2016)

358 Citations

Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data

Yu Liu;Zhengwei Sui;Chaogui Kang;Yong Gao.
PLOS ONE (2014)

355 Citations

Urban land uses and traffic 'source-sink areas': Evidence from GPS-enabled taxi data in Shanghai

Yu Liu;Yu Liu;Fahui Wang;Yu Xiao;Song Gao.
Landscape and Urban Planning (2012)

353 Citations

Understanding intra-urban trip patterns from taxi trajectory data

Yu Liu;Chaogui Kang;Song Gao;Yu Xiao.
Journal of Geographical Systems (2012)

308 Citations

Revealing travel patterns and city structure with taxi trip data

Xi Liu;Li Gong;Yongxi Gong;Yu Liu.
Journal of Transport Geography (2015)

269 Citations

Correlating mobile phone usage and travel behavior – A case study of Harbin, China

Yihong Yuan;Martin Raubal;Martin Raubal;Yu Liu.
Computers, Environment and Urban Systems (2012)

251 Citations

Intra-urban human mobility patterns: An urban morphology perspective

Chaogui Kang;Xiujun Ma;Daoqin Tong;Yu Liu.
Physica A-statistical Mechanics and Its Applications (2012)

212 Citations

Intra-urban human mobility and activity transition: evidence from social media check-in data.

Lun Wu;Ye Zhi;Zhengwei Sui;Yu Liu.
PLOS ONE (2014)

201 Citations

Discovering Spatial Interaction Communities from Mobile Phone Data

Song Gao;Yu Liu;Yaoli Wang;Xiujun Ma.
Transactions in Gis (2013)

199 Citations

Best Scientists Citing Yu Liu

Qingquan Li

Qingquan Li

Shenzhen University

Publications: 51

Carlo Ratti

Carlo Ratti

MIT

Publications: 43

Shih-Lung Shaw

Shih-Lung Shaw

University of Tennessee at Knoxville

Publications: 27

Krzysztof Janowicz

Krzysztof Janowicz

University of California, Santa Barbara

Publications: 15

Qinghua Guo

Qinghua Guo

Chinese Academy of Sciences

Publications: 14

Xia Li

Xia Li

East China Normal University

Publications: 12

Wenzhong Shi

Wenzhong Shi

Hong Kong Polytechnic University

Publications: 11

Satish V. Ukkusuri

Satish V. Ukkusuri

Purdue University West Lafayette

Publications: 10

Martin Raubal

Martin Raubal

ETH Zurich

Publications: 10

Stephan Winter

Stephan Winter

University of Melbourne

Publications: 9

José J. Ramasco

José J. Ramasco

Spanish National Research Council

Publications: 9

Sune Lehmann

Sune Lehmann

Technical University of Denmark

Publications: 9

Yinhai Wang

Yinhai Wang

University of Washington

Publications: 9

Chau Yuen

Chau Yuen

Singapore University of Technology and Design

Publications: 8

Alexander Zipf

Alexander Zipf

Heidelberg University

Publications: 8

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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