Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore

320 indexed citations

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This paper, published in 2016, received 320 indexed citations. Written by Shan Jiang, Joseph Ferreira and Marta C. González covering the research area of Transportation. It is primarily cited by scholars working on Transportation (293 citations), Global and Planetary Change (76 citations) and Building and Construction (52 citations). Published in IEEE Transactions on Big Data.

Countries where authors are citing Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore

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This map shows the geographic impact of Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore more than expected).

Fields of papers citing Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/10.1109/tbdata.2016.2631141.

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