Long-term validation of inner-urban mobility metrics derived from Twitter/X
Urban mobility analysis using Twitter as a proxy has gained significant attention in various application fields; however, long-term validation studies are scarce. This paper addresses this gap by assessing the reliability of Twitter data for modeling inner-urban mobility dynamics over a 27-month per...
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| Hauptverfasser: | , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
October 14, 2024
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| In: |
Environment and planning. B, Urban analytics and city science
Year: 2024, Pages: 1-25 |
| ISSN: | 2399-8091 |
| DOI: | 10.1177/23998083241278275 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1177/23998083241278275 Verlag, kostenfrei, Volltext: https://journals.sagepub.com/doi/10.1177/23998083241278275 
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| Verfasserangaben: | Steffen Knoblauch, Simon Groß, Sven Lautenbach, Antonio Augusto de Aragão Rocha, Marta C González, Bernd Resch, Dorian Arifi, Thomas Jänisch, Ivonne Morales, Alexander Zipf |
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| 245 | 1 | 0 | |a Long-term validation of inner-urban mobility metrics derived from Twitter/X |c Steffen Knoblauch, Simon Groß, Sven Lautenbach, Antonio Augusto de Aragão Rocha, Marta C González, Bernd Resch, Dorian Arifi, Thomas Jänisch, Ivonne Morales, Alexander Zipf |
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| 520 | |a Urban mobility analysis using Twitter as a proxy has gained significant attention in various application fields; however, long-term validation studies are scarce. This paper addresses this gap by assessing the reliability of Twitter data for modeling inner-urban mobility dynamics over a 27-month period in the metropolitan area of Rio de Janeiro, Brazil. The evaluation involves the validation of Twitter-derived mobility estimates at both temporal and spatial scales, employing over 1.6 × 1011 mobile phone records of around three million users during the non-stationary mobility period from April 2020 to June 2022, which coincided with the COVID-19 pandemic. The results highlight the need for caution when using Twitter for short-term modeling of urban mobility flows. Short-term inference can be influenced by Twitter policy changes and the availability of publicly accessible tweets. On the other hand, this long-term study demonstrates that employing multiple mobility metrics simultaneously, analyzing dynamic and static mobility changes concurrently, and employing robust preprocessing techniques such as rolling window downsampling can enhance the inference capabilities of Twitter data. These novel insights gained from a long-term perspective are vital, as Twitter - rebranded to X in 2023 - is extensively used by researchers worldwide to infer human movement patterns. Since conclusions drawn from studies using Twitter could be used to inform public policy, emergency response, and urban planning, evaluating the reliability of this data is of utmost importance. | ||
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