About This Project
This paper examines activity spaces of individuals in an urban area based on a long-duration geo-tagged Twitter dataset obtained from the social media data provider- Gnip.com. The paper demonstrates the possibility and merits to rely on social media as an innovative data source to understand individual activity travel behaviors in time and space.
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Join The DiscussionWhat is the context of this research?
Activity space has been considered as an important concept in understanding urban mobility and individual activity-travel behaviors in many disciplines. The advent of big data has brought new opportunities to perform empirical studies on individual spatial activity patterns which have been historically challenging due to the lack of data (Järv et al. 2014). Recently, emerging data sources have been generating growing attentions in activity-travel research, However, the use of geo-tagged social media data in related activity-travel research is still initial. To the best of my knowledge, only few studies have recently employed social media data to explore human activity-travel patterns, nevertheless, none of which has actually examined the activity space concept.
What is the significance of this project?
This project aims at taking advantage of the emerging social media, to contribute to researches on activity travel behaviors. The study brings a possibility for empirical studies to keep pace with theoretical development. The main contribution lies in two aspects. First, the new activity space model addresses some issues of the existing activity models that will improve our understandings of the activity space concept. More importantly, to our best knowledge, this is the first time that activity space is related to the big data domain, in which more collaborative works can be conducted in the future. Our findings would be valuable to studies in various disciplines that are to reveal people's travel and activity behaviors for different purposes.
What are the goals of the project?
In this research, I will focus on a study of using social media data, an important type of big data, to help construct a new model to assess human activity space and examine activity-travel patterns. Following this research, I will use the social media data to address three research questions:
1. How to use geo-tagged social media data to generate activity space to verify my theoretical model?
2. How would activity spaces resulted from social media data vary among individuals who are in different social-economic statuses?
3. What similarities could we observe when correlating the activity spaces with other existing facts such as traffic data?
Budget
A 6-month long historical twitter data covering the research area, New York City, will be purchased from Gnip.com. Considering the fact that the project requires to capture every moment of people's temporal-spatial activities, any loss of the detail due to the failure or interruption of data collection would result in unexpected impacts on the results. In this sense, it is crucial to make the data quality as high as possible to facilitate better quality of research. I've contacted Twitter and they referred me to their official partner that distributes commercial-quality data, Gnip.com. The company is well known for its reliable quality data supply of plenty of social media platforms including Twitter. Without the help of the data, the model I proposed will be purely theoretical that cannot to be tested.
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Meet the Team
Ran Li
Ran Li received his Ph.D. degree in geography from the University of Arizona in 2016. His research interests include location modeling, activity-travel, GIS and urban/transportation planning.
Lab Notes
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Additional Information
The nature of this project is interdisciplinary. It engages conversation between geographers, transportation engineers, urban planners and civil engineers. The project mainly investigates human activity-travel behaviors that have been considered as a common interest in multiple disciplines: geography, urban planning, civil engineering and so forth (Wang et al. 2013; Schönfelder & Axhausen, 2010). In addition, new domains of social media or even big data will be introduced to the study, which will bring new thoughts and potentials for future collaborations among the related areas. By analyzing and assessing people’s movements in space and time using social media data, activity-travel patterns of people in time and space can be well examined and understood, based on which more specific research questions will be future addressed. Furthermore, the research findings of this project will better assist decision making processes such as urban land use and structure planning, transportation planning, environmental policy making, etc.
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