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For example, in VANETs, the vehicles' mobility patterns are highly relying on the road structures.
First, city-wide mobility patterns are modeled based on GPS trajectories.
Wang et al. [131] showed that indeed, nodes that are not connected in the network, but topologically close, and who show similar mobility patterns are likely to create a link.
From a dynamical point of view, different papers [10, 11] studied the mobility of researchers during their academic career, showing that the statistical properties of their mobility patterns are mainly determined by simple features, such as geographical distance, university rankings and cultural similarity.
However, this view ignores two core findings of the economic systems literature: first, that mobility patterns are institutional microsystems that need to be complementary to other institutions in the labour market; and second, that new industries may be characterised by incremental and complex innovation.
Thus, while communities can be defined as equally remote mobility, patterns are locally heterogeneous.
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Another important metric for assessing mobility patterns is the radius of gyration.
The identification of urban mobility patterns is very important for predicting and controlling spatial events.
This, however, cannot be done in conjunction with mobility consideration, since the information on mobility patterns is not directly present.
Clearly, understanding the human mobility patterns is a key challenge for the design of routing protocols based on such paradigms.
The study of hunter gatherer mobility patterns is of vital importance to our understanding of the paleolithic archeological record.
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