Commuter Mobility Patterns in Social Media: Correlating Twitter and LODES Data

Andreas Petutschnig; Jochen Albrecht; Bernd Resch; Laxmi Ramasubramanian; Aleisha Wright

doi:10.3390/ijgi11010015

Commuter Mobility Patterns in Social Media: Correlating Twitter and LODES Data

Andreas Petutschnig^*, Jochen Albrecht, Bernd Resch, Laxmi Ramasubramanian, Aleisha Wright

^*Corresponding author for this work

Geoinformatics

Research output: Contribution to journal › Article › peer-review

Original language	English
Article number	15
Journal	ISPRS International Journal of Geo-Information
Volume	11
Issue number	1
DOIs	https://doi.org/10.3390/ijgi11010015
Publication status	Published - Jan 2022

Bibliographical note

Funding Information:
Funding: This research was funded by the Austrian Science Fund (FWF) through the project “The Scales and Structures of Intra-Urban Spaces” (reference number P 29135-N29). This research was funded by the Austrian Science Fund (FWF) through the project “Geographic Information Science. Integrating interdisciplinary concepts and methods” (reference number W 1237). This research was funded by the Mineta Transportation Institute (MTI) through the project “Deriving Commuting Patterns From Tweets: Investigating the Benefits and Limits of Using Publicly Available Volunteered Geographic Information” (reference number 2037).

Funding Information:
This research was funded by the Austrian Science Fund (FWF) through the project ?The Scales and Structures of Intra-Urban Spaces? (reference number P 29135-N29). This research was funded by the Austrian Science Fund (FWF) through the project ?Geographic Information Science. Integrating interdisciplinary concepts and methods? (reference number W 1237). This research was funded by the Mineta Transportation Institute (MTI) through the project ?Deriving Commuting Patterns From Tweets: Investigating the Benefits and Limits of Using Publicly Available Volunteered Geographic Information? (reference number 2037). We would like to thank Harvard University?s Center for Geographic Analysis for their support in providing us with the Twitter data for our study.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

Collective movement
Commuter mobility
Twitter mobility
Urban planning

Fields of Science and Technology Classification 2012

211 Other Technical Sciences
507 Human Geography, Regional Geography, Regional Planning

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10.3390/ijgi11010015

Cite this

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title = "Commuter Mobility Patterns in Social Media: Correlating Twitter and LODES Data",

keywords = "Collective movement, Commuter mobility, Twitter mobility, Urban planning",

author = "Andreas Petutschnig and Jochen Albrecht and Bernd Resch and Laxmi Ramasubramanian and Aleisha Wright",

note = "Funding Information: Funding: This research was funded by the Austrian Science Fund (FWF) through the project “The Scales and Structures of Intra-Urban Spaces” (reference number P 29135-N29). This research was funded by the Austrian Science Fund (FWF) through the project “Geographic Information Science. Integrating interdisciplinary concepts and methods” (reference number W 1237). This research was funded by the Mineta Transportation Institute (MTI) through the project “Deriving Commuting Patterns From Tweets: Investigating the Benefits and Limits of Using Publicly Available Volunteered Geographic Information” (reference number 2037). Funding Information: This research was funded by the Austrian Science Fund (FWF) through the project ?The Scales and Structures of Intra-Urban Spaces? (reference number P 29135-N29). This research was funded by the Austrian Science Fund (FWF) through the project ?Geographic Information Science. Integrating interdisciplinary concepts and methods? (reference number W 1237). This research was funded by the Mineta Transportation Institute (MTI) through the project ?Deriving Commuting Patterns From Tweets: Investigating the Benefits and Limits of Using Publicly Available Volunteered Geographic Information? (reference number 2037). We would like to thank Harvard University?s Center for Geographic Analysis for their support in providing us with the Twitter data for our study. Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.",

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AU - Petutschnig, Andreas

AU - Albrecht, Jochen

AU - Resch, Bernd

AU - Ramasubramanian, Laxmi

AU - Wright, Aleisha

N1 - Funding Information: Funding: This research was funded by the Austrian Science Fund (FWF) through the project “The Scales and Structures of Intra-Urban Spaces” (reference number P 29135-N29). This research was funded by the Austrian Science Fund (FWF) through the project “Geographic Information Science. Integrating interdisciplinary concepts and methods” (reference number W 1237). This research was funded by the Mineta Transportation Institute (MTI) through the project “Deriving Commuting Patterns From Tweets: Investigating the Benefits and Limits of Using Publicly Available Volunteered Geographic Information” (reference number 2037). Funding Information: This research was funded by the Austrian Science Fund (FWF) through the project ?The Scales and Structures of Intra-Urban Spaces? (reference number P 29135-N29). This research was funded by the Austrian Science Fund (FWF) through the project ?Geographic Information Science. Integrating interdisciplinary concepts and methods? (reference number W 1237). This research was funded by the Mineta Transportation Institute (MTI) through the project ?Deriving Commuting Patterns From Tweets: Investigating the Benefits and Limits of Using Publicly Available Volunteered Geographic Information? (reference number 2037). We would like to thank Harvard University?s Center for Geographic Analysis for their support in providing us with the Twitter data for our study. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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KW - Urban planning

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Commuter Mobility Patterns in Social Media: Correlating Twitter and LODES Data

Bibliographical note

Keywords

Fields of Science and Technology Classification 2012

Access to Document

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