Database Reference
In-Depth Information
5
Mobility and Uncertainty
Claudio Silvestri and Alejandro A. Vaisman
5.1 Introduction
Mobility data are inherently uncertain due to several contributing factors related
to different phases of their life cycle, from acquisition to interpretation. When
data are processed, uncertainty propagates to intermediate and final results.
Thus, it is important to be aware of uncertainty in trajectory data and explicitly
account for it in their modeling and managing. For example, consider a simple
scenario where people move around a city and disclose their positions twice an
hour; to avoid stalking, the disclosed position is randomly selected from inside a
circle with a radius of one kilometer, which contains the position of the user. Not
being aware of uncertainty could lead to inconsistent conclusions. For instance,
we could erroneously assume that a group of people have met or that someone
has visited a privacy-sensitive place. On the contrary, taking uncertainty into
account, we can avoid such erroneous conclusions; for example, if someone
was farther than one kilometer from the place of an accident, we can certainly
assume that this person was not involved in that accident.
We next introduce a well-known taxonomy of uncertainty (see Bibliographic
Notes section), aimed at clearly defining terms that are often given multiple
meanings in the literature.
A Taxonomy of Uncertainty
The taxonomy we present here considers, at the highest abstraction level, that
uncertainty in mobility and geographic information is caused by the complexity
of the system conformed by three kinds of entities: human being, earth (i.e.,
geographic/moving), and computing machinery. In simple terms, uncertainty
reflects the variety of the geographic and movement reality, the computational
capability of machines, and the limits of human cognition.
A first distinction classifies uncertainty as: (1) uncertainty of the
enti-
ties within
each one of the three domains above, and (2) uncertainty of the
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