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Understanding human mobility is of great importance to various applications, such as urban planning, traffic scheduling, and location prediction. While there has been fruitful research on modeling human mobility using tracking data (e.g., GPS traces), the recent growth of geo-tagged social media (GeoSM) brings new opportunities to this task because of its(More)
With the ever-increasing urbanization process, systematically modeling people's activities in the urban space is being recognized as a crucial socioeconomic task. This task was nearly impossible years ago due to the lack of reliable data sources, yet the emergence of geo-tagged social media (GTSM) data sheds new light on it. Recently, there have been(More)
To judge how much a pair of words (or texts) are semantically related is a cognitive process. However, previous algorithms for computing semantic relatedness are largely based on co-occurrences within textual windows, and do not actively leverage cognitive human perceptions of relatedness. To bridge this perceptional gap, we propose to utilize free(More)
Spatiotemporalactivity modeling is an important task for applications like tour recommendation and place search. The recently developed geographical topic models have demonstrated compelling results in using geo-tagged social media (GTSM) for spatiotemporal activity modeling. Nevertheless, they all operate in batch and cannot dynamically accommodate the(More)
Understanding human activities is gradually recognized as a cornerstone task that is critical to a large number of practical applications, ranging from urban planning to location recommendation. While traditional approaches of analyzing people’s city-wide activities heavily rely on paid surveys and human investigations , the explosively growing geo-tagged(More)
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