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Detecting Origin-Destination Mobility Flows From Geotagged Tweets in Greater Los Angeles Area

Trajectory-based mobility research plays an increasing role in GIScience and related domains. Typically, the research results heavily depend on the quality and resolution of data that can be collected, e.g., via surveys. In travel behaviour and transportation studies, time and cost constrains are the limiting factors for the collection of large-scale individual travel behaviour data using traditional trip-diary surveys (McNally 2000). With the fast development of information and communication technologies (ICT), new data sources including GPS logs, smart card records, mobile phone data, and location-based social media have become potential alternatives or complementary approaches to study large-scale human mobility patterns and travel behaviour (Calabrese et al. 2011, Liu et al. 2012a, Yue et al. 2014). Human movement origin-destination (OD) information is of major importance in urban transportation modelling and infrastructure planning in order to optimize the use of street networks. The increasing use of social media like Twitter offers unprecedented opportunities to study individual activities, to know where users are at which time, and what they are talking about. In this work we study the reliability of detecting regional OD trips from individual geotagged tweets in comparison with survey data in a quantitative manner, and explore the spatiotemporal flow patterns extracted from social media. We will investigate the research question of whether OD trips mined from social media yield comparable results to expensive and labour intensive large-scale studies. To do so, we will derive OD trips from geotagged tweets, aggregate them, and compare the results by correlating them to the American Community Survey data.