  Recent studies on human mobility show that human movements are not random and
tend to be clustered. In this connection, the movements of Twitter users
captured by geo-located tweets were found to follow similar patterns, where a
few geographic locations dominate the tweeting activity of individual users.
However, little is known about the semantics (landuse types) and temporal
tweeting behavior at those frequently-visited locations. Furthermore, it is
generally assumed that the top two visited locations for most of the users are
home and work locales (Hypothesis A) and people tend to tweet at their top
locations during a particular time of the day (Hypothesis B). In this paper, we
tested these two frequently cited hypotheses by examining the tweeting patterns
of more than 164,000 unique Twitter users whom were residents of the city of
Chicago during 2014. We extracted landuse attributes for each geo-located tweet
from the detailed inventory of the Chicago Metropolitan Agency for Planning.
Top-visited locations were identified by clustering semantic enriched tweets
using a DBSCAN algorithm. Our results showed that although the top two
locations are likely to be residential and occupational/educational, a portion
of the users deviated from this case, suggesting that the first hypothesis
oversimplify real-world situations. However, our observations indicated that
people tweet at specific times and these temporal signatures are dependent on
landuse types. We further discuss the implication of confounding variables,
such as clustering algorithm parameters and relative accuracy of tweet
coordinates, which are critical factors in any experimental design involving
Twitter data.
