  Life pattern clustering is essential for abstracting the groups'
characteristics of daily mobility patterns and activity regularity. Based on
millions of GPS records, this paper proposed a framework on the life pattern
clustering which can efficiently identify the groups have similar life pattern.
The proposed method can retain original features of individual life pattern
data without aggregation. Metagraph-based data structure is proposed for
presenting the diverse life pattern. Spatial-temporal similarity includes
significant places semantics, time sequential properties and frequency are
integrated into this data structure, which captures the uncertainty of an
individual and the diversities between individuals.
Non-negative-factorization-based method was utilized for reducing the
dimension. The results show that our proposed method can effectively identify
the groups have similar life pattern and takes advantages in computation
efficiency and robustness comparing with the traditional method. We revealed
the representative life pattern groups and analyzed the group characteristics
of human life patterns during different periods and different regions. We
believe our work will help in future infrastructure planning, services
improvement and policies making related to urban and transportation, thus
promoting a humanized and sustainable city.
