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People's daily activities in the urban environment are complex and vary byindividuals. Existing studies using mobile phone data revealed distinct andrecurrent transitional activity patterns, known as mobility motifs, in people'sdaily lives. However, the limitation in using only a few inferred activitytypes hinders our ability to examine general patterns in detail. We proposed amobility network approach with geographic context-aware Twitter data toinvestigate granular daily activity patterns in the urban environment. We firstutilized publicly accessible geo-located tweets to track the movements ofindividuals in two major U.S. cities: Chicago and Greater Boston, where eachrecorded location is associated with its closest land use parcel to enrich itsgeographic context. A direct mobility network represents the daily locationhistory of the selected active users, where the nodes are physical places withsemantically labeled activity types, and the edges represent the transitions.Analyzing the isomorphic structure of the mobility networks uncovered 16 typesof location-based motifs, which describe over 83% of the networks in bothcities and are comparable to those from previous studies. With detailed andsemantically labeled transitions between every two activities, we furtherdissected the general location-based motifs into activity-based motifs, where16 common activity-based motifs describe more than 57% transitional behaviorsin the daily activities in the two cities. The integration of geographiccontext from the synthesis of geo-located Twitter data with land use parcelsenables us to reveal unique activity motifs that form the fundamental elementsembedded in complex urban activities.

Characterizing People's Daily Activity Patterns in the Urban Environment: A Mobility Network Approach with Geographic Context-Aware Twitter Data