  Characterizing human mobility patterns is essential for understanding human
behaviors and the interactions with socioeconomic and natural environment. With
the continuing advancement of location and Web 2.0 technologies, location-based
social media (LBSM) have been gaining widespread popularity in the past few
years. With an access to locations of users, profiles and the contents of the
social media posts, the LBSM data provided a novel modality of data source for
human mobility study. By exploiting the explicit location footprints and mining
the latent demographic information implied in the LBSM data, the purpose of
this paper is to investigate the spatiotemporal characteristics of human
mobility with a particular focus on the impact of demography. We first collect
geo-tagged Twitter feeds posted in the conterminous United States area, and
organize the collection of feeds using the concept of space-time trajectory
corresponding to each Twitter user. Commonly human mobility measures, including
detected home and activity centers, are derived for each user trajectory. We
then select a subset of Twitter users that have detected home locations in the
city of Chicago as a case study, and apply name analysis to the names provided
in user profiles to learn the implicit demographic information of Twitter
users, including race/ethnicity, gender and age. Finally we explore the
spatiotemporal distribution and mobility characteristics of Chicago Twitter
users, and investigate the demographic impact by comparing the differences
across three demographic dimensions (race/ethnicity, gender and age). We found
that, although the human mobility measures of different demographic groups
generally follow the generic laws (e.g., power law distribution), the
demographic information, particular the race/ethnicity group, significantly
affects the urban human mobility patterns.
