  People's daily activities in the urban environment are complex and vary by
individuals. Existing studies using mobile phone data revealed distinct and
recurrent transitional activity patterns, known as mobility motifs, in people's
daily lives. However, the limitation in using only a few inferred activity
types hinders our ability to examine general patterns in detail. We proposed a
mobility network approach with geographic context-aware Twitter data to
investigate granular daily activity patterns in the urban environment. We first
utilized publicly accessible geo-located tweets to track the movements of
individuals in two major U.S. cities: Chicago and Greater Boston, where each
recorded location is associated with its closest land use parcel to enrich its
geographic context. A direct mobility network represents the daily location
history of the selected active users, where the nodes are physical places with
semantically labeled activity types, and the edges represent the transitions.
Analyzing the isomorphic structure of the mobility networks uncovered 16 types
of location-based motifs, which describe over 83% of the networks in both
cities and are comparable to those from previous studies. With detailed and
semantically labeled transitions between every two activities, we further
dissected the general location-based motifs into activity-based motifs, where
16 common activity-based motifs describe more than 57% transitional behaviors
in the daily activities in the two cities. The integration of geographic
context from the synthesis of geo-located Twitter data with land use parcels
enables us to reveal unique activity motifs that form the fundamental elements
embedded in complex urban activities.
