  The dynamics of human mobility characterizes the trajectories humans follow
during their daily activities and is the foundation of processes from epidemic
spreading to traffic prediction and information recommendation. In this paper,
we investigate a massive data set of human activity including both online
behavior of browsing websites and offline one of visiting towers based mobile
terminations. The non-Markovian character observed from both online and offline
cases is suggested by the scaling law in the distribution of dwelling time at
individual and collective levels, respectively. Furthermore, we argue that the
lower entropy and higher predictability in human mobility for both online and
offline cases may origin from this non-Markovian character. However, the
distributions of individual entropy and predictability show the different
degrees of non-Markovian character from online to offline cases. To accounting
for non-Markovian character in human mobility, we introduce a protype model
with three basic ingredients, \emph{preferential return, inertial effect, and
exploration} to reproduce the dynamic process of online and offline human
mobility. In comparison with standard and biased random walk models with
assumption of Markov process, the proposed model is able to obtain characters
much closer to these empirical observations.
