English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Human mobility demonstrates a high degree of regularity, which facilitatesthe discovery of lifestyle profiles. Existing research has yet to fully utilizethe regularities embedded in high-order features extracted from human mobilityrecords in such profiling. This study proposes a progressive feature extractionstrategy that mines high-order mobility features from users' moving trajectoryrecords from the spatial, temporal, and semantic dimensions. Specific featuresare extracted such as travel motifs, rhythms decomposed by discrete Fouriertransform (DFT) of mobility time series, and vectorized place semantics byword2vec, respectively to the three dimensions, and they are further clusteredto reveal the users' lifestyle characteristics. An experiment using atrajectory dataset of over 500k users in Shenzhen, China yields seven userclusters with different lifestyle profiles that can be well interpreted bycommon sense. The results suggest the possibility of fine-grained userprofiling through cross-order trajectory feature engineering and clustering.

A framework for mining lifestyle profiles through multi-dimensional and high-order mobility feature clustering