  Human mobility demonstrates a high degree of regularity, which facilitates
the discovery of lifestyle profiles. Existing research has yet to fully utilize
the regularities embedded in high-order features extracted from human mobility
records in such profiling. This study proposes a progressive feature extraction
strategy that mines high-order mobility features from users' moving trajectory
records from the spatial, temporal, and semantic dimensions. Specific features
are extracted such as travel motifs, rhythms decomposed by discrete Fourier
transform (DFT) of mobility time series, and vectorized place semantics by
word2vec, respectively to the three dimensions, and they are further clustered
to reveal the users' lifestyle characteristics. An experiment using a
trajectory dataset of over 500k users in Shenzhen, China yields seven user
clusters with different lifestyle profiles that can be well interpreted by
common sense. The results suggest the possibility of fine-grained user
profiling through cross-order trajectory feature engineering and clustering.
